Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressive Sensing Based Joint Activity and Data Detection for Grant-Free Massive Access

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vector (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency massive access.
Cinematic-L1 Video Stabilization with a Log-Homography Model

We present a method for stabilizing handheld video that simulates the camera motions cinematographers achieve with equipment like tripods, dollies, and Steadicams. We formulate a constrained convex optimization problem minimizing the ℓ1-norm of the first three derivatives of the stabilized motion. Our approach extends the work of Grundmann et al. [9] by solving with full homographies (rather than affinities) in order to correct perspective, preserving linearity by working in log-homography space. We also construct crop constraints that preserve field-of-view; model the problem as a quadratic (rather than linear) program to allow for an ℓ2 term encouraging fidelity to the original trajectory; and add constraints and objectives to reduce distortion. Furthermore, we propose new methods for handling salient objects via both inclusion constraints and centering objectives. Finally, we describe a windowing strategy to approximate the solution in linear time and bounded memory. Our method is computationally efficient, running at 300fps on an iPhone XS, and yields high-quality results, as we demonstrate with a collection of stabilized videos, quantitative and qualitative comparisons to [9] and other methods, and an ablation study.
Creation of a novel inverted charge density wave state

Charge density wave (CDW) order is an emergent quantum phase that is characterized by periodic lattice distortion and charge density modulation, often present near superconducting transitions. Here, we uncover a novel inverted CDW state by using a femtosecond laser to coherently reverse the star-of-David lattice distortion in 1T-TaSe2. We track the signature of this novel CDW state using time- and angle-resolved photoemission spectroscopy and the time-dependent density functional theory to validate that it is associated with a unique lattice and charge arrangement never before realized. The dynamic electronic structure further reveals its novel properties that are characterized by an increased density of states near the Fermi level, high metallicity, and altered electron–phonon couplings. Our results demonstrate how ultrafast lasers can be used to create unique states in materials by manipulating charge-lattice orders and couplings.
A Model of Polarization on Social Media Caused by Empathy and Repulsion

In recent years, the ease with which social media can be accessed has led to the unexpected problem of a shrinkage in information sources. This phenomenon is caused by a system that facilitates the connection of people with similar ideas and recommendation systems. Bias in the selection of information sources promotes polarization that divides people into multiple groups with opposing views and creates conflicts between opposing groups. This paper elucidates the mechanism of polarization by proposing a model of opinion formation in social media that considers users' reactions of empathy and repulsion. Based on the idea that opinion neutrality is only relative, this model offers a novel technology for dealing with polarization.
LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

Online action detection is a task with the aim of identifying ongoing actions from streaming videos without any side information or access to future frames. Recent methods proposed to aggregate fixed temporal ranges of invisible but anticipated future frames representations as supplementary features and achieved promising performance. They are based on the observation that human beings often detect ongoing actions by contemplating the future vision simultaneously. However, we observed that at different action progressions, the optimal supplementary features should be obtained from distinct temporal ranges instead of simply fixed future temporal ranges. To this end, we introduce an adaptive features sampling strategy to overcome the mentioned variable-ranges of optimal supplementary features. Specifically, in this paper, we propose a novel Learning Action Progression Network termed LAP-Net, which integrates an adaptive features sampling strategy. At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features. We evaluated our LAP-Net on three benchmark datasets, TVSeries, THUMOS-14 and HDD. The extensive experiments demonstrate that with our adaptive feature sampling strategy, the proposed LAP-Net can significantly outperform current state-of-the-art methods with a large margin.
The Person Index Challenge: Extraction of Persons from Messy, Short Texts

When persons are mentioned in texts with their first name, last name and/or middle names, there can be a high variation which of their names are used, how their names are ordered and if their names are abbreviated. If multiple persons are mentioned consecutively in very different ways, especially short texts can be perceived as "messy". Once ambiguous names occur, associations to persons may not be inferred correctly. Despite these eventualities, in this paper we ask how well an unsupervised algorithm can build a person index from short texts. We define a person index as a structured table that distinctly catalogs individuals by their names. First, we give a formal definition of the problem and describe a procedure to generate ground truth data for future evaluations. To give a first solution to this challenge, a baseline approach is implemented. By using our proposed evaluation strategy, we test the performance of the baseline and suggest further improvements. For future research the source code is publicly available.
Analyzing Current Interference Situations of Connected Devices Using Context-Information and the Centralized Broker-Approach

