Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified neural encoding of social communication reflects lifestyle

As signal production changes through speciation, the sensory systems that receive these signals must also adapt to extract the most relevant information effectively. In a species of weakly electric fish, Apteronotus albifrons we examine the unique neurophysiological properties that support the encoding of electrosensory communication signals that the animal would encounter in social exchanges. We compare our findings to known coding properties of the closely related species, Apteronotus leptorhynchus, to establish how these animals differ in their ability to encode their distinctive communication signals. While there are many similarities between these two species, we find notable differences leading to relatively poor coding of the details of the chirp structure. As a result, small differences in chirp properties are poorly resolved by the nervous system. We performed behavioral tests to relate A. albifrons chirp coding strategies to its use of chirps during social encounters. Our results suggest that A. albifrons do not exchange frequent chirps in non-breeding condition. These findings parallel the mediocre chirp coding accuracy in that they both point to the sparse reliance on chirps in social interactions. Therefore, our study suggests that neural coding strategies in the central nervous system vary across species in a way that parallels the behavioral use of the sensory signals.
Attractive multicomponent Gaudin-Yang model: Three roads to the energy gap

We determine analytically the energy gap at weak coupling in the attractive multi-component Gaudin--Yang model, an integrable model which describes interacting fermions in one dimension with $\kappa$ components. We use three different methods. The first one is based on a direct analysis of the Bethe ansatz equations. The second method uses the theory of resurgence and the large order behavior of the perturbative series for the ground state energy. The third method is based on a renormalization group analysis. The three methods lead to the same answer, providing in this way a non-trivial test of the ideas of resurgence and renormalons as applied to non-relativistic many-body systems.
PyraPose: Feature Pyramids for Fast and Accurate Object Pose Estimation under Domain Shift

Object pose estimation enables robots to understand and interact with their environments. Training with synthetic data is necessary in order to adapt to novel situations. Unfortunately, pose estimation under domain shift, i.e., training on synthetic data and testing in the real world, is challenging. Deep learning-based approaches currently perform best when using encoder-decoder networks but typically do not generalize to new scenarios with different scene characteristics. We argue that patch-based approaches, instead of encoder-decoder networks, are more suited for synthetic-to-real transfer because local to global object information is better represented. To that end, we present a novel approach based on a specialized feature pyramid network to compute multi-scale features for creating pose hypotheses on different feature map resolutions in parallel. Our single-shot pose estimation approach is evaluated on multiple standard datasets and outperforms the state of the art by up to ∼35 %. We also perform grasping experiments in the real world to demonstrate the advantage of using synthetic data to generalize to novel environments.
Over-parametrized neural networks as under-determined linear systems

We draw connections between simple neural networks and under-determined linear systems to comprehensively explore several interesting theoretical questions in the study of neural networks. First, we emphatically show that it is unsurprising such networks can achieve zero training loss. More specifically, we provide lower bounds on the width of a single hidden layer neural network such that only training the last linear layer suffices to reach zero training loss. Our lower bounds grow more slowly with data set size than existing work that trains the hidden layer weights. Second, we show that kernels typically associated with the ReLU activation function have fundamental flaws -- there are simple data sets where it is impossible for widely studied bias-free models to achieve zero training loss irrespective of how the parameters are chosen or trained. Lastly, our analysis of gradient descent clearly illustrates how spectral properties of certain matrices impact both the early iteration and long-term training behavior. We propose new activation functions that avoid the pitfalls of ReLU in that they admit zero training loss solutions for any set of distinct data points and experimentally exhibit favorable spectral properties.
How Does Performance Scale with Antenna Number for Extremely Large-Scale MIMO?

