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We revisit the foundational Moment Formula proved by Roger Lee fifteen years ago. We show that when the underlying stock price martingale admits finite log-moments E[|log(S)|^q] for some positive q, the arbitrage-free growth in the left wing of the implied volatility smile is less constrained than Lee's bound. The result is rationalised by a market trading discretely monitored variance swaps wherein the payoff is a function of squared log-returns, and requires no assumption for the underlying martingale to admit any negative moment. In this respect, the result can derived from a model-independent setup. As a byproduct, we relax the moment assumptions on the stock price to provide a new proof of the notorious Gatheral-Fukasawa formula expressing variance swaps in terms of the implied volatility.

We present a combined angle-resolved photoemission spectroscopy and low-energy electron diffraction (LEED) study of the prominent transition metal dichalcogenide IrTe$_2$ upon potassium (K) deposition on its surface. Pristine IrTe$_2$ undergoes a series of charge-ordered phase transitions below room temperature that are characterized by the formation of stripes of Ir dimers of different periodicities. Supported by density functional theory calculations, we first show that the K atoms dope the topmost IrTe$_2$ layer with electrons, therefore strongly decreasing the work function and shifting only the electronic surface states towards higher binding energy. We then follow the evolution of its electronic structure as a function of temperature across the charge-ordered phase transitions and observe that their critical temperatures are unchanged for K coverages of $0.13$ and $0.21$~monolayer (ML). Using LEED, we also confirm that the periodicity of the related stripe phases is unaffected by the K doping. We surmise that the charge-ordered phase transitions of IrTe$_2$ are robust against electron surface doping, because of its metallic nature at all temperatures, and due to the importance of structural effects in stabilizing charge order in IrTe$_2$.

We theoretically show that two distinctive spin textures manifest themselves around saddle points of energy bands in a monolayer NbSe$_2$ under external gate potentials. While the density of states at all saddle points diverge logarithmically, ones at the zone boundaries display a windmill-shaped spin texture while the others unidirectional spin orientations. The disparate spin-resolved states are demonstrated to contribute an intrinsic spin Hall conductivity significantly while their characteristics differ from each other.Based on a minimal but essential tight-binding approximation reproducing first-principles computation results, we established distinct effective Rashba Hamiltonians for each saddle point, realizing the unique spin textures depending on their momentum. Energetic positions of the saddle points in a single layer NbSe$_2$ are shown to be well controlled by a gate potential so that it could be a prototypical system to test a competition between various collective phenomena triggered by diverging density of states and their spin textures in low-dimension.

The time of the first occurrence of a threshold crossing event in a stochastic process, known as the first passage time, is of interest in many areas of sciences and engineering. Conventionally, there is an implicit assumption that the notional 'sensor' monitoring the threshold crossing event is always active. In many realistic scenarios, the sensor monitoring the stochastic process works intermittently. Then, the relevant quantity of interest is the $\textit{first detection time}$, which denotes the time when the sensor detects the threshold crossing event for the first time. In this work, a birth-death process monitored by a random intermittent sensor is studied, for which the first detection time distribution is obtained. In general, it is shown that the first detection time is related to, and is obtainable from, the first passage time distribution. Our analytical results display an excellent agreement with simulations. Further, this framework is demonstrated in several applications -- the SIS compartmental and logistic models, and birth-death processes with resetting. Finally, we solve the practically relevant problem of inferring the first passage time distribution from the first detection time.

Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond.

We aim at measuring the influence of the nondeterministic choices of a part of a system on its ability to satisfy a specification. For this purpose, we apply the concept of Shapley values to verification as a means to evaluate how important a part of a system is. The importance of a component is measured by giving its control to an adversary, alone or along with other components, and testing whether the system can still fulfill the specification. We study this idea in the framework of model-checking with various classical types of linear-time specification, and propose several ways to transpose it to branching ones. We also provide tight complexity bounds in almost every case.

The associations between emergent physical phenomena (e.g., superconductivity) and orbital, charge, and spin degrees of freedom of $3d$ electrons are intriguing in transition metal compounds. Here, we successfully manipulate the superconductivity of spinel oxide Li$_{1\pm x}$Ti$_2$O$_{4-\delta}$ (LTO) by ionic liquid gating. A dome-shaped superconducting phase diagram is established, where two insulating phases are disclosed both in heavily electron-doping and hole-doping regions. The superconductor-insulator transition (SIT) in the hole-doping region can be attributed to the loss of Ti valence electrons. In the electron-doping region, LTO exhibits an unexpected SIT instead of a metallic behavior despite an increase in carrier density. Furthermore, a thermal hysteresis is observed in the normal state resistance curve, suggesting a first-order phase transition. We speculate that the SIT and the thermal hysteresis stem from the enhanced $3d$ electron correlations and the formation of orbital ordering by comparing the transport and structural results of LTO with the other spinel oxide superconductor MgTi$_2$O$_4$, as well as analysing the electronic structure by first-principles calculations. Further comprehension of the detailed interplay between superconductivity and orbital ordering would contribute to the revealing of unconventional superconducting pairing mechanism.

This paper explores the options available to the anti-realist to defend a Quinean empirical under-determination thesis using examples of dualities. I first explicate a version of the empirical under-determination thesis that can be brought to bear on theories of contemporary physics. Then I identify a class of examples of dualities that lead to empirical under-determination. But I argue that the resulting under-determination is benign, and is not a threat to a cautious scientific realism. Thus dualities are not new ammunition for the anti-realist. The paper also shows how the number of possible interpretative options about dualities that have been considered in the literature can be reduced, and suggests a general approach to scientific realism that one may take dualities to favour.

Two-dimensional (2D) magnets have broad application prospects in the spintronics, but how to effectively control them with a small electric field is still an issue. Here we propose that 2D magnets can be efficiently controlled in a multiferroic heterostructure composed of 2D magnetic material and perovskite oxide ferroelectric (POF) whose dielectric polarization is easily flipped under a small electric field. We illustrate the feasibility of such strategy in the bilayer CrI3/BiFeO3(001) heterostructure by using the first-principles calculations. Different from the traditional POF multiferroic heterostructures which have strong interface interactions, we find that the interface interaction between CrI3 and BiFeO3(001) is van der Waals type. Whereas, the heterostructure has particular strong magnetoelectric coupling where the bilayer CrI3 can be efficiently switched between ferromagnetic and antiferromagnetic types by the polarized states of BiFeO3(001). We also discover the competing effect between electron doping and the additional electric field on the interlayer exchange coupling interaction of CrI3, which is responsible to the magnetic phase transition. Our results provide a new avenue for the tuning of 2D magnets with a small electric field.

Previous studies have shown that the ground state of systems of nucleons composed by an equal number of protons and neutrons interacting via proton-neutron pairing forces can be described accurately by a condensate of $\alpha$-like quartets. Here we extend these studies to the low-lowing excited states of these systems and show that these states can be accurately described by breaking a quartet from the ground state condensate and replacing it with an "excited" quartet. This approach, which is analogous to the one-broken-pair approximation employed for like-particle pairing, is analysed for various isovector and isovector-isoscalar pairing

This paper focuses on regularisation methods using models up to the third order to search for up to second-order critical points of a finite-sum minimisation problem. The variant presented belongs to the framework of [3]: it employs random models with accuracy guaranteed with a sufficiently large prefixed probability and deterministic inexact function evaluations within a prescribed level of accuracy. Without assuming unbiased estimators, the expected number of iterations is $\mathcal{O}\bigl(\epsilon_1^{-2}\bigr)$ or $\mathcal{O}\bigl(\epsilon_1^{-{3/2}}\bigr)$ when searching for a first-order critical point using a second or third order model, respectively, and of $\mathcal{O}\bigl(\max[\epsilon_1^{-{3/2}},\epsilon_2^{-3}]\bigr)$ when seeking for second-order critical points with a third order model, in which $\epsilon_j$, $j\in\{1,2\}$, is the $j$th-order tolerance. These results match the worst-case optimal complexity for the deterministic counterpart of the method. Preliminary numerical tests for first-order optimality in the context of nonconvex binary classification in imaging, with and without Artifical Neural Networks (ANNs), are presented and discussed.

Organic-inorganic metal halide perovskites have recently attracted increasing attention as highly efficient light harvesting materials for photovoltaic applications. However, the precise control of crystallization and morphology of organometallic perovskites deposited from solution, considered crucial for enhancing the final photovoltaic performance, remains challenging. In this context, here, we report on growing microcrystalline deposits of CH3NH3PbI3 (MAPbI3), by one-step solution casting on cylinde-shaped quartz substrates (rods). We show that the substrate curvature has a strong influence on morphology of the obtained polycrystalline deposits of MAPbI3. Although the crystalline width and length markedly decreased for substrates with higher curvatures, the photoluminescence (PL) spectral peak positions did not significantly evolve for MAPbI3 deposits on substrates with different diameters. The crystalline size reduction and denser coverage of microcrystalline MAPbI3 deposits on cylinder-shaped substrates with higher curvatures were attributed to two major contributions, both related to the annealing step of the MAPbI3 deposits. In particular, the diameter-dependent variability of the heat capacities and the substrate curvature-enhanced solvent evaporation rate seemed to contribute the most to the crystallization process and the resulting morphology changes of MAPbI3 deposits on cylinder-shaped quartz substrates with various diameters. The longitudinal geometry of cylinder-shaped substrates provided also a facile solution for checking the PL response of the deposits of MAPbI3 exposed to the flow of various gaseous media, such as oxygen, nitrogen and argon. Overall, the approach reported herein inspires novel, cylinder-shaped geometries of MAPbI3 deposits, which can find applications in low-cost photo-optical devices, including gas sensors.

