Miracle-dz/UniFAR-FTUs huggingface dataset:
The Hot Neptune WASP-166 b with ESPRESSO I: Refining the planetary architecture and stellar variability

In this paper, we present high-resolution spectroscopic transit observations from ESPRESSO of the super-Neptune WASP-166 b. In addition to spectroscopic ESPRESSO data, we analyse photometric data from TESS of six WASP-166 b transits along with simultaneous NGTS observations of the ESPRESSO runs. These observations were used to fit for the planetary parameters as well as assessing the level of stellar activity (e.g. spot crossings, flares) present during the ESPRESSO observations. We utilize the Reloaded Rossiter McLaughlin (RRM) technique to spatially resolve the stellar surface, characterizing the centre-to-limb convection-induced variations, and to refine the star-planet obliquity. We find WASP-166 b has a projected obliquity of $\lambda = -15.52^{+2.85}_{-2.76}\, ^{\circ }$ and vsin (i) = 4.97 ± 0.09 kms−1 which is consistent with the literature. We were able to characterise centre-to-limb convective variations as a result of granulation on the surface of the star on the order of a few kms−1 for the first time. We modelled the centre-to-limb convective variations using a linear, quadratic and cubic model with the cubic being preferred. In addition, by modelling the differential rotation and centre-to-limb convective variations simultaneously we were able to retrieve a potential anti-solar differential rotational shear (α ∼ -0.5) and stellar inclination (i* either 42.03$^{+9.13}_{-9.60}\, ^{\circ }$ or 133.64$^{+8.42}_{-7.98}\, ^{\circ }$ if the star is pointing towards or away from us). Finally, we investigate how the shape of the cross-correlation functions change as a function of limb angle and compare our results to magnetohydrodynamic simulations.
Revisiting Hotels-50K and Hotel-ID

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions provide evaluation setups with different levels of difﬁculty to better align with the intended real-world application, i.e. countering human trafﬁcking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.
A Dynamical Systems Algorithm for Clustering in Hyperspectral Imagery

In this paper we present a new dynamical systems algorithm for clustering in hyperspectral images. The main idea of the algorithm is that data points are \`pushed\' in the direction of increasing density and groups of pixels that end up in the same dense regions belong to the same class. This is essentially a numerical solution of the differential equation defined by the gradient of the density of data points on the data manifold. The number of classes is automated and the resulting clustering can be extremely accurate. In addition to providing a accurate clustering, this algorithm presents a new tool for understanding hyperspectral data in high dimensions. We evaluate the algorithm on the Urban (Available at www.tec.ary.mil/Hypercube/) scene comparing performance against the k-means algorithm using pre-identified classes of materials as ground truth.
Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network

For market-based procurement of low voltage (LV) ﬂexibility, DSOs identify the amount of ﬂexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avoid over or under procurement of ﬂexibility in the presence of uncertainty. To this end, we propose a scenario-based robust chance-constrained (CC) day-ahead ﬂexibility needs assessment (FNA) framework. The CC level is analogous to the risk DSO is willing to take in ﬂexibility planning. Multi-period optimal power ﬂow is performed to calculate the amount of ﬂexibility needed to avoid network issues. Flexibility is deﬁned in terms of nodal power ramp-up and ramp-down and cumulative energy needs over a full day for each node. Future uncertainties are considered as multiple scenarios generated using multivariate Gaussian distribution and Cholesky decomposition. These scenarios are utilized to solve the ﬂexibility needs assessment optimal power ﬂow (FNA-OPF) problem. Zonal clustering of an LV feeder is performed using electrical distance as a measure and spatial partitioning. The FNA tool calculates ramp-up and ramp-down ﬂexibility’s power and energy requirements. Energy and power needs are often valued differently in many energy markets. We identify the marginal value of ﬂexibility associated with energy and power needs separately. From numerical results for an LV feeder, it is observed that zonal ﬂexibility needs assessment is more immune to uncertainty than nodal ﬂexibility needs, making it more useful for DSOs to evaluate day-ahead ﬂexibility procurement. We also propose a Pareto optimal mechanism for selecting CC level to reduce ﬂexibility needs while reducing DN congestion.
Design and evaluation of a transverse gradient set for magnetic resonance imaging of the human brain

A genetic algorithm has been used to design a 35 cm diameter transverse gradient set for human brain imaging. Despite strict constraints on the maximum coil length (33 cm), gradient performance comparable to that from far longer sets of conventional geometry is achieved; thus the gradient efficiency is with a 24% standard deviation from this within a 26 cm sphere. Phase maps obtained using a gradient-acquisition for shimming sequence were used to test the linearity of the gradients without requiring a complex phantom.
Far infrared spectra of magnetic ions in crystals