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications correspond to systems whose antenna size is so large that conventional assumptions, such as uniform plane wave (UPW) impingement, are no longer valid. This paper studies the channel modelling and performance analysis of XL-MIMO communication based on the generic spherical wavefront propagation model. First, for the single-user uplink/downlink communication with the optimal maximum ratio combining/transmission (MRC/MRT), we rigorously derive a new closed-form expression for the resulting signal-to-noise ratio (SNR), which includes the conventional SNR expression based on UPW assumption as a special case. Our result shows that instead of scaling linearly with the base station (BS) antenna number M, the SNR with the more generic spherical wavefront model increases with M with diminishing return, governed by a new parameter called angular span. One important finding from our derivation is the necessity to introduce a new distance criterion, termed critical distance, to complement the classical Rayleigh distance for separating the near- and far-field propagation regions. While Rayleigh distance is based on the phase difference across array elements and hence depends on the electrical size of the antenna, the critical distance cares about the amplitude/power difference and only depends on its physical size. We then extend the study to the multi-user XL-MIMO communication system, for which we demonstrate that inter-user interference (IUI) can be mitigated not just by angle separation, but also by distance separation along the same direction. This offers one new degree of freedom (DoF) for interference suppression with XL-MIMO.
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization. However, it is unclear when IRM should be preferred over the widely-employed empirical risk minimization (ERM) framework. In this work, we analyze both these frameworks from the perspective of sample complexity, thus taking a firm step towards answering this important question. We find that depending on the type of data generation mechanism, the two approaches might have very different finite sample and asymptotic behavior. For example, in the covariate shift setting we see that the two approaches not only arrive at the same asymptotic solution, but also have similar finite sample behavior with no clear winner. For other distribution shifts such as those involving confounders or anti-causal variables, however, the two approaches arrive at different asymptotic solutions where IRM is guaranteed to be close to the desired OOD solutions in the finite sample regime, while ERM is biased even asymptotically. We further investigate how different factors -- the number of environments, complexity of the model, and IRM penalty weight -- impact the sample complexity of IRM in relation to its distance from the OOD solutions
Fiber Tracking Velocimetry for Two-Point Statistics of Turbulence

A novel experimental technique to measure two-point statistics of turbulent flows is proposed and tested. It consists in spreading rigid fibers in the flow and tracking their position and orientation in time and therefore been named "Fiber Tracking Velocimetry" (FTV). By choosing different fiber lengths, i.e. within the inertial range of scales or within the viscous layer, the statistics of turbulence fluctuations at the selected lengthscale can be proved accurately by simply measuring the fiber velocity at its two ends, and projecting it along the transverse-to-the-fiber direction. By means of fully-resolved direct numerical simulations and experiments, we show that these fiber-based transverse velocity increments are statistically equivalent to the (unperturbed) flow transverse velocity increments. Moreover, we show that the turbulent energy dissipation rate can be accurately measured exploiting sufficiently short fibers. The technique has been tested against standard Particle Tracking Velocimetry (PTV) of flow tracers with excellent agreement. Our technique overcomes the well-known problem of PTV to probe two-point statistics reliably because of the fast relative diffusion in turbulence that prevents the mutual distance between particles to remain constant at the lengthscale of interest. This problem, making it difficult to obtain converged statistics for a fixed separation distance, is even more dramatic for natural flows in open domains. A prominent example are oceanic currents, where drifters (i.e.the tracer-particle counterpart used in field measurements) disperse quickly, but at the same time their number has to be limited to save costs. Inspired by our laboratory experiments, we propose couples of connected drifters as a viable option to solve the issue.
Classifying Malware Images with Convolutional Neural Network Models

Due to increasing threats from malicious software (malware) in both number and complexity, researchers have developed approaches to automatic detection and classification of malware, instead of analyzing methods for malware files manually in a time-consuming effort. At the same time, malware authors have developed techniques to evade signature-based detection techniques used by antivirus companies. Most recently, deep learning is being used in malware classification to solve this issue. In this paper, we use several convolutional neural network (CNN) models for static malware classification. In particular, we use six deep learning models, three of which are past winners of the ImageNet Large-Scale Visual Recognition Challenge. The other three models are CNN-SVM, GRU-SVM and MLP-SVM, which enhance neural models with support vector machines (SVM). We perform experiments using the Malimg dataset, which has malware images that were converted from Portable Executable malware binaries. The dataset is divided into 25 malware families. Comparisons show that the Inception V3 model achieves a test accuracy of 99.24%, which is better than the accuracy of 98.52% achieved by the current state-of-the-art system called the M-CNN model.
Inherent Trade-offs in the Fair Allocation of Treatments

Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we propose a causal framework that learns optimal intervention policies from data subject to fairness constraints. We define two measures of treatment bias and infer best treatment assignment that minimizes the bias while optimizing overall outcome. We demonstrate that there is a dilemma of balancing fairness and overall benefit; however, allowing preferential treatment to protected classes in certain circumstances (affirmative action) can dramatically improve the overall benefit while also preserving fairness. We apply our framework to data containing student outcomes on standardized tests and show how it can be used to design real-world policies that fairly improve student test scores. Our framework provides a principled way to learn fair treatment policies in real-world settings.
Turbulent Gas in Lensed Planck-selected Starbursts at z ∼ 1–3.5