The digitalization of manufacturing processes is leading to a highly increased amount of connected devices. In the course of this development a process was developed and implemented, which optimizes IEEE 802.11-systems relating to the interference situation by using context-information. This was realized by division into two fields. First of all, data providers calculate their own interference situation to determine the optimal frequency out of the ascertained data. In this case optimal means that the chosen channel should have the lowest interference power. In a second step these providers also calculating the interference information about all Service Set Identifier (SSID) in range. The measured data is then divided into the allowed radio channel. Afterwards the gathered information will be transferred to a central space. An already existing infrastructure is used as a initial point for the further process. As soon as the provider has transferred new data, a broker can inform connected users that there is an update in the inference situation. With this developed architecture an approach to face the hidden node problem is given. Additional a system for safety-critical information is implemented.
Tissue characterization based on the analysis on i3DUS data for diagnosis support in neurosurgery

Brain shift makes the pre-operative MRI navigation highly inaccurate hence the intraoperative modalities are adopted in surgical theatre. Due to the excellent economic and portability merits, the Ultrasound imaging is used at our collaborating hospital, Charing Cross Hospital, Imperial College London, UK. However, it is found that intraoperative diagnosis on Ultrasound images is not straightforward and consistent, even for very experienced clinical experts. Hence, there is a demand to design a Computer-aided-diagnosis system to provide a robust second opinion to help the surgeons. The proposed CAD system based on "Mixed-Attention Res-U-net with asymmetric loss function" achieves the state-of-the-art results comparing to the ground truth by classification at pixel-level directly, it also outperforms all the current main stream pixel-level classification methods (e.g. U-net, FCN) in all the evaluation metrices.
Hybrid Repair Combined with Fresh Arterial Allograft Extra-Anatomical Reconstruction: The Treatment of Infrarenal Abdominal Aneurysm above an Aortobifemoral Bypass Complicated by an Infected Pseudoaneurysm in the Left Groin

Introduction A 72-year-old male patient was admitted into our centre with large infected pseudoaneurysm (PSA) in the left groin. The patient underwent a CT angiography (CTA) that confirmed a large partly thrombosed 6.5 × 5.5 cm PSA in the left groin arising from the distal anastomosis of the aortobifemoral bypass (ABF). Furthermore, the CTA revealed 11 cm juxtarenal abdominal aortic aneurysm (JAAA) from which the proximal anastomosis of the ABF was arising. Method Aorto-uni-iliac stent graft Cook was placed from the right groin trough native severely stenotic right iliac arteries with proximal landing zone below the renal arteries, excluding the JAAA and the ABF. The distal landing zone was in the common iliac artery maintaining patent right internal iliac artery. Afterwards, a femoro-femoral crossover bypass from right to left was performed using a fresh arterial allograft. Postprocedurally, the hospital stay was uneventful. The left groin PSA cultures came positive for Staphylococcus epidermidis and Corynebacterium tuberculostearicum, both sensitive to vancomycin and rifampicin. Result The patient underwent intravenous ATB treatment with vancomycin for two weeks, followed by four weeks of oral rifampicin. The patient was discharged on the 20th postoperative days. Conclusion Hybrid repair combining aortic stent graft and extra-anatomical bypass in the treatment of infected distal parts of an aortofemoral bypass is an acceptable treatment modality.
Associations between Interleukin-32 Gene Polymorphisms rs12934561 and rs28372698 and Susceptibilities to Bladder Cancer and the Prognosis in Chinese Han Population

The proinflammatory chemokine interleukin-32 is related to various diseases, including cancer. However, it has never been associated with bladder cancer (BC). To detect whether there is a relationship between the IL-32 gene polymorphisms (rs12934561 C/T and rs28372698 T/A) and BC, the study enrolled 170 non-muscle-invasive bladder cancer (NMIBC) patients, 151 muscle-invasive bladder cancer (MIBC) patients, and 437 healthy controls. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used for the IL-32 single-nucleotide polymorphism (SNP) genotyping. Statistical analysis was performed using SNPstats online analysis software and SPSS software. Our data revealed that the CC homozygous genotype of rs12934561 in BC patients was significantly higher than that in controls (P = 0.03, OR = 1.47, 95%CI = 1.04‐2.08), and the percentage of TC genotype carriers was relatively less than that of controls (P = 0.001, OR = 0.61, 95%CI = 0.45‐0.82). Furthermore, the TT homozygous genotype of rs28372698 was associated with a significantly lower overall survival rate in MIBC patients (P = 0.028, OR = 2.77, 95%CI = 1.11‐6.90). The IL-32 gene polymorphism rs12934561 might be associated with increased BC risk, and the rs28372698 might participate in the prognosis of BC patients. Therefore, they could be potential forecasting factors for the prognosis of MIBC patients.
Stein Variational Model Predictive Control

Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitude of solutions as posterior distributions. By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem. We propose a Stein variational gradient descent method to estimate the posterior directly over control parameters, given a cost function and observed state trajectories. We show that this framework leads to successful planning in challenging, non-convex optimal control problems.
Nonuniform Quantized Decoder for Polar Codes With Minimum Distortion Quantizer

We propose a nonuniform quantized decoder for polar codes. The design metric of the quantizers is to minimize the distortion incurred by quantization. The quantizers are obtained via dynamic programming and the optimality of the quantizer is proved as well. Simulation results show that the error correction performance degradation of the proposed nonuniform quantized decoder is less than 0.1 dB compared to conventional float-point decoders under 5-bit nonuniform quantization, outperforming previous uniform quantized decoders for polar codes significantly.
Dynamical effective field model for interacting ferrofluids: I. Derivations for homogeneous, inhomogeneous, and polydisperse cases

Quite recently I have proposed a nonperturbative dynamical effective field model (DEFM) to quantitatively describe the dynamics of interacting ferrofluids. Its predictions compare very well with the results from Brownian dynamics simulations. In this paper I put the DEFM on firm theoretical ground by deriving it within the framework of dynamical density functional theory, taking into account nonadiabatic effects. The DEFM is generalized to inhomogeneous finite-size samples for which the macroscopic and mesoscopic scale separation is nontrivial due to the presence of long-range dipole–dipole interactions. The demagnetizing field naturally emerges from microscopic considerations and is consistently accounted for. The resulting mesoscopic dynamics only involves macroscopically local quantities such as local magnetization and Maxwell field. Nevertheless, the local demagnetizing field essentially couples to magnetization at distant macroscopic locations. Thus, a two-scale parallel algorithm, involving information transfer between different macroscopic locations, can be applied to fully solve the dynamics in an inhomogeneous sample. I also derive the DEFM for polydisperse ferrofluids, in which different species can be strongly coupled to each other dynamically. I discuss the underlying assumptions in obtaining a thermodynamically consistent polydisperse magnetization relaxation equation, which is of the same generic form as that for monodisperse ferrofluids. The theoretical advances presented in this paper are important for both qualitative understanding and quantitative modeling of the dynamics of ferrofluids and other dipolar systems.
Temporal Dynamic Model for Resting State fMRI Data: A Neural Ordinary Differential Equation approach

The objective of this paper is to provide a temporal dynamic model for resting state functional Magnetic Resonance Imaging (fMRI) trajectory to predict future brain images based on the given sequence. To this end, we came up with the model that takes advantage of representation learning and Neural Ordinary Differential Equation (Neural ODE) to compress the fMRI image data into latent representation and learn to predict the trajectory following differential equation. Latent space was analyzed by Gaussian Mixture Model. The learned fMRI trajectory embedding can be used to explain the variance of the trajectory and predict human traits for each subject. This method achieves average 0.5 spatial correlation for the whole predicted trajectory, and provide trained ODE parameter for further analysis.
Stochastic modification of Newtonian dynamics and induced potential—Application to spiral galaxies and the dark potential

Using the formalism of stochastic embedding developed by [J. Cresson, D. Darses, J. Math. Phys. 48, 072703 (2007)], we study how the dynamics of the classical Newton equation for a force deriving from a potential is deformed under the assumption that this equation can admit stochastic processes as solutions. We focus on two denitions of a stochastic Newton's equation called dierential and variational. We rst prove a stochastic virial theorem which is a natural generalization of the classical case. The stochasticity modies the virial relation by adding a potential term called the induced potential which corresponds in quantum mechanics to the Bohm potential. Moreover, the dierential stochastic Newton equation naturally provides an action functional which sat-ises a stochastic Hamilton-Jacobi equation. The real part of this equation corresponds to the classical Hamilton-Jacobi equation with an extra potential term corresponding to the induced potential already observed in the stochastic virial theorem. The induced potential has an explicit form depending on the density of the stochastic processes solutions of the stochastic Newton equation. It is proved that this density satises a nonlinear Schr{o}dinger equation. Applying this formalism for the Kepler potential, one proves that the induced potential coincides with the ad-hoc ''dark potential'' used to recover a at rotation curve of spiral galaxies. We then discuss the application of the previous formalism in the context of spiral galaxies following the proposal and computations given by [D. Da Rocha and L. Nottale, Chaos, Solitons and Fractals, 16(4):565-595, 2003] where the emergence of the ''dark potential'' is seen as a consequence of the fractality of space in the context of the Scale relativity theory.
Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