The absorption spectra of magnetic ions in crystalline solids, observed in the spectral range between approximately 5 and 200 cm−1, are surveyed. The electronic excitations of single ions are considered first. The measurements of line positions, intensities and widths are discussed in terms of both static and dynamic crystal electric field models. The second half of the review deals with the excitation of coupled ions both in dilute and concentrated materials. The spectra observed are discussed in terms of current models of the various interactions which can occur. In both areas there are obvious gaps between the experimental data and their theoretical interpretation. For pairs of interacting ions the theory is well advanced but as yet there is little data available for interpretation; while for many effects in single ion spectra the difficulties inherent in the theory limit our understanding. Progress towards the elimination of these problems is likely to aid our understanding of the behaviour of ions in crystals.
Neutrino masses and mixing in Minimal Inverse Seesaw using A 4 modular symmetry

In this paper, we construct a model with the help of modular symmetry in the framework of minimal inverse seesaw [ISS(2,3)]. We have used Γ(3) modular group which is isomorphic to non-Abelian discrete symmetry group A 4 . In this group there are three Yukawa modular forms of weight 2. Through this model, we study neutrino masses and mixing for both normal and inverted hierarchy. Use of modular symmetry reduces the need for extra ﬂavons and their speciﬁc VEV alignments, as such, minimality of the model is maintained to a great extent. Along with A 4 symmetry group, we have used Z 3 to restrict certain interaction terms in the Lagrangian. Further we calculate the eﬀective mass to address the phenomena of neutrinoless double-beta decay ( 0 νββ ). The values of eﬀective mass is found to lie within the bound ( m eff < 0 . 165 eV) as predicted by diﬀerent 0 νββ experiments.
A Ransomware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework

Solutions for rapid prioritization of different ransomware have been raised to formulate fast response plans to minimize socioeco-nomic damage from the massive growth of ransomware attacks in recent years. To address this concern, we propose a ransomware triage approach that can rapidly classify and prioritize different ransomware classes. Our Siamese Neural Network (SNN) based approach utilizes a pre-trained ResNet18 network in a meta-learning fashion to reduce the biases in weight and parameter calculations typically associated with a machine learning model trained with a limited number of training samples. Instead of image features typically used as inputs to many existing machine learning-based triage applications, our approach uses the entropy features directly obtained from the ransomware binary ﬁles to improve feature representation, resilient to obfuscation noise, and computationally less expensive. Our triage approach can classify ransomware samples into the correct classes if the ransomware features signif-icantly match known ransomware proﬁles. Our evaluation shows that this classiﬁcation part of our proposed approach achieves the accuracy exceeding 88% and outperforms other similar classiﬁcation only machine learning-based approaches. In addition, we offer a new triage strategy based on the normalized and regularized weight ratios that evaluate the level of similarity matching across ransomware classes to identify any risky and unknown ransomware (e.g., zero-day attacks) so that a rapid further analysis can be conducted.
Hausdorff closed limits and the c-boundary I: a new topology for the c-completion of spacetimes

This is the first of two papers (see also Costa e Silva et al (2019)) in which we argue for the benefits of considering the closed limit topology (CLT) on the Geroch–Kronheimer–Penrose causal completion of globally hyperbolic spacetimes with (possibly empty) smooth timelike boundary . We show here that this topology, introduced by Hausdorff himself (see Hausdorff, is not only metrizable, but retains essentially all the good properties of certain topologies previously defined in this ambient. In particular, we discuss the relationship of the CLT with the more generally applicable (but not always Hausdorff) chronological topology, and show that they coincide exactly in those cases when the latter happens to be Hausdorff.
Room geometry blind inference based on the localization of real sound source and first order reflections

—The conventional room geometry blind inference techniques with acoustic signals are conducted based on the prior knowledge of the environment, such as the room impulse response (RIR) or the sound source position, which will limit its application under unknown scenarios. To solve this problem, we have proposed a room geometry reconstruction method in this paper by using the geometric relation between the direct signal and ﬁrst-order reﬂections. In addition to the information of the compact microphone array itself, this method does not need any precognition of the environmental parameters. Besides, the learning-based DNN models are designed and used to improve the accuracy and integrity of the localization results of the direct source and ﬁrst-order reﬂections. The direction of arrival (DOA) and time di ﬀ erence of arrival (TDOA) information of the direct and reﬂected signals are ﬁrstly estimated using the proposed DCNN and TD-CNN models, which have higher sensitivity and accuracy than the conventional methods. Then the position of the sound source is inferred by integrating the DOA, TDOA and array height using the proposed DNN model. After that, the positions of image sources and corresponding boundaries are derived based on the geometric relation. Experimental results of both simulations and real measurements verify the e ﬀ ectiveness and accuracy of the proposed techniques compared with the conventional methods under di ﬀ erent reverberant environments.
Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes

In this paper, we investigate the opportunities of automating the judgment process in online one-on-one math classes. We build a Wide&Deep framework to learn fine-grained predictive representations from a limited amount of noisy classroom conversation data that perform better student judgments. We conducted experiments on the task of predicting students' levels of mastery of example questions and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.
Electrostatic scanning force microscopy images of long molecules: single-walled carbon nanotubes and DNA

Experimental measurements of the electrostatic properties of long molecules are presented in this work. It is shown that, in the case of carbon nanotubes, the electrostatic force between the scanning force microscopy tip and the sample enhances the network of nanotubes electrically connected to a metal electrode. On λ-DNA molecules the same kind of experiments result in essentially no measurable electrostatic force due to the molecules. From this we conclude that λ-DNA has a negligible electronic conductivity.
Topology in Linear Mechanical Metamaterials

This thesis deals with topological effects in linear mechanical metamaterials governed by Newton’s equation of motion. Mechanical metamaterials are artificially designed structures whose behavior arises from a scale much larger than its constituting units. The topological effects taken into account are the ones known from single-particle condensed matter physics. While first introduced in the context of quantum mechanics, they are not bound to it and appear in other contexts as well. As such, the field of topological mechanical metamaterials is the youngest offspring implementing ideas from topological band theory and beyond. This thesis is part of this development and its contributions are fourfold. The first contribution is a systematic approach to import topological effects from single-particle condensed matter physics to mechanical metamaterials. We show how to bring Newton’s equation of motion into a form akin the Schrödinger equation. This then allows for a direct import of the desired physics. Besides, through this transformation we combine previously unconnected approaches to carry over topological effects, and set out how further ones transfer. A central feature of topological materials is the presence of robust surface modes that are of interest in sight of applications. However, some types of topological effects can only be implemented if classical timereversal symmetry is broken, which in turn can be hard to achieve for versatile and affordable mechanical metamaterial. We theoretically and experimentally demonstrate how a passive, time-reversal-invariant topological material can be built, by implementing the topological aspects of the quantum spin Hall effect in a mechanical metamaterial. We experimentally characterize the edge (surface) modes and discuss their topological protection. The quantum spin Hall effect is protected by quantum time-reversal symmetry, which translates into a local symmetry in its mechanical version. By deliberately breaking this symmetry, we show how the topological edge channels can be switched on and off. Furthermore, we experimentally show that it is sufficient to break the symmetry in a very small spatial region of the system to obtain an almost perfect switching behavior. Taking into account additional energy terms renders topological invariants often ill-defined. Nevertheless, they offer a structured approach to engineer peculiar surface physics. We create such a metamaterial with a polar elastic response. When straining the material with a point-like object on a certain surface, it provides a very soft response, whereas when poked on the opposite surface the material is hard. The effect is robust, and in particular not prone to wearing.
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness

Defending deep neural networks against adversarial examples is a key challenge for AI safety. To improve the robustness effectively, recent methods focus on important data points near the decision boundary in adversarial training. However, these methods are vulnerable to Auto-Attack, which is an ensemble of parameter-free attacks for reliable evaluation. In this paper, we experimentally investigate the causes of their vulnerability and ﬁnd that existing methods reduce margins between logits for the true label and the other labels while keeping their gradient norms non-small values. Reduced margins and non-small gradient norms cause their vulnerability since the largest logit can be easily ﬂipped by the perturbation. Our experiments also show that the histogram of the logit margins has two peaks, i.e., small and large logit margins. From the observations, we propose switching one-versus-the-rest loss (SOVR), which uses one-versus-the-rest loss when data have small logit margins so that it increases the margins. We ﬁnd that SOVR increases logit margins more than existing methods while keeping gradient norms small and outperforms them in terms of the robustness against Auto-Attack. roughly From these observations, we propose one-versus-the-rest (SOVR), which switches losses to increase the logit margins when the data point has small margin. We show that one-versus-the-rest loss (OVR) is always greater than or equal to cross-entropy on any logits and expect that OVR penalizes small margins strongly.
Incentive Designs for Stackelberg Games with a Large Number of Followers and their Mean-Field Limits