Dusty star-forming galaxies at high redshift (1 < z < 3) represent the most intense star-forming regions in the universe. Key aspects to these processes are the gas heating and cooling mechanisms, and although it is well known that these galaxies are gas-rich, little is known about the gas excitation conditions. Only a few detailed radiative transfer studies have been carried out owing to a lack of multiple line detections per galaxy. Here we examine these processes in a sample of 24 strongly lensed star-forming galaxies identified by the Planck satellite (LPs) at z ∼ 1.1–3.5. We analyze 162 CO rotational transitions (ranging from Jup = 1 to 12) and 37 atomic carbon fine-structure lines ([C i]) in order to characterize the physical conditions of the gas in the sample of LPs. We simultaneously fit the CO and [C i] lines and the dust continuum emission, using two different non-LTE, radiative transfer models. The first model represents a two-component gas density, while the second assumes a turbulence-driven lognormal gas density distribution. These LPs are among the most gas-rich, IR-luminous galaxies ever observed (μL L IR ( 8 − 1000 μ m ) ∼ 10 13 − 14.6 L⊙; 〈 μLMISM 〉 = (2.7 ± 1.2) × 1012 M⊙, with μL ∼ 10–30 the average lens magnification factor). Our results suggest that the turbulent interstellar medium present in the LPs can be well characterized by a high turbulent velocity dispersion ( 〈 ΔVturb 〉 ∼ 100 km s−1) and ratios of gas kinetic temperature to dust temperature 〈 Tkin/Td 〉 ∼ 2.5, sustained on scales larger than a few kiloparsecs. We speculate that the average surface density of the molecular gas mass and IR luminosity, Σ M ISM ∼ 103–4 M⊙ pc−2 and Σ L IR ∼ 1011–12 L⊙ kpc−2, arise from both stellar mechanical feedback and a steady momentum injection from the accretion of intergalactic gas.
Quasiparticle self-consistent 
GW
 band structures and high-pressure phase transitions of 
LiGaO2
 and 
NaGaO2

Quasi-particle self-consistent $GW$ calculations are presented for the band structures of LiGaO2 and NaGaO2 in the orthorhombic $Pna2_1$ tetrahedrally coordinated crystal structures. Symmetry labeling of the bands near the gap is carried out and effective mass tensors are extracted for the conduction band minimum and crystal field split valence band maxima at $\Gamma$. The gap is found to be direct at $\Gamma$ and is 5.81 eV in LiGaO2 and 5.46 eV in NaGaO2. Electron-phonon coupling zero-point normalization is estimated to lower these gaps by about 0.2 eV. Optical response functions are calculated within the independent particle long wavelength limit and show the expected anisotropy of the absorption onsets due to the crystal field splitting of the VBM. The results show that both materials are promising candidates as ultrawide gap semiconductors with wurtzite based tetrahedrally bonded crystal structures. Direct transitions from the lowest conduction band to higher bands, relevant to n-type doped material and transparent conduction applications are found to start only above 3.9 eV and are allowed for only one polarization, and several higher band transitions are forbidden by symmetry. Alternative crystal structures, such as $R\bar{3}m$ and a rocksalt type phase with tetragonally distorted $P4/mmm$ spacegroup, both with octahedral coordination of the cations are also investigated. They are found to have higher energy but about 20 \% smaller volume per formula unit. The transition pressures to these phases are determined and for LiGaO2 found to be in good agreement with experimental studies. The $R\bar{3}m$phase also has a comparably high but slightly indirect band gap while the rocksalt type phase if found to have a considerably smaller gap of about 3.1 eV in LiGaO2 and 1.0 eV in NaGaO2.
X-ray spectra in magnetic van der Waals materials Fe$_3$GeTe$_2$, CrI$_3$, and CrGeTe$_3$: a first-principles study

Using density functional theory (DFT) methods, we have calculated X-ray absorption spectroscopy (XAS) and X-ray circular dichroism (XMCD) spectra in bulk and thin films of Fe$_3$GeTe$_2$, CrI$_3$, and CrGeTe$_3$. DFT+$U$ methods are employed for better handling of correlation effects of 3$d$ electrons of transition metals. We discuss relations between the density of states, radial matrix elements, and the corresponding spectra. By comparing the calculated spectra with previously measured spectra, we discuss the reliability of DFT+$U$ methods to describe the electronic structures of these materials and determine the corresponding optimal $U$ and $J$ parameters.
Spin-wave dispersion and exchange stiffness in 
Nd2Fe14B
 and 
RFe11Ti
 (
R=Y,Nd,Sm
) from first-principles calculations

We theoretically investigate spin-wave dispersion in rare-earth magnet compounds by using first-principles calculations and a method we call the reciprocal-space algorithm. The value of the calculated exchange stiffness for ${\mathrm{Nd}}_{2}{\mathrm{Fe}}_{14}\mathrm{B}$ is within the range of reported experimental values. We find that the exchange stiffness is considerably anisotropic when only short-range exchange couplings are considered, whereas inclusion of long-range couplings weakens the anisotropy. In contrast, $R{\mathrm{Fe}}_{11}\mathrm{Ti}$ ($R=\mathrm{Y},\mathrm{Nd},\mathrm{Sm}$) shows large anisotropy in the exchange stiffness.
Evaluation of approaches for accommodating interactions and non‐linear terms in multiple imputation of incomplete three‐level data