Among the most prevalent motion planning techniques, sampling and trajectory optimization have emerged successful due to their ability to handle tight constraints and high-dimensional systems, respectively. However, limitations in sampling in higher dimensions and local minima issues in optimization have hindered their ability to excel beyond static scenes in offline settings. Here we consider highly dynamic environments with long horizons that necessitate a fast on-line solution. We present a unified approach that leverages the complementary strengths of sampling and optimization, and interleaves them both in a manner that is well suited to this challenging problem. With benchmarks in multiple synthetic and realistic simulated environments, we show that our approach performs significantly better on various metrics against baselines that employ either only sampling or only optimization. Project page: https://sites.google. com/view/jistplanner
Reachability in Two-Parametric Timed Automata with one Parameter is EXPSPACE-Complete

Parametric timed automata (PTA) have been introduced by Alur, Henzinger, and Vardi as an extension of timed automata in which clocks can be compared against parameters. The reachability problem asks for the existence of an assignment of the parameters to the non-negative integers such that reachability holds in the underlying timed automaton. The reachability problem for PTA is long known to be undecidable, already over three parametric clocks. A few years ago, Bundala and Ouaknine proved that for PTA over two parametric clocks and one parameter the reachability problem is decidable and also showed a lower bound for the complexity class P S P A C E ^ N E X P . Our main result is that the reachability problem for two-parametric timed automata with one parameter is E X P S P A C E -complete. Our contribution is two-fold. For the E X P S P A C E lower bound, inspired by [ 13 , 14 ], we make use of deep results from complexity theory, namely a serializability characterization of E X P S P A C E (in turn based on Barrington’s Theorem) and a logspace translation of numbers in Chinese remainder representation to binary representation due to Chiu, Davida, and Litow. It is shown that with small PTA over two parametric clocks and one parameter one can simulate serializability computations. For the E X P S P A C E upper bound, we first give a careful exponential time reduction from PTA over two parametric clocks and one parameter to a (slight subclass of) parametric one-counter automata over one parameter based on a minor adjustment of a construction due to Bundala and Ouaknine. For solving the reachability problem for parametric one-counter automata with one parameter, we provide a series of techniques to partition a fictitious run into several carefully chosen subruns that allow us to prove that it is sufficient to consider a parameter value of exponential magnitude only. This allows us to show a doubly-exponential upper bound on the value of the only parameter of a PTA over two parametric clocks and one parameter. We hope that extensions of our techniques lead to finally establishing decidability of the long-standing open problem of reachability in parametric timed automata with two parametric clocks (and arbitrarily many parameters) and, if decidability holds, determinining its precise computational complexity.
Impact of crop diversification on socio-economic life of tribal farmers: A case study from Eastern ghats of India

In this study we investigated impact of crop diversification on socio-economic life of tribal people from eastern ghats of India. We have adopted linear regression formalism to check impact of cross diversification. We observe a positive intercept for almost all factors. Coefficient of correlation is calculated to examine the inter dependence of CDI and our various individually measured dependent variables. A positive correlation is observed in almost all factors. This study shows that a positive change occurred in their social, economic life in the post diversification era.
SOLUTIONS OF THE tt*-TODA EQUATIONS AND QUANTUM COHOMOLOGY OF MINUSCULE FLAG MANIFOLDS

Abstract We relate the quantum cohomology of minuscule flag manifolds to the tt*-Toda equations, a special case of the topological–antitopological fusion equations which were introduced by Cecotti and Vafa in their study of supersymmetric quantum field theories. To do this, we combine the Lie-theoretic treatment of the tt*-Toda equations of Guest–Ho with the Lie-theoretic description of the quantum cohomology of minuscule flag manifolds from Chaput–Manivel–Perrin and Golyshev–Manivel.