We study incentive designs for a class of stochastic Stackelberg games with one leader and a large number of (finite as well as infinite population of) followers. We investigate whether the leader can craft a strategy under a dynamic information structure that induces a desired behavior among the followers. For the finite population setting, under convexity of the leader's cost and other sufficient conditions, we show that there exist symmetric \emph{incentive} strategies for the leader that attain approximately optimal performance from the leader's viewpoint and lead to an approximate symmetric (pure) Nash best response among the followers. Leveraging functional analytic tools, we further show that there exists a symmetric incentive strategy, which is affine in the dynamic part of the leader's information, comprising partial information on the actions taken by the followers. Driving the follower population to infinity, we arrive at the interesting result that in this infinite-population regime the leader cannot design a smooth ``finite-energy'' incentive strategy, namely, a mean-field limit for such games is not well-defined. As a way around this, we introduce a class of stochastic Stackelberg games with a leader, a major follower, and a finite or infinite population of minor followers. For this class of problems, we establish the existence of an incentive strategy and the corresponding mean-field Stackelberg game. Examples of quadratic Gaussian games are provided to illustrate both positive and negative results. In addition, as a byproduct of our analysis, we establish the existence of a randomized incentive strategy for the class mean-field Stackelberg games, which in turn provides an approximation for an incentive strategy of the corresponding finite population Stackelberg game.
First principles investigations in the carbon–silicon system of novel tetragonal C 8 and Si 8 allotropes, and binary Si 2 C 6 and Si 4 C 4 phases.

Within the carbon–silicon system, novel tetragonal C 8 and Si 8 allotropes and two silicon carbides Si 2 C 6 and Si 4 C 4 are devised. The propositions are based on density functional theory (DFT) calculations of template structures, optimized to ground state energies and subsequently derived physical properties. All four phases belong to primitive tetragonal space group P m 2 N°115 characterized by large c/a tetragonality ratio. The structures consist of corner sharing C4 and Si4 tetrahedra highlighting covalent (in C 8 ) and polar covalent (in silicon carbides) chemical systems illustrated with charge density projections. C 8 is identified as ultra-hard with a Vickers hardness H V amounting to 113 GPa, a result assigned to the large tetragonality ratio. Oppositely, Si 8 allotrope is found soft with H V =13 GPa alike cubic Si, and Si 4 C 4 is identified with H V =33 GPa alike experimental SiC. Larger C-content Si 2 C 6 is identified as super-hard with H V =51 GPa. All new phases are mechanically (elastic constants with bulk and shear moduli) and dynamically (phonon band structures) stable. The electronic band structures are characteristic of insulating C 8 with large band gaps of about 5 eV like diamond, and semi-conducting Si 2 C 6 , Si 4 C 4 , and Si 8 with band gaps of ~1 eV. The results are claimed as enriching further the Si-C system with novel materials aimed at diverse electronic and mechanic applications.
UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

In this article, we consider multiple solar-powered wireless nodes (WNs) which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node user association, and uplink power control to effectively utilize the harvested energy and manage co-channel interference within a finite time horizon. The design goal is to ensure the fairness of WNs by maximizing the worst user rate. The joint design problem is highly nonconvex and requires causal (future) knowledge of the instantaneous energy state information (ESI) and channel state information (CSI), which are difficult to predict in reality. To overcome these challenges, we propose an offline method based on convex optimization that only utilizes the average ESI and CSI, where line-of-sight (LOS) and non-LOS (NLOS) channels are considered. The problem is solved by three convex subproblems with successive convex approximation (SCA) and alternative optimization. We further design an online convex-assisted reinforcement learning (CARL) method based on real-time environmental information. An idea of multi-UAV regulated flight corridors, based on the optimal offline UAV trajectories, is proposed to avoid unnecessary flight exploration by UAVs and enables us to improve the learning efficiency and system performance, as compared with the conventional reinforcement learning (RL) method. Computer simulations are used to verify the effectiveness of the proposed methods. The proposed CARL method provides 25% and 12% improvement on the worst user rate over the offline and conventional RL methods.
Tackling the infamous $g^6$ term of the QCD pressure

We report on ongoing efforts to tackle an important open problem in QCD thermodynamics, namely an evaluation of the pressure to order $g^6$ in a weak-coupling expansion, corresponding to four loops. In particular, we identify a class of contributing Feynman sum-integrals with lower-loop factors, describe the formalism to tensor decompose those, and manage to map them onto scalar master sum-integrals that have already been evaluated in the literature.