This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres labelled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes.

We prove that two Enriques surfaces defined over an algebraically closed field of characteristic different from $2$ are isomorphic if their Kuznetsov components are equivalent. This improves and complete our previous result joint with Nuer where the same statement is proved for generic Enriques surfaces.

Let $\{(A_i,B_i)\}_{i=1}^m$ be a set pair system. F\"{u}redi, Gy\'{a}rf\'{a}s and Kir\'{a}ly called it {\em $1$-cross intersecting} if $|A_i\cap B_j|$ is $1$ when $i\neq j$ and $0$ if $i=j$. They studied such systems and their generalizations, and in particular considered $m(a,b,1)$ -- the maximum size of a $1$-cross intersecting set pair system in which $|A_i|\leq a$ and $|B_i|\leq b$ for all $i$. F\"{u}redi, Gy\'{a}rf\'{a}s and Kir\'{a}ly proved that $m(n,n,1)\geq 5^{(n-1)/2}$ and asked whether there are upper bounds on $m(n,n,1)$ significantly better than the classical bound ${2n\choose n}$ of Bollob\' as for cross intersecting set pair systems. Answering one of their questions, Holzman recently proved that if $a,b\geq 2$, then $m(a,b,1)\leq \frac{29}{30}\binom{a+b}{a}$. He also conjectured that the factor $\frac{29}{30}$ in his bound can be replaced by $\frac{5}{6}$. The goal of this paper is to prove this bound.

The structure and stability of ternary systems prepared with polysorbate 60 and various combinations of cetyl (C16) and stearyl (C18) alcohols (fatty alcohol 16g, polysorbate 4g, water 180g) were examined as they aged over 3 months at 25oC. Rheological results showed that the consistency of these systems increased initially during roughly the first week of aging, which was succeeded by little changes in consistency (systems containing from 30% to 70% C18, with the 50% C18 system showing the highest consistencies in viscosity and elasticity) or significant breakdown of structure (remaining systems). The formation and/or disintegration of all ternary systems were also detected by microscopy and differential scanning calorimetry experiments. This study emphasizes the fact that the structure and consistency of ternary systems are dominantly controlled by the swelling capacity of the lamellar $\alpha-$crystalline gel phase. When the conversion of this gel phase into non-swollen $\beta$- or $\gamma$-crystals occurs, systems change from semisolids to fluids. Molecular dynamics simulations were performed to provide important details on the molecular mechanism of our ternary systems. Computational results supported the hypothesis experimentally proposed for the stability of the mixed system being due to an increase in the flexibility, hence an increase in the configurational entropy of the chain tip of the alcohol with a longer hydrocarbon chain (with the highest flexibility observed in the 50:50 C18:C16 system). This finding is in excellent agreement with experimental conclusions. Additionally, simulation data show that in the mixed system, the alcohol with shorter hydrocarbon chain becomes more rigid. These molecular details could not be available in experimental measurements

The slow revolution of the Earth and Moon around their barycentrum does not induce Coriolis accelerations. On the other hand, the motion of Sun and Earth is a rotation with Coriolis forces which appear not to have been calculated yet, nor have the inertial accelerations within the system of motion of all three celestial bodies. It is the purpose of this contribution to evaluate the related Coriolis and centrifugal terms and to compare them to the available atmospheric standard terms. It is a main result that the revolution is of central importance in the combined dynamics of Earth, Moon and Sun. Covariant flow equations are well known tools for dealing with such complicated flow settings. They are used here to quantify the effects of the Earth's revolution around the Earth-Moon barycenter and its rotation around the Sun on the atmospheric circulation. It is found that the motion around the Sun adds time dependent terms to the standard Coriolis forces. The related centrifugal accelerations are presented. A major part of these accelerations is balanced by the gravitational attraction by Moon and Sun, but important unbalanced contributions remain. New light on the consequences of the Earth's revolution is shed by repeating the calculations for a rotating Earth-Moon pair. It is found that the revolution complicates the atmospheric dynamics.

It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result. To that end, the community has seen many efforts to automate the process of finding the perfect augmentation procedure for any task at hand. Unfortunately, even recent cutting-edge methods bring massive computational overhead, requiring as many as 100 full model trainings to settle on an ideal configuration. We show how to achieve equivalent performance in just 6: with Random Unidimensional Augmentation. Source code is available at https://github.com/fastestimator/RUA

By processing in the frequency domain (FD), massive MIMO systems can approach the theoretical per-user capacity using a single carrier modulation (SCM) waveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and zero forcing (ZF) precoding have been shown to effectively cancel multi-user interference while compensating for inter-symbol interference. In this paper, we present a modified downlink precoding approach in the FD based on regularized zero forcing (RZF), which reuses the matrix inverses calculated as part of the FD MMSE uplink detection. By reusing these calculations, the computational complexity of the RZF precoder is drastically lowered, compared to the ZF precoder. Introduction of the regularization in RZF leads to a bias in the detected data symbols at the user terminals. We show this bias can be removed by incorporating a scaling factor at the receiver. Furthermore, it is noted that user powers have to be optimized to strike a balance between noise and interference seen at each user terminal. The resulting performance of the RZF precoder exceeds that of the ZF precoder for low and moderate input signal-to-noise ratio (SNR) conditions, and performance is equal for high input SNR. These results are established and confirmed by analysis and simulation.

We analyze the orthogonal greedy algorithm when applied to dictionaries $\mathbb{D}$ whose convex hull has small entropy. We show that if the metric entropy of the convex hull of $\mathbb{D}$ decays at a rate of $O(n^{-\frac{1}{2}-\alpha})$ for $\alpha > 0$, then the orthogonal greedy algorithm converges at the same rate on the variation space of $\mathbb{D}$. This improves upon the well-known $O(n^{-\frac{1}{2}})$ convergence rate of the orthogonal greedy algorithm in many cases, most notably for dictionaries corresponding to shallow neural networks. These results hold under no additional assumptions on the dictionary beyond the decay rate of the entropy of its convex hull. In addition, they are robust to noise in the target function and can be extended to convergence rates on the interpolation spaces of the variation norm. Finally, we show that these improved rates are sharp and prove a negative result showing that the iterates generated by the orthogonal greedy algorithm cannot in general be bounded in the variation norm of $\mathbb{D}$.

Network flows are one of the most studied combinatorial optimization problems with innumerable applications. Any flow on a directed acyclic graph (DAG) $G$ having $n$ vertices and $m$ edges can be decomposed into a set of $O(m)$ paths, with applications from network routing to assembly of biological sequences. In some applications, the flow decomposition corresponds to some particular data that need to be reconstructed from the flow, which require finding paths (or subpaths) appearing in all possible flow decompositions, referred to as safe paths. Recently, Ma et al. [WABI 2020] addressed a related problem in a probabilistic framework. Later, they gave a quadratic-time algorithm based on a global criterion, for a generalized version (AND-Quant) of the corresponding problem, i.e., reporting if a given flow path is safe. Our contributions are as follows: 1- A simple characterization for the safety of a given path based on a local criterion, which can be directly adapted to give an optimal linear time verification algorithm. 2- A simple enumeration algorithm that reports all maximal safe paths on a flow network in $O(mn)$ time. The algorithm reports all safe paths using a compact representation of the solution (called ${\cal P}_c$), which is $\Omega(mn)$ in the worst case, but merely $O(m+n)$ in the best case. 3- An improved enumeration algorithm where all safe paths ending at every vertex are represented as funnels using $O(n^2+|{\cal P}_c|)$ space. These can be computed and used to report all maximal safe paths, using time linear in the total space required by funnels, with an extra logarithmic factor. Overall we present a simple characterization for the problem leading to an optimal verification algorithm and a simple enumeration algorithm. The enumeration algorithm is improved using the funnel structures for safe paths, which may be of independent interest.

The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of two implementations of such software: the original open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a type of problem solvable on modern quantum annealers. We find that the Mukai QUBO solver outperforms the Ocean qbsolv for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the development of software to assist in the utilization of modern quantum annealers.

We derive the Thouless-Anderson-Palmer (TAP) equations for the Ghatak and Sherrington model. Our derivation, based on the cavity method, holds at high temperature and at all values of the crystal field. It confirms the prediction of Yokota.

One of the most complex and devastating disaster scenarios that the U.S.~Pacific Northwest region and the state of Oregon faces is a large magnitude Cascadia Subduction Zone earthquake event. The region's electrical grid lacks in resilience against the destruction of a megathrust earthquake, a powerful tsunami, hundreds of aftershocks and increased volcanic activity, all of which are highly probable components of this hazard. This research seeks to catalyze further understanding and improvement of resilience. By systematizing power system related experiences of historical earthquakes, and collecting practical and innovative ideas from other regions on how to enhance network design, construction, and operation, important steps are being taken toward a more resilient, earthquake-resistant grid. This paper presents relevant findings in an effort to be an overview and a useful guideline for those who are also working towards greater electrical grid resilience.

The representation of data and its relationships using networks is prevalent in many research fields such as computational biology, medical informatics and social networks. Recently, complex networks models have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks -based models have been introduced, which consist in mapping information as pair of networks containing the same nodes but different edges. We focus on the use of a novel approach to visualise and analyse dual networks. The method uses two algorithms for community discovery, and it is provided as a Python-based tool with a graphical user interface. The tool is able to load dual networks and to extract both the densest connected subgraph as well as the common modular communities. The latter is obtained by using an adapted implementation of the Louvain algorithm. The proposed algorithm and graphical tool have been tested by using social, biological, and co-authorship networks. Results demonstrate that the proposed approach is efficient and is able to extract meaningful information from dual networks. Finally, as contribution, the proposed graphical user interface can be considered a valuable innovation to the context.