Three‐level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple imputation (MI). Although several MI approaches can be used to account for the three‐level structure, including adaptations to single‐ and two‐level approaches, when the substantive analysis model includes interactions or quadratic effects, these too need to be accommodated in the imputation model. In such analyses, substantive model compatible (SMC) MI has shown great promise in the context of single‐level data. Although there have been recent developments in multilevel SMC MI, to date only one approach that explicitly handles incomplete three‐level data is available. Alternatively, researchers can use pragmatic adaptations to single‐ and two‐level MI approaches, or two‐level SMC‐MI approaches. We describe the available approaches and evaluate them via simulations in the context of three three‐level random effects analysis models involving an interaction between the incomplete time‐varying exposure and time, an interaction between the time‐varying exposure and an incomplete time‐fixed confounder, or a quadratic effect of the exposure. Results showed that all approaches considered performed well in terms of bias and precision when the target analysis involved an interaction with time, but the three‐level SMC MI approach performed best when the target analysis involved an interaction between the time‐varying exposure and an incomplete time‐fixed confounder, or a quadratic effect of the exposure. We illustrate the methods using data from the Childhood to Adolescence Transition Study.
Prediction of USA November 2020 Election Results Using Multifactor Twitter Data Analysis Method

In studies on election result prediction based on Twitter data, estimates were made using one of the factors such as the number of positive, negative, and neutral tweets posted about parties, the effect size of these tweets (the number of re-tweets), or the number of people who posted these tweets. However, no study was found that used all of these factors together. The goal of this study is to develop a new approach that takes into account all of the factors described and contributes to the literature in this context. A new multifactor model for the election result prediction based on Twitter data has been developed for this purpose. The model was tested by attempting to predict the results of the US 2020 elections in November, which had not yet taken place when the first version of this article was written. Also, a comparison has been made with alternative estimation approaches in the literature. Analyzes were made for approximately 10 million tweets collected between September 1 and October 21, 2020. As a result of the analysis, consistent with the results of the polls, Biden wins the election with a difference of 9.22% according to our method of estimating votes based on positive and negative tweet numbers, which are the current approaches in the literature. However, by using our multifactor model, the parameters for 3 November were calculated as -0.213423 for Democrats and 0.0455818 for Republicans. Based on these scores, it is concluded that the Republicans will win the election with a very small margin.
Computing solutions of Schrödinger equations on unbounded domains- On the brink of numerical algorithms

We address the open problem of determining which classes of time-dependent linear Schrodinger equations and focusing and defocusing cubic and quintic non-linear Schrodinger equations (NLS) on unbounded domains that can be computed by an algorithm. We demonstrate how such an algorithm in general does not exist, yielding a substantial classification theory of which problems in quantum mechanics that can be computed. Moreover, we establish classifications on which problems that can be computed with a uniform bound on the runtime, as a function of the desired $\epsilon$-accuracy of the approximation. This include linear and nonlinear Schrodinger equations for which we provide positive and negative results and conditions on both the initial state and the potentials such that there exist computational (recursive) a priori bounds that allow reduction of the IVP on an unbounded domain to an IVP on a bounded domain, yielding an algorithm that can produce an $\epsilon$-approximation. In addition, we show how no algorithm can decide, and in fact not verify nor falsify, if the focusing NLS will blow up in finite time or not, yet, for the defocusing NLS, solutions can be computed given mild assumptions on the initial state and the potentials. Finally, we show that solutions to discrete NLS equations (focusing and defocusing) on an unbounded domain can always be computed with uniform bounds on the runtime of the algorithm. The algorithms presented are not just of theoretical interest, but efficient and easy to implement in applications. Our results have implications beyond computational quantum mechanics and are a part of the Solvability Complexity Index (SCI) hierarchy and Smale's program on the foundations of computational mathematics. For example our results provide classifications of which mathematical problems may be solved by computer assisted proofs.
Three-velocity coalescing ballistic annihilation

Ballistic annihilation is an interacting particle system in which particles move throughout the real line at preassigned velocities and mutually annihilate upon colliding. In the symmetric three-velocity setting, we generalize these dynamics to include the possibility that collisions result in coalescence. We provide a conjectural formula for the critical initial density of middle-velocity particles that holds for many cases. One corollary is that there are coalescing systems with no phase transition.