Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Omics of Fungi for Plant Biomass Deconstruction.

Wood-decay fungi contain the cellular mechanisms to decompose such plant cell wall components as cellulose, hemicellulose, and lignin. A multi-omics approach to the comparative analysis of wood-decay fungi gives not only new insights into their strategies for decomposing recalcitrant plant biomass, but also an understanding of how to exploit these mechanisms for biotechnological applications. We have developed an analytical workﬂow, Applied Biomass Conversion Design for Efﬁcient Fungal Green Technology (ABCDEFGT), to simplify the analysis and interpretation of transcriptomic and secretomic data. ABCDEFGT utilizes self-organizing maps for grouping genes with similar transcription patterns, and an overlay with secreted proteins. The key feature of ABCDEFGT is simple graphic outputs of genome-wide transcriptomic and secretomic topographies, which enables visual inspection without a priori of the omics data and facilitates discoveries of co-regulated genes and proteins. Genome-wide omics landscapes were built with the newly sequenced fungal species Pycnoporus coccineus , Pycnoporus sanguineus , and Pycnoporus cinnabarinus grown on various carbon sources. Integration of the post-genomic data revealed a global overlap, conﬁrming the pertinence of the genome-wide approach. ABCDEFGT was evaluated by comparison with the latest clustering method for ease of output interpretation, and ABCDEFGT gave a better biological representation of fungal behaviors. The genome-wide multi-omics strategy allowed us to determine the potential synergy of particular enzymes decomposing cellulose, hemicellulose, and lignin such as Lytic Polysaccharide Monooxygenases, modular enzymes associated with a cellulose binding module1, and Class II Peroxidase isoforms co-regulated with oxido-reductases. Overall, ABCDEFGT was capable of visualizing genome-wide transcriptional and secretomic proﬁles for intuitive interpretations and is suitable for exploration of newly-sequenced
MT-SNN: Spiking Neural Network that Enables Single-Tasking of Multiple Tasks

In this paper we explore capabilities of spiking neural networks in solving multi-task classification problems using the approach of single-tasking of multiple tasks. We designed and implemented a multi-task spiking neural network (MT-SNN) that can learn two or more classification tasks while performing one task at a time. The task to perform is selected by modulating the firing threshold of leaky integrate and fire neurons used in this work. The network is implemented using Intel's Lava platform for the Loihi2 neuromorphic chip. Tests are performed on dynamic multitask classification for NMNIST data. The results show that MT-SNN effectively learns multiple tasks by modifying its dynamics, namely, the spiking neurons' firing threshold.
COMET: Coverage-guided Model Generation For Deep Learning Library Testing

Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications. Techniques have been proposed to generate various DL models and apply them to test these libraries. However, their test effectiveness is constrained by the diversity of layer API calls in their generated DL models. Our study reveals that these techniques can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. As a result, we find that many bugs arising from specific layer API calls (i.e., specific layer inputs, parameter values, or layer sequences) can be missed by existing techniques. Because of this limitation, we propose COMET to effectively generate DL models with diverse layer API calls for DL library testing. COMET: (1) designs a set of mutation operators and a coverage-based search algorithm to diversify layer inputs, layer parameter values, and layer sequences in DL models. (2) proposes a model synthesis method to boost the test efficiency without compromising the layer API call diversity. Our evaluation result shows that COMET outperforms baselines by covering twice as many layer inputs (69.7% vs. 34.1%), layer parameter values (50.2% vs. 25.9%), and layer sequences (39.0% vs. 15.