In this article, we develop an algebraic framework of axioms which abstracts various high-level properties of multi-qudit representations of generalized Clifford algebras. We further construct an explicit model and prove that it satisfies these axioms. Strengths of our algebraic framework include the minimality of its assumptions, and the readiness by which one may give an explicit construction satisfying these assumptions. In terms of applications, this algebraic framework provides a solid foundation which opens the way for developing a graphical calculus for multi-qudit representations of generalized Clifford algebras using purely algebraic methods, which is addressed in a follow-up paper.

Assuming the Riemann hypothesis we establish explicit bounds for the modulus of the log-derivative of Riemann's zeta-function in the critical strip.

Abstract symbolic reasoning, as required in domains such as mathematics and logic, is a key component of human intelligence. Solvers for these domains have important applications, especially to computer-assisted education. But learning to solve symbolic problems is challenging for machine learning algorithms. Existing models either learn from human solutions or use hand-engineered features, making them expensive to apply in new domains. In this paper, we instead consider symbolic domains as simple environments where states and actions are given as unstructured text, and binary rewards indicate whether a problem is solved. This flexible setup makes it easy to specify new domains, but search and planning become challenging. We introduce four environments inspired by the Mathematics Common Core Curriculum, and observe that existing Reinforcement Learning baselines perform poorly. We then present a novel learning algorithm, Contrastive Policy Learning (ConPoLe) that explicitly optimizes the InfoNCE loss, which lower bounds the mutual information between the current state and next states that continue on a path to the solution. ConPoLe successfully solves all four domains. Moreover, problem representations learned by ConPoLe enable accurate prediction of the categories of problems in a real mathematics curriculum. Our results suggest new directions for reinforcement learning in symbolic domains, as well as applications to mathematics education.

With the ongoing penetration of conversational user interfaces, a better understanding of social and emotional characteristic inherent to dialogue is required. Chatbots in particular face the challenge of conveying human-like behaviour while being restricted to one channel of interaction, i.e., text. The goal of the presented work is thus to investigate whether characteristics of social intelligence embedded in human-chatbot interactions are perceivable by human interlocutors and if yes, whether such influences the experienced interaction quality. Focusing on the social intelligence dimensions Authenticity, Clarity and Empathy, we first used a questionnaire survey evaluating the level of perception in text utterances, and then conducted a Wizard of Oz study to investigate the effects of these utterances in a more interactive setting. Results show that people have great difficulties perceiving elements of social intelligence in text. While on the one hand they find anthropomorphic behaviour pleasant and positive for the naturalness of a dialogue, they may also perceive it as frightening and unsuitable when expressed by an artificial agent in the wrong way or at the wrong time.

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets.

We propose SinIR, an efficient reconstruction-based framework trained on a single natural image for general image manipulation, including super-resolution, editing, harmonization, paint-to-image, photo-realistic style transfer, and artistic style transfer. We train our model on a single image with cascaded multi-scale learning, where each network at each scale is responsible for image reconstruction. This reconstruction objective greatly reduces the complexity and running time of training, compared to the GAN objective. However, the reconstruction objective also exacerbates the output quality. Therefore, to solve this problem, we further utilize simple random pixel shuffling, which also gives control over manipulation, inspired by the Denoising Autoencoder. With quantitative evaluation, we show that SinIR has competitive performance on various image manipulation tasks. Moreover, with a much simpler training objective (i.e., reconstruction), SinIR is trained 33.5 times faster than SinGAN (for 500 X 500 images) that solves similar tasks. Our code is publicly available at github.com/YooJiHyeong/SinIR.

This paper studies the model compression problem of vision transformers. Benefit from the self-attention module, transformer architectures have shown extraordinary performance on many computer vision tasks. Although the network performance is boosted, transformers are often required more computational resources including memory usage and the inference complexity. Compared with the existing knowledge distillation approaches, we propose to excavate useful information from the teacher transformer through the relationship between images and the divided patches. We then explore an efficient fine-grained manifold distillation approach that simultaneously calculates cross-images, cross-patch, and random-selected manifolds in teacher and student models. Experimental results conducted on several benchmarks demonstrate the superiority of the proposed algorithm for distilling portable transformer models with higher performance. For example, our approach achieves 75.06% Top-1 accuracy on the ImageNet-1k dataset for training a DeiT-Tiny model, which outperforms other ViT distillation methods.

Adversarial examples mainly exploit changes to input pixels to which humans are not sensitive to, and arise from the fact that models make decisions based on uninterpretable features. Interestingly, cognitive science reports that the process of interpretability for human classification decision relies predominantly on low spatial frequency components. In this paper, we investigate the robustness to adversarial perturbations of models enforced during training to leverage information corresponding to different spatial frequency ranges. We show that it is tightly linked to the spatial frequency characteristics of the data at stake. Indeed, depending on the data set, the same constraint may results in very different level of robustness (up to 0.41 adversarial accuracy difference). To explain this phenomenon, we conduct several experiments to enlighten influential factors such as the level of sensitivity to high frequencies, and the transferability of adversarial perturbations between original and low-pass filtered inputs.

In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet. Unlike most existing works that separate the counting and localization tasks, we consider those tasks as a pixel-wise dense prediction problem and integrate them into an end-to-end framework. Specifically, for crowd counting, we adopt a counting head supervised by the Mean Square Error (MSE) loss. For crowd localization, the key insight is to recognize the keypoint of people, i.e., the center point of heads. We propose a localization head to distinguish dense crowds trained by two loss functions, i.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which balances the positive/negative examples and handles the false-positive predictions. Experiments on the recent and large-scale benchmark, NWPU-Crowd, show that our approach outperforms the state-of-the-art methods by more than 5% and 10% improvement in crowd localization and counting tasks, respectively. The code is publicly available at https://github.com/WangyiNTU/SCALNet.

Harmonic generation in atoms and molecules has reshaped our understanding of ultrafast phenomena beyond the traditional nonlinear optics and has launched attosecond physics. Harmonics from solids represent a new frontier, where both majority and minority spin channels contribute to harmonics.} This is true even in a ferromagnet whose electronic states are equally available to optical excitation. Here, we demonstrate that harmonics can be generated {mostly} from a single spin channel in half metallic chromium dioxide. {An energy gap in the minority channel greatly reduces the harmonic generation}, so harmonics predominantly emit from the majority channel, with a small contribution from the minority channel. However, this is only possible when the incident photon energy is well below the energy gap in the minority channel, so all the transitions in the minority channel are virtual. The onset of the photon energy is determined by the transition energy between the dipole-allowed transition between the O-$2p$ and Cr-$3d$ states. Harmonics {mainly} from a single spin channel can be detected, regardless of laser field strength, as far as the photon energy is below the minority band energy gap. This prediction should be tested experimentally.

Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit such prototypes for further analysis. We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative prototypes for temporal recognition tasks. DP-DTW shows superior performance compared to conventional DTWs on time series classification benchmarks. Combined with end-to-end deep learning, DP-DTW can handle challenging weakly supervised action segmentation problems and achieves state of the art results on standard benchmarks. Moreover, detailed reasoning on the input video is enabled by the learned action prototypes. Specifically, an action-based video summarization can be obtained by aligning the input sequence with action prototypes.

Hadronic matrix elements of local four-quark operators play a central role in non-leptonic kaon decays, while vacuum matrix elements involving the same kind of operators appear in inclusive dispersion relations, such as those relevant in $\tau$-decay analyses. Using an $SU(3)_L\otimes SU(3)_R$ decomposition of the operators, we derive generic relations between these matrix elements, extending well-known results that link observables in the two different sectors. Two relevant phenomenological applications are presented. First, we determine the electroweak-penguin contribution to the kaon CP-violating ratio $\varepsilon'/\varepsilon$, using the measured hadronic spectral functions in $\tau$ decay. Second, we fit our $SU(3)$ dynamical parameters to the most recent lattice data on $K\to\pi\pi$ matrix elements. The comparison of this numerical fit with results from previous analytical approaches provides an interesting anatomy of the $\Delta I = \frac{1}{2}$ enhancement, confirming old suggestions about its underlying dynamical origin.

We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data. We used a timestep reduction technique to reduce the computational and memory requirements and trained a bidirection LSTM network to predict soil water and stream flow from time series data observed and simulated over eighty years in the Wabash River Watershed. We show that our simple model can be trained much faster than complex attention networks such as GeoMAN without sacrificing accuracy. Based on the predicted values of soil water and stream flow, we predict the occurrence and severity of extreme hydrologic events such as droughts. We also demonstrate that extreme events can be predicted in geographical locations separate from locations observed during the training process. This spatially-inductive setting enables us to predict extreme events in other areas in the US and other parts of the world using our model trained with the Wabash Basin data.

Electronic and optical properties of doped organic semiconductors are dominated by local interactions between donor and acceptor molecules. However, when such systems are in crystalline form, long-range order competes against short-range couplings. In a first-principles study on three experimentally resolved bulk structures of quaterthiophene doped by (fluorinated) tetracyanoquinodimethane, we demonstrate the crucial role of long-range interactions in donor/acceptor co-crystals. The band structures of the investigated materials exhibit direct band-gaps decreasing in size with increasing amount of F atoms in the acceptors. The valence-band maximum and conduction-band minimum are found at the Brillouin zone boundary and the corresponding wave-functions are segregated on donor and acceptor molecules, respectively. With the aid of a tight-binding model, we rationalize that the mechanisms responsible for these behaviors, which are ubiquitous in donor/acceptor co-crystals, are driven by long-range interactions. The optical response of the analyzed co-crystals is highly anisotropic. The absorption onset is dominated by an intense resonance corresponding to a charge-transfer excitation. Long-range interactions are again responsible for this behavior, which enhances the efficiency of the co-crystals for photo-induced charge separation and transport. In addition to this result, which has important implications in the rational design of organic materials for opto-electronics, our study clarifies that cluster models, accounting only for local interactions, cannot capture the relevant impact of long-range order in donor/acceptor co-crystals.

Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretability, making the behaviors of the deep networks difficult to explain. Hence, their potential applications could be limited in practice. To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP. Given a set of past surrounding-view images, our IVMP first predicts future egocentric semantic maps in bird's-eye-view space, which are then employed to plan trajectories for self-driving vehicles. The predicted future semantic maps not only provide useful interpretable information, but also allow our motion planning module to handle objects with low probability, thus improving the safety of autonomous driving. Moreover, we also develop an optical flow distillation paradigm, which can effectively enhance the network while still maintaining its real-time performance. Extensive experiments on the nuScenes dataset and closed-loop simulation show that our IVMP significantly outperforms the state-of-the-art approaches in imitating human drivers with a much higher success rate. Our project page is available at https://sites.google.com/view/ivmp.

We develop methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion. Our framework builds on ideas from conformal inference to provide a general wrapper that can be combined with any black box method that produces point predictions of the unseen label or estimated quantiles of its distribution. While previous conformal inference methods rely on the assumption that the data points are exchangeable, our adaptive approach provably achieves the desired coverage frequency over long-time intervals irrespective of the true data generating process. We accomplish this by modelling the distribution shift as a learning problem in a single parameter whose optimal value is varying over time and must be continuously re-estimated. We test our method, adaptive conformal inference, on two real world datasets and find that its predictions are robust to visible and significant distribution shifts.

We analyze a fully discrete finite element numerical scheme for the Cahn-Hilliard-Stokes-Darcy system that models two-phase flows in coupled free flow and porous media. To avoid a well-known difficulty associated with the coupling between the Cahn-Hilliard equation and the fluid motion, we make use of the operator-splitting in the numerical scheme, so that these two solvers are decoupled, which in turn would greatly improve the computational efficiency. The unique solvability and the energy stability have been proved in~\cite{CHW2017}. In this work, we carry out a detailed convergence analysis and error estimate for the fully discrete finite element scheme, so that the optimal rate convergence order is established in the energy norm, i.e.,, in the $\ell^\infty (0, T; H^1) \cap \ell^2 (0, T; H^2)$ norm for the phase variables, as well as in the $\ell^\infty (0, T; H^1) \cap \ell^2 (0, T; H^2)$ norm for the velocity variable. Such an energy norm error estimate leads to a cancellation of a nonlinear error term associated with the convection part, which turns out to be a key step to pass through the analysis. In addition, a discrete $\ell^2 (0;T; H^3)$ bound of the numerical solution for the phase variables plays an important role in the error estimate, which is accomplished via a discrete version of Gagliardo-Nirenberg inequality in the finite element setting.

In this paper, the Hankel transform of the generalized q-exponential polynomial of the first form (q, r)-Whitney numbers of the second kind is established using the method of Cigler. Consequently, the Hankel transform of the first form (q, r)-Dowling numbers is obtained as special case.

In the article, we investigate the diquark-diquark-antiquark type fully-heavy pentaquark states with the spin-parity $J^P={\frac{1}{2}}^-$ via the QCD sum rules, and obtain the masses $M_{cccc\bar{c}}=7.93\pm 0.15\,\rm{GeV}$ and $M_{bbbb\bar{b}}=23.91\pm0.15\,\rm{GeV}$. We can search for the fully-heavy pentaquark states in the $J/\psi \Omega_{ccc}$ and $\Upsilon \Omega_{bbb}$ invariant mass spectrum in the future.

Optical phenomena associated with extremely localized field should be understood with considerations of nonlocal and quantum effects, which pose a hurdle to conceptualize the physics with a picture of eigenmodes. Here we first propose a generalized Lorentz model to describe general nonlocal media under linear mean-field approximation and formulate source-free Maxwell's equations as a linear eigenvalue problem to define the quasinormal modes. Then we introduce an orthonormalization scheme for the modes and establish a canonical quasinormal mode framework for general nonlocal media. Explicit formalisms for metals described by quantum hydrodynamic model and polar dielectrics with nonlocal response are exemplified. The framework enables for the first time direct modal analysis of mode transition in the quantum tunneling regime and provides physical insights beyond usual far-field spectroscopic analysis. Applied to nonlocal polar dielectrics, the framework also unveils the important roles of longitudinal phonon polaritons in optical response.

Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-based vulnerabilities, which casts a shadow on its applications. As smart contracts are unpatchable (due to the immutability of blockchain), it is essential that smart contracts are guaranteed to be free of vulnerabilities. Unfortunately, smart contract languages such as Solidity are Turing-complete, which implies that verifying them statically is infeasible. Thus, alternative approaches must be developed to provide the guarantee. In this work, we develop an approach which automatically transforms smart contracts so that they are provably free of 4 common kinds of vulnerabilities. The key idea is to apply runtime verification in an efficient and provably correct manner. Experiment results with 5000 smart contracts show that our approach incurs minor run-time overhead in terms of time (i.e., 14.79%) and gas (i.e., 0.79%).

We present a structure preserving discretization of the fundamental spacetime geometric structures of fluid mechanics in the Lagrangian description in 2D and 3D. Based on this, multisymplectic variational integrators are developed for barotropic and incompressible fluid models, which satisfy a discrete version of Noether theorem. We show how the geometric integrator can handle regular fluid motion in vacuum with free boundaries and constraints such as the impact against an obstacle of a fluid flowing on a surface. Our approach is applicable to a wide range of models including the Boussinesq and shallow water models, by appropriate choice of the Lagrangian.

Modularity of neural networks -- both biological and artificial -- can be thought of either structurally or functionally, and the relationship between these is an open question. We show that enforcing structural modularity via sparse connectivity between two dense sub-networks which need to communicate to solve the task leads to functional specialization of the sub-networks, but only at extreme levels of sparsity. With even a moderate number of interconnections, the sub-networks become functionally entangled. Defining functional specialization is in itself a challenging problem without a universally agreed solution. To address this, we designed three different measures of specialization (based on weight masks, retraining and correlation) and found them to qualitatively agree. Our results have implications in both neuroscience and machine learning. For neuroscience, it shows that we cannot conclude that there is functional modularity simply by observing moderate levels of structural modularity: knowing the brain's connectome is not sufficient for understanding how it breaks down into functional modules. For machine learning, using structure to promote functional modularity -- which may be important for robustness and generalization -- may require extremely narrow bottlenecks between modules.

Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best peforming approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose PatchCore, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the standard dataset MVTec AD, PatchCore achieves an image-level anomaly detection AUROC score of $99.1\%$, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime.

Novel many-body and topological electronic phases can be created in assemblies of interacting spins coupled to a superconductor, such as one-dimensional topological superconductors with Majorana zero modes (MZMs) at their ends. Understanding and controlling interactions between spins and the emergent band structure of the in-gap Yu-Shiba-Rusinov (YSR) states they induce in a superconductor are fundamental for engineering such phases. Here, by precisely positioning magnetic adatoms with a scanning tunneling microscope (STM), we demonstrate both the tunability of exchange interaction between spins and precise control of the hybridization of YSR states they induce on the surface of a bismuth (Bi) thin film that is made superconducting with the proximity effect. In this platform, depending on the separation of spins, the interplay between Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction, spin-orbit coupling, and surface magnetic anisotropy stabilizes different types of spin alignments. Using high-resolution STM spectroscopy at millikelvin temperatures, we probe these spin alignments through monitoring the spin-induced YSR states and their energy splitting. Such measurements also reveal a quantum phase transition between the ground states with different electron number parity for a pair of spins in a superconductor tuned by their separation. Experiments on larger assemblies show that spin-spin interactions can be mediated in a superconductor over long distances. Our results show that controlling hybridization of the YSR states in this platform provides the possibility of engineering the band structure of such states for creating topological phases.

We describe a numerical method that simulates the interaction of the helium atom with sequences of femtosecond and attosecond light pulses. The method, which is based on the close-coupling expansion of the electronic configuration space in a B-spline bipolar spherical harmonic basis, can accurately reproduce the excitation and single ionization of the atom, within the electrostatic approximation. The time dependent Schr\"odinger equation is integrated with a sequence of second-order split-exponential unitary propagators. The asymptotic channel-, energy- and angularly-resolved photoelectron distributions are computed by projecting the wavepacket at the end of the simulation on the multichannel scattering states of the atom, which are separately computed within the same close-coupling basis. This method is applied to simulate the pump-probe ionization of helium in the vicinity of the $2s/2p$ excitation threshold of the He$^+$ ion. This work confirms the qualitative conclusions of one of our earliest publications [L Argenti and E Lindroth, Phys. Rev. Lett. {\bf 105}, 53002 (2010)], in which we demonstrated the control of the $2s/2p$ ionization branching-ratio. Here, we take those calculations to convergence and show how correlation brings the periodic modulation of the branching ratios in almost phase opposition. The residual total ionization probability to the $2s+2p$ channels is dominated by the beating between the $sp_{2,3}^+$ and the $sp_{2,4}^+$ doubly excited states, which is consistent with the modulation of the complementary signal in the $1s$ channel, measured in 2010 by Chang and co-workers~[S Gilbertson~\emph{et al.}, Phys. Rev. Lett. {\bf 105}, 263003 (2010)].

Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.

The formation of $\alpha$ particle on nuclear surface has been a fundamental problem since the early age of nuclear physics. It strongly affects the $\alpha$ decay lifetime of heavy and superheavy elements, level scheme of light nuclei, and the synthesis of the elements in stars. However, the $\alpha$-particle formation in medium-mass nuclei has been poorly known despite its importance. Here, based on the $^{48}{\rm Ti}(p,p\alpha)^{44}{\rm Ca}$ reaction analysis, we report that the $\alpha$-particle formation in a medium-mass nucleus $^{48}{\rm Ti}$ is much stronger than that expected from a mean-field approximation, and the estimated average distance between $\alpha$ particle and the residue is as large as 4.5 fm. This new result poses a challenge of describing four nucleon correlations by microscopic nuclear models.

Giant spin-splitting was recently predicted in collinear antiferromagnetic materials with a specific class of magnetic space group. In this work, we have predicted a two-dimensional (2D) antiferromagnetic Weyl semimetal (WS), CrO with large spin-split band structure, spin-momentum locked transport properties and high N\'eel temperature. It has two pairs of spin-polarized Weyl points at the Fermi level. By manipulating the position of the Weyl points with strain, four different antiferromagnetic spintronic states can be achieved: WSs with two spin-polarized transport channels (STCs), WSs with single STC, semiconductors with two STCs, and semiconductors with single STC. Based on these properties, a new avenue in spintronics with 2D collinear antiferromagnets is proposed.

Being the seventh most spoken language in the world, the use of the Bangla language online has increased in recent times. Hence, it has become very important to analyze Bangla text data to maintain a safe and harassment-free online place. The data that has been made accessible in this article has been gathered and marked from the comments of people in public posts by celebrities, government officials, athletes on Facebook. The total amount of collected comments is 44001. The dataset is compiled with the aim of developing the ability of machines to differentiate whether a comment is a bully expression or not with the help of Natural Language Processing and to what extent it is improper if it is an inappropriate comment. The comments are labeled with different categories of harassment. Exploratory analysis from different perspectives is also included in this paper to have a detailed overview. Due to the scarcity of data collection of categorized Bengali language comments, this dataset can have a significant role for research in detecting bully words, identifying inappropriate comments, detecting different categories of Bengali bullies, etc. The dataset is publicly available at https://data.mendeley.com/datasets/9xjx8twk8p.

Let $\Omega \Subset \mathbb R^n$, $f \in C^1(\mathbb R^{N\times n})$ and $g\in C^1(\mathbb R^N)$, where $N,n \in \mathbb N$. We study the minimisation problem of finding $u \in W^{1,\infty}_0(\Omega;\mathbb R^N)$ that satisfies \[ \big\| f(\mathrm D u) \big\|_{L^\infty(\Omega)} \! = \inf \Big\{\big\| f(\mathrm D v) \big\|_{L^\infty(\Omega)} \! : \ v \! \in W^{1,\infty}_0(\Omega;\mathbb R^N), \, \| g(v) \|_{L^\infty(\Omega)}\! =1\Big\}, \] under natural assumptions on $f,g$. This includes the $\infty$-eigenvalue problem as a special case. Herein we prove existence of a minimiser $u_\infty$ with extra properties, derived as the limit of minimisers of approximating constrained $L^p$ problems as $p\to \infty$. A central contribution and novelty of this work is that $u_\infty$ is shown to solve a divergence PDE with measure coefficients, whose leading term is a divergence counterpart equation of the non-divergence $\infty$-Laplacian. Our results are new even in the scalar case of the $\infty$-eigenvalue problem.

This paper presents the detailed simulation of a double-pixel structure for charged particle detection based on the 3D-trench silicon sensor developed for the TIMESPOT project and a comparison of the simulation results with measurements performed at $\pi-$M1 beam at PSI laboratory. The simulation is based on the combined use of several software tools (TCAD, GEANT4, TCoDe and TFBoost) which allow to fully design and simulate the device physics response in very short computational time, O(1-100 s) per simulated signal, by exploiting parallel computation using single or multi-thread processors. This allowed to produce large samples of simulated signals, perform detailed studies of the sensor characteristics and make precise comparisons with experimental results.

We investigate the possibility that radio-bright active galactic nuclei (AGN) are responsible for the TeV--PeV neutrinos detected by IceCube. We use an unbinned maximum-likelihood-ratio method, 10 years of IceCube muon-track data, and 3388 radio-bright AGN selected from the Radio Fundamental Catalog. None of the AGN in the catalog have a large global significance. The two most significant sources have global significance of $\simeq$ 1.5$\sigma$ and 0.8$\sigma$, though 4.1$\sigma$ and 3.8$\sigma$ local significance. Our stacking analyses show no significant correlation between the whole catalog and IceCube neutrinos. We infer from the null search that this catalog can account for at most 30\% (95\% CL) of the diffuse astrophysical neutrino flux measured by IceCube. Moreover, our results disagree with recent work that claimed a 4.1$\sigma$ detection of neutrinos from the sources in this catalog, and we discuss the reasons of the difference.

We propose a generalization of the coherent anomaly method to extract the critical exponents of a phase transition occurring in the steady-state of an open quantum many-body system. The method, originally developed by Suzuki [J. Phys. Soc. Jpn. {\bf 55}, 4205 (1986)] for equilibrium systems, is based on the scaling properties of the singularity in the response functions determined through cluster mean-field calculations. We apply this method to the dissipative transverse-field Ising model and the dissipative XYZ model in two dimensions obtaining convergent results already with small clusters.

As a step towards quantization of Higher Spin Gravities we construct the presymplectic AKSZ sigma-model for $4d$ Higher Spin Gravity which is AdS/CFT dual of Chern-Simons vector models. It is shown that the presymplectic structure leads to the correct quantum commutator of higher spin fields and to the correct algebra of the global higher spin symmetry currents. The presymplectic AKSZ model is proved to be unique, it depends on two coupling constants in accordance with the AdS/CFT duality, and it passes some simple checks of interactions.

Electric, intelligent, and network are the most important future development directions of automobiles. Intelligent electric vehicles have shown great potentials to improve traffic mobility and reduce emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key factor in traffic mobility improvement. In this paper, we propose a graph-based cooperation method to formalize the conflict-free scheduling problem at unsignalized intersections. Firstly, conflict directed graphs and coexisting undirected graphs are built to describe the conflict relationship of the vehicles. Then, two graph-based methods are introduced to solve the vehicle passing order. One method is an optimized depth-first spanning tree method which aims to find the local optimal passing order for each vehicle. The other method is a maximum matching algorithm that solves the global optimal problem. The computational complexity of both methods is also derived. Numerical simulation results demonstrate the effectiveness of the proposed algorithms.

As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thus has attracted more and more attention. Due to the limitations of high complexity and low efficiency of traditional speech synthesis technology, the current research focus is the deep learning-based end-to-end speech synthesis technology, which has more powerful modeling ability and a simpler pipeline. It mainly consists of three modules: text front-end, acoustic model, and vocoder. This paper reviews the research status of these three parts, and classifies and compares various methods according to their emphasis. Moreover, this paper also summarizes the open-source speech corpus of English, Chinese and other languages that can be used for speech synthesis tasks, and introduces some commonly used subjective and objective speech quality evaluation method. Finally, some attractive future research directions are pointed out.

The internet advertising market is a multi-billion dollar industry, in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. In recent years, the industry has shifted to first-price auctions as the preferred paradigm for selling advertising slots. Another important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in first-price auctions for advertisers who satisfy budget constraints on average. Our model dispenses with the common, yet unrealistic assumption that advertisers' values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value-pacing-based Bayes-Nash equilibrium under very mild assumptions, and study its structural properties. Furthermore, we generalize the existence result to standard auctions and prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints.

We study the dynamics of a ferrofluid thin film confined in a Hele-Shaw cell, and subjected to a tilted nonuniform magnetic field. It is shown that the interface between the ferrofluid and an inviscid outer fluid (air) supports traveling waves, governed by a novel modified Kuramoto--Sivashinsky-type equation derived under the long-wave approximation. The balance between energy production and dissipation in this long-wave equations allows for the existence of dissipative solitons. These permanent traveling waves' propagation velocity and profile shape are shown to be tunable via the external magnetic field. A multiple-scale analysis is performed to obtain the correction to the linear prediction of the propagation velocity, and to reveal how the nonlinearity arrests the linear instability. The traveling periodic interfacial waves discovered are identified as fixed points in an energy phase plane. It is shown that transitions between states (wave profiles) occur. These transitions are explained via the spectral stability of the traveling waves. Interestingly, multiperiodic waves, which are a non-integrable analog of the double cnoidal wave, are also found to propagate under the model long-wave equation. These multiperiodic solutions are investigated numerically, and they are found to be long-lived transients, but ultimately abruptly transition to one of the stable periodic states identified.

The origin of the gamma-ray emission of the blazar Mrk 421 is still a matter of debate. We used 5.5 years of unbiased observing campaign data, obtained using the FACT telescope and the Fermi LAT detector at TeV and GeV energies, the longest and densest so far, together with contemporaneous multi-wavelength observations, to characterise the variability of Mrk 421 and to constrain the underlying physical mechanisms. We studied and correlated light curves obtained by ten different instruments and found two significant results. The TeV and X-ray light curves are very well correlated with a lag of <0.6 days. The GeV and radio (15 Ghz band) light curves are widely and strongly correlated. Variations of the GeV light curve lead those in the radio. Lepto-hadronic and purely hadronic models in the frame of shock acceleration predict proton acceleration or cooling timescales that are ruled out by the short variability timescales and delays observed in Mrk 421. Instead the observations match the predictions of leptonic models.

A pair-density wave state has been suggested to exist in underdoped cuprate superconductors, with some supporting experimental evidence emerging over the past few years from scanning tunneling spectroscopy. Several studies have also linked the observed quantum oscillations in these systems to a reconstruction of the Fermi surface by a pair-density wave. Here, we show, using semiclassical analysis and numerical calculations, that a Fermi pocket created by first-order scattering from a pair-density wave cannot induce such oscillations. In contrast, pockets resulting from second-order scattering can cause oscillations. We consider the effects of a finite pair-density wave correlation length on the signal, and demonstrate that it is only weakly sensitive to disorder in the form of $\pi$-phase slips. Finally, we discuss our results in the context of the cuprates and show that a bidirectional pair-density wave may produce observed oscillation frequencies.

Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data samples. Recent works highlighted several privacy and robustness weaknesses in FL and addressed these concerns using local differential privacy (LDP) and some well-studied methods used in conventional ML, separately. However, it is still not clear how LDP affects adversarial robustness in FL. To fill this gap, this work attempts to develop a comprehensive understanding of the effects of LDP on adversarial robustness in FL. Clarifying the interplay is significant since this is the first step towards a principled design of private and robust FL systems. We certify that local differential privacy has both positive and negative effects on adversarial robustness using theoretical analysis and empirical verification.

We define the flow group of any component of any stratum of rooted abelian or quadratic differentials (those marked with a horizontal separatrix) to be the group generated by almost-flow loops. We prove that the flow group is equal to the fundamental group of the component. As a corollary, we show that the plus and minus modular Rauzy--Veech groups are finite-index subgroups of their ambient modular monodromy groups. This partially answers a question of Yoccoz. Using this, and recent advances on algebraic hulls and Zariski closures of monodromy groups, we prove that the Rauzy--Veech groups are Zariski dense in their ambient symplectic groups. Density, in turn, implies the simplicity of the plus and minus Lyapunov spectra of any component of any stratum of quadratic differentials. Thus, we establish the Kontsevich--Zorich conjecture.

By using the Boole summation formula, we obtain asymptotic expansions for the first and higher order derivatives of the alternating Hurwitz zeta function $$\zeta_{E}(z,q)=\sum_{n=0}^\infty\frac{(-1)^{n}}{(n+q)^z}$$ with respect to its first argument $$\zeta_{E}^{(m)}(z,q)\equiv\frac{\partial^m}{\partial z^m}\zeta_E(z,q).$$

We show that every finite semilattice can be represented as an atomized semilattice, an algebraic structure with additional elements (atoms) that extend the semilattice's partial order. Each atom maps to one subdirectly irreducible component, and the set of atoms forms a hypergraph that fully defines the semilattice. An atomization always exists and is unique up to "redundant atoms". Atomized semilattices are representations that can be used as computational tools for building semilattice models from sentences, as well as building its subalgebras and products. Atomized semilattices can be applied to machine learning and to the study of semantic embeddings into algebras with idempotent operators.

The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of generative models deteriorates significantly with the distortions present in real-world input signals. We argue that this deterioration is due to the sensitivity of the maximum likelihood criterion to outliers and the ineffectiveness of modeling a sum of independent signals with a single autoregressive model. We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance. We show that noise reduction to remove unwanted signals can significantly increase performance. We provide extensive subjective performance evaluations that show that our system based on generative modeling provides state-of-the-art coding performance at 3 kb/s for real-world speech signals at reasonable computational complexity.

We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally ill-posed due to the loss of information during image formation. Current solutions require precise 3D labels which are labor-intensive to gather, and infeasible to create for many systems such as deformable solids or cloth. We present gradSim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering to jointly model the evolution of scene dynamics and image formation. This novel combination enables backpropagation from pixels in a video sequence through to the underlying physical attributes that generated them. Moreover, our unified computation graph -- spanning from the dynamics and through the rendering process -- enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision, while obtaining performance competitive to or better than techniques that rely on precise 3D labels.

In this paper, we study quasi post-critically finite degenerations for rational maps. We construct limits for such degenerations as geometrically finite rational maps on a finite tree of Riemann spheres. We prove the boundedness for such degenerations of hyperbolic rational maps with Sierpinski carpet Julia set and give criteria for the convergence for quasi-Blaschke products $\mathcal{QB}_d$, making progress towards the analogues of Thurston's compactness theorem for acylindrical $3$-manifold and the double limit theorem for quasi-Fuchsian groups in complex dynamics. In the appendix, we apply such convergence results to show the existence of certain polynomial matings.

Optical wireless communications (OWCs) have been recognized as a candidate enabler of next generation in-body nano-scale networks and implants. The development of an accurate channel model capable of accommodating the particularities of different type of tissues is expected to boost the design of optimized communication protocols for such applications. Motivated by this, this paper focuses on presenting a general pathloss model for in-body OWCs. In particular, we use experimental measurements in order to extract analytical expressions for the absorption coefficients of the five main tissues' constitutions, namely oxygenated and de-oxygenated blood, water, fat, and melanin. Building upon these expressions, we derive a general formula for the absorption coefficient evaluation of any biological tissue. To verify the validity of this formula, we compute the absorption coefficient of complex tissues and compare them against respective experimental results reported by independent research works. Interestingly, we observe that the analytical formula has high accuracy and is capable of modeling the pathloss and, therefore, the penetration depth in complex tissues.

We theoretically analyze a possibility of electromagnetic wave emission due to electron transitions between spin subbands in a ferromagnet. Different mechanisms of such spin-flip transitions are cousidered. One mechanism is the electron transitions caused by magnetic field of the wave. Another mechanism is due to Rashba spin-orbit interaction. While two mentioned mechanisms exist in a homogeneously magnetized ferromagnet, there are two other mechanisms that exist in non-collinearly magnetized medium. First mechanism is known and is due to the dependence of exchange interaction constant on the quasimomentum of conduction electrons. Second one exists in any non-collinearly magnetized medium. We study these mechanisms in a non-collinear ferromagnet with helicoidal magnetization distribution. The estimations of probabilities of electron transitions due to different mechanisms are made for realistic parameters, and we compare the mechanisms. We also estimate the radiation power and threshold current in a simple model in which spin is injected into the ferromagnet by a spin-polarized electric current through a tunnel barrier.

We consider the sharp interface limit for the scalar-valued and vector-valued Allen-Cahn equation with homogeneous Neumann boundary condition in a bounded smooth domain $\Omega$ of arbitrary dimension $N\geq 2$ in the situation when a two-phase diffuse interface has developed and intersects the boundary $\partial\Omega$. The limit problem is mean curvature flow with $90${\deg}-contact angle and we show convergence in strong norms for well-prepared initial data as long as a smooth solution to the limit problem exists. To this end we assume that the limit problem has a smooth solution on $[0,T]$ for some time $T>0$. Based on the latter we construct suitable curvilinear coordinates and set up an asymptotic expansion for the scalar-valued and the vector-valued Allen-Cahn equation. Finally, we prove a spectral estimate for the linearized Allen-Cahn operator in both cases in order to estimate the difference of the exact and approximate solutions with a Gronwall-type argument.

We prove that the unique possible flow in an Alexandroff $T_{0}$-space is the trivial one. On the way of motivation, we relate Alexandroff spaces with topological hyperspaces.

Transformer models have demonstrated superior performance in natural language processing. The dot product self-attention in Transformer allows us to model interactions between words. However, this modeling comes with significant computational overhead. In this work, we revisit the memory-compute trade-off associated with Transformer, particularly multi-head attention, and show a memory-heavy but significantly more compute-efficient alternative to Transformer. Our proposal, denoted as PairConnect, a multilayer perceptron (MLP), models the pairwise interaction between words by explicit pairwise word embeddings. As a result, PairConnect substitutes self dot product with a simple embedding lookup. We show mathematically that despite being an MLP, our compute-efficient PairConnect is strictly more expressive than Transformer. Our experiment on language modeling tasks suggests that PairConnect could achieve comparable results with Transformer while reducing the computational cost associated with inference significantly.

Multi-agent collision-free trajectory planning and control subject to different goal requirements and system dynamics has been extensively studied, and is gaining recent attention in the realm of machine and reinforcement learning. However, in particular when using a large number of agents, constructing a least-restrictive collision avoidance policy is of utmost importance for both classical and learning-based methods. In this paper, we propose a Least-Restrictive Collision Avoidance Module (LR-CAM) that evaluates the safety of multi-agent systems and takes over control only when needed to prevent collisions. The LR-CAM is a single policy that can be wrapped around policies of all agents in a multi-agent system. It allows each agent to pursue any objective as long as it is safe to do so. The benefit of the proposed least-restrictive policy is to only interrupt and overrule the default controller in case of an upcoming inevitable danger. We use a Long Short-Term Memory (LSTM) based Variational Auto-Encoder (VAE) to enable the LR-CAM to account for a varying number of agents in the environment. Moreover, we propose an off-policy meta-reinforcement learning framework with a novel reward function based on a Hamilton-Jacobi value function to train the LR-CAM. The proposed method is fully meta-trained through a ROS based simulation and tested on real multi-agent system. Our results show that LR-CAM outperforms the classical least-restrictive baseline by 30 percent. In addition, we show that even if a subset of agents in a multi-agent system use LR-CAM, the success rate of all agents will increase significantly.

In this article we will show $2$ different proofs for the fact that there exist relatively prime positive integers $a,b$ such that: $a^2+ab+b^2=7^n$.

We theoretically analyze the typical learning performance of $\ell_{1}$-regularized linear regression ($\ell_1$-LinR) for Ising model selection using the replica method from statistical mechanics. For typical random regular graphs in the paramagnetic phase, an accurate estimate of the typical sample complexity of $\ell_1$-LinR is obtained. Remarkably, despite the model misspecification, $\ell_1$-LinR is model selection consistent with the same order of sample complexity as $\ell_{1}$-regularized logistic regression ($\ell_1$-LogR), i.e., $M=\mathcal{O}\left(\log N\right)$, where $N$ is the number of variables of the Ising model. Moreover, we provide an efficient method to accurately predict the non-asymptotic behavior of $\ell_1$-LinR for moderate $M, N$, such as precision and recall. Simulations show a fairly good agreement between theoretical predictions and experimental results, even for graphs with many loops, which supports our findings. Although this paper mainly focuses on $\ell_1$-LinR, our method is readily applicable for precisely characterizing the typical learning performances of a wide class of $\ell_{1}$-regularized $M$-estimators including $\ell_1$-LogR and interaction screening.

We consider the recent surge of information on the potential benefits of acid-suppression drugs in the context of COVID-19, with an eye on the variability (and confusion) across the reported findings--at least as regards the popular antacid famotidine. The inconsistencies reflect contradictory conclusions from independent clinical-based studies that took roughly similar approaches, in terms of experimental design (retrospective, cohort-based, etc.) and statistical analyses (propensity-score matching and stratification, etc.). The confusion has significant ramifications in choosing therapeutic interventions: e.g., do potential benefits of famotidine indicate its use in a particular COVID-19 case? Beyond this pressing therapeutic issue, conflicting information on famotidine must be resolved before its integration in ontological and knowledge graph-based frameworks, which in turn are useful in drug repurposing efforts. To begin systematically structuring the rapidly accumulating information, in the hopes of clarifying and reconciling the discrepancies, we consider the contradictory information along three proposed 'axes': (1) a context-of-disease axis, (2) a degree-of-[therapeutic]-benefit axis, and (3) a mechanism-of-action axis. We suspect that incongruencies in how these axes have been (implicitly) treated in past studies has led to the contradictory indications for famotidine and COVID-19. We also trace the evolution of information on acid-suppression agents as regards the transmission, severity, and mortality of COVID-19, given the many literature reports that have accumulated. By grouping the studies conceptually and thematically, we identify three eras in the progression of our understanding of famotidine and COVID-19. Harmonizing these findings is a key goal for both clinical standards-of-care (COVID and beyond) as well as ontological and knowledge graph-based approaches.

We consider the asymmetric simple exclusion process (ASEP) with forward hopping rate 1, backward hopping rate q and periodic boundary conditions. We show that the Bethe equations of ASEP can be decoupled, at all order in perturbation in the variable q, by introducing a formal Laurent series mapping the Bethe roots of the totally asymmetric case q=0 (TASEP) to the Bethe roots of ASEP. The probability of the height for ASEP is then written as a single contour integral on the Riemann surface on which symmetric functions of TASEP Bethe roots live.

In this study, we investigated a method allowing the determination of the femur bone surface as well as its mechanical axis from some easy-to-identify bony landmarks. The reconstruction of the whole femur is therefore performed from these landmarks using a Statistical Shape Model (SSM). The aim of this research is therefore to assess the impact of the number, the position, and the accuracy of the landmarks for the reconstruction of the femur and the determination of its related mechanical axis, an important clinical parameter to consider for the lower limb analysis. Two statistical femur models were created from our in-house dataset and a publicly available dataset. Both were evaluated in terms of average point-to-point surface distance error and through the mechanical axis of the femur. Furthermore, the clinical impact of using landmarks on the skin in replacement of bony landmarks is investigated. The predicted proximal femurs from bony landmarks were more accurate compared to on-skin landmarks while both had less than 3.5 degrees mechanical axis angle deviation error. The results regarding the non-invasive determination of the mechanical axis are very encouraging and could open very interesting clinical perspectives for the analysis of the lower limb either for orthopedics or functional rehabilitation.

Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot account for the heavy tail phenomenon of stock returns. Recently, a robust estimator was developed to handle heavy-tailed distributions with some bounded fourth-moment assumption. However, we often observe that log-returns have heavier tail distribution than the finite fourth-moment and that the degrees of heaviness of tails are heterogeneous over the asset and time period. In this paper, to deal with the heterogeneous heavy-tailed distributions, we develop an adaptive robust integrated volatility estimator that employs pre-averaging and truncation schemes based on jump-diffusion processes. We call this an adaptive robust pre-averaging realized volatility (ARP) estimator. We show that the ARP estimator has a sub-Weibull tail concentration with only finite 2$\alpha$-th moments for any $\alpha>1$. In addition, we establish matching upper and lower bounds to show that the ARP estimation procedure is optimal. To estimate large integrated volatility matrices using the approximate factor model, the ARP estimator is further regularized using the principal orthogonal complement thresholding (POET) method. The numerical study is conducted to check the finite sample performance of the ARP estimator.

Context. Turbulent transport in stellar radiative zones is a key ingredient of stellar evolution theory, but the anisotropy of the transport due to the stable stratification and the rotation of these regions is poorly understood. The assumption of shellular rotation, which is a cornerstone of the so-called rotational mixing, relies on an efficient horizontal transport. However, this transport is included in many stellar evolution codes through phenomenological models that have never been tested. Aims. We investigate the impact of horizontal shear on the anisotropy of turbulent transport. Methods. We used a relaxation approximation (also known as {\tau} approximation) to describe the anisotropising effect of stratification, rotation, and shear on a background turbulent flow by computing velocity correlations. Results. We obtain new theoretical scalings for velocity correlations that include the effect of horizontal shear. These scalings show an enhancement of turbulent motions, which would lead to a more efficient transport of chemicals and angular momentum, in better agreement with helio- and asteroseismic observations of rotation in the whole Hertzsprung-Russell diagram. Moreover, we propose a new choice for the non-linear time used in the relaxation approximation, which characterises the source of the turbulence. Conclusions. For the first time, we describe the effect of stratification, rotation, and vertical and horizontal shear on the anisotropy of turbulent transport in stellar radiative zones. The new prescriptions need to be implemented in stellar evolution calculations. To do so, it may be necessary to implement non-diffusive transport.

Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well. An increasingly popular reactive policy architecture in robotics is Behavior Trees but as with other architectures, programming time still drives cost and limits flexibility. There are two main branches of algorithms to generate policies automatically, automated planning and machine learning, both with their own drawbacks. We propose a method for generating Behavior Trees using a Genetic Programming algorithm and combining the two branches by taking the result of an automated planner and inserting it into the population. Experimental results confirm that the proposed method of combining planning and learning performs well on a variety of robotic assembly problems and outperforms both of the base methods used separately. We also show that this type of high level learning of Behavior Trees can be transferred to a real system without further training.

Kernel segmentation aims at partitioning a data sequence into several non-overlapping segments that may have nonlinear and complex structures. In general, it is formulated as a discrete optimization problem with combinatorial constraints. A popular algorithm for optimally solving this problem is dynamic programming (DP), which has quadratic computation and memory requirements. Given that sequences in practice are too long, this algorithm is not a practical approach. Although many heuristic algorithms have been proposed to approximate the optimal segmentation, they have no guarantee on the quality of their solutions. In this paper, we take a differentiable approach to alleviate the aforementioned issues. First, we introduce a novel sigmoid-based regularization to smoothly approximate the combinatorial constraints. Combining it with objective of the balanced kernel clustering, we formulate a differentiable model termed Kernel clustering with sigmoid-based regularization (KCSR), where the gradient-based algorithm can be exploited to obtain the optimal segmentation. Second, we develop a stochastic variant of the proposed model. By using the stochastic gradient descent algorithm, which has much lower time and space complexities, for optimization, the second model can perform segmentation on overlong data sequences. Finally, for simultaneously segmenting multiple data sequences, we slightly modify the sigmoid-based regularization to further introduce an extended variant of the proposed model. Through extensive experiments on various types of data sequences performances of our models are evaluated and compared with those of the existing methods. The experimental results validate advantages of the proposed models. Our Matlab source code is available on github.

Phishing is the number one threat in the world of internet. Phishing attacks are from decades and with each passing year it is becoming a major problem for internet users as attackers are coming with unique and creative ideas to breach the security. In this paper, different types of phishing and anti-phishing techniques are presented. For this purpose, the Systematic Literature Review(SLR) approach is followed to critically define the proposed research questions. At first 80 articles were extracted from different repositories. These articles were then filtered out using Tollgate Approach to find out different types of phishing and anti-phishing techniques. Research study evaluated that spear phishing, Email Spoofing, Email Manipulation and phone phishing are the most commonly used phishing techniques. On the other hand, according to the SLR, machine learning approaches have the highest accuracy of preventing and detecting phishing attacks among all other anti-phishing approaches.

This paper proposes a model-free nonparametric estimator of conditional quantile of a time series regression model where the covariate vector is repeated many times for different values of the response. This type of data is abound in climate studies. To tackle such problems, our proposed method exploits the replicated nature of the data and improves on restrictive linear model structure of conventional quantile regression. Relevant asymptotic theory for the nonparametric estimators of the mean and variance function of the model are derived under a very general framework. We provide a detailed simulation study which clearly demonstrates the gain in efficiency of the proposed method over other benchmark models, especially when the true data generating process entails nonlinear mean function and heteroskedastic pattern with time dependent covariates. The predictive accuracy of the non-parametric method is remarkably high compared to other methods when attention is on the higher quantiles of the variable of interest. Usefulness of the proposed method is then illustrated with two climatological applications, one with a well-known tropical cyclone wind-speed data and the other with an air pollution data.

Polarimetric imaging is one of the most effective techniques for high-contrast imaging and characterization of circumstellar environments. These environments can be characterized through direct-imaging polarimetry at near-infrared wavelengths. The SPHERE/IRDIS instrument installed on the Very Large Telescope in its dual-beam polarimetric imaging (DPI) mode, offers the capability to acquire polarimetric images at high contrast and high angular resolution. However dedicated image processing is needed to get rid of the contamination by the stellar light, of instrumental polarization effects, and of the blurring by the instrumental point spread function. We aim to reconstruct and deconvolve the near-infrared polarization signal from circumstellar environments. We use observations of these environments obtained with the high-contrast imaging infrared polarimeter SPHERE-IRDIS at the VLT. We developed a new method to extract the polarimetric signal using an inverse approach method that benefits from the added knowledge of the detected signal formation process. The method includes weighted data fidelity term, smooth penalization, and takes into account instrumental polarization. The method enables to accurately measure the polarized intensity and angle of linear polarization of circumstellar disks by taking into account the noise statistics and the convolution of the observed objects by the instrumental point spread function. It has the capability to use incomplete polarimetry cycles which enhance the sensitivity of the observations. The method improves the overall performances in particular for low SNR/small polarized flux compared to standard methods.

Traditional and deep learning-based fusion methods generated the intermediate decision map to obtain the fusion image through a series of post-processing procedures. However, the fusion results generated by these methods are easy to lose some source image details or results in artifacts. Inspired by the image reconstruction techniques based on deep learning, we propose a multi-focus image fusion network framework without any post-processing to solve these problems in the end-to-end and supervised learning way. To sufficiently train the fusion model, we have generated a large-scale multi-focus image dataset with ground-truth fusion images. What's more, to obtain a more informative fusion image, we further designed a novel fusion strategy based on unity fusion attention, which is composed of a channel attention module and a spatial attention module. Specifically, the proposed fusion approach mainly comprises three key components: feature extraction, feature fusion and image reconstruction. We firstly utilize seven convolutional blocks to extract the image features from source images. Then, the extracted convolutional features are fused by the proposed fusion strategy in the feature fusion layer. Finally, the fused image features are reconstructed by four convolutional blocks. Experimental results demonstrate that the proposed approach for multi-focus image fusion achieves remarkable fusion performance compared to 19 state-of-the-art fusion methods.

In this work, the aim is to study the spread of a contagious disease and information on a multilayer social system. The main idea is to find a criterion under which the adoption of the spreading information blocks or suppresses the epidemic spread. A two-layer network is the base of the model. The first layer describes the direct contact interactions, while the second layer is the information propagation layer. Both layers consist of the same nodes. The society consists of five different categories of individuals: susceptibles, infective, recovered, vaccinated and precautioned. Initially, only one infected individual starts transmitting the infection. Direct contact interactions spread the infection to the susceptibles. The information spreads through the second layer. The SIR model is employed for the infection spread, while the Bass equation models the adoption of information. The control parameters of the competition between the spread of information and spread of disease are the topology and the density of connectivity. The topology of the information layer is a scale-free network with increasing density of edges. In the contact layer, regular and scale-free networks with the same average degree per node are used interchangeably. The observation is that increasing complexity of the contact network reduces the role of individual awareness. If the contact layer consists of networks with limited range connections, or the edges sparser than the information network, spread of information plays a significant role in controlling the epidemics.

This paper first formalises a new observability concept, called weak regular observability, that is adapted to Fast Moving Horizon Estimation where one aims to estimate the state of a nonlinear system efficiently on rolling time windows in the case of small initial error. Additionally, sufficient conditions of weak regular observability are provided in a problem of Simultaneous Localisation and Mapping (SLAM) for different measurement models. In particular it is shown that following circular trajectories leads to weak regular observability in a second order 2D SLAM problem with several possible types of sensors.

The dark matter halo surface density, given by the product of the dark matter core radius ($r_c$) and core density ($\rho_c$) has been shown to be a constant for a wide range of isolated galaxy systems. Here, we carry out a test of this {\em ansatz} using a sample of 17 relaxed galaxy groups observed using Chandra and XMM-Newton, as an extension of our previous analysis with galaxy clusters. We find that $\rho_c \propto r_c^{-1.35^{+0.16}_{-0.17}}$, with an intrinsic scatter of about 27.3%, which is about 1.5 times larger than that seen for galaxy clusters. Our results thereby indicate that the surface density is discrepant with respect to scale invariance by about 2$\sigma$, and its value is about four times greater than that for galaxies. Therefore, the elevated values of the halo surface density for groups and clusters indicate that the surface density cannot be a universal constant for all dark matter dominated systems. Furthermore, we also implement a test of the radial acceleration relation for this group sample. We find that the residual scatter in the radial acceleration relation is about 0.32 dex and a factor of three larger than that obtained using galaxy clusters. The acceleration scale which we obtain is in-between that seen for galaxies and clusters.

The nature of unconventional superconductivity is intimately linked to the microscopic nature of the pairing interactions. In this work, motivated by cubic heavy fermion compounds with embedded multipolar moments, we theoretically investigate superconducting instabilities instigated by multipolar Kondo interactions. Employing multipolar fluctuations (mediated by RKKY interaction) coupled to conduction electrons via two-channel Kondo and novel multipolar Kondo interactions, we uncover a variety of superconducting states characterized by higher-angular momentum Cooper pairs, $J=0,1,2,3$. We demonstrate that both odd and even parity pairing functions are possible, regardless of the total angular momentum of the Cooper pairs, which can be traced back to the atypical nature of the multipolar Kondo interaction that intertwines conduction electron spin and orbital degrees of freedom. We determine that different (point-group) irrep classified pairing functions may coexist with each other, with some of them characterized by gapped and point node structures in their corresponding quasiparticle spectra. This work lays the foundation for discovery and classification of superconducting states in rare-earth metallic compounds with multipolar local moments.

Satellites, are both crucial and, despite common misbelieve, very fragile parts our civilian and military critical infrastructure. While, many efforts are focused on securing ground and space segments, especially when national security or large businesses interests are affected, the small-sat, newspace revolution democratizes access to, and exploitation of the near earth orbits. This brings new players to the market, typically in the form of small to medium sized companies, offering new or more affordable services. Despite the necessity and inevitability of this process, it also opens potential new venues for targeted attacks against space-related infrastructure. Since sources of satellite ephemerides are very often centralized, they are subject to classical Man-in-the-Middle attacks which open venues for TLE spoofing attack, which may result in unnecessary collision avoidance maneuvers, in best case and orchestrated crashes, in worst case. In this work, we propose a countermeasure to the presented problem that include distributed solution, which will have no central authority responsible for storing and disseminating TLE information. Instead, each of the peers participating to the system, have full access to all of the records stored in the system, and distribute the data in a consensual manner,ensuring information replication at each peer node. This way, single point of failure syndromes of classic systems, which currently exist due to the direct ephemerids distribution mechanism, are removed. Our proposed solution is to build data dissemination systems using permissioned, private ledgers where peers have strong and verifiable identities, which allow also for redundancy in SST data sourcing.

Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.

This paper presents an analytical study for the metric properties of the paraboloidal double projection, i.e. central and orthogonal projections used in the catadioptric camera system. Metric properties have not sufficiently studied in previous treatments of such system. These properties incorporate the determination of the true lengths of projected lines and areas bounded by projected lines. The advantageous main gain of determining metric elements of the paraboloidal double projection is studying distortion analysis and camera calibration, which is considered an essential tool in testing camera accuracy. Also, this may be considered as a significant utility in studying comparison analysis between different cameras projection systems.

Anti-ferromagnetic materials have the possibility to offer ultra fast, high data density spintronic devices. A significant challenge is the reliable detection of the state of the antiferromagnet, which can be achieved using exchange bias. Here we develop an atomistic spin model of the athermal training effect, a well known phenomenon in exchange biased systems where the bias is significantly reduced after the first hysteresis cycle. We find that the setting process in granular thin films relies on the presence of interfacial mixing between the ferromagnetic and antiferromagnetic layers. We systematically investigate the effect of the intermixing and find that the exchange bias, switching field and coercivity all increase with increased intermixing. The interfacial spin state is highly frustrated leading to a systematic decrease in interfacial ordering of the ferromagnet. This metastable spin structure of initially irreversible spins leads to a large effective exchange coupling and thus large increase in the switching field. After the first hysteresis cycle these metastable spins drop into a reversible ground state that is repeatable for all subsequent hysteresis cycles, demonstrating that the effect is truly athermal. Our simulations provide new insights into the role of interface mixing and the importance of metastable spin structures in exchange biased systems which could help with the design an optimisation of antiferromagnetic spintronic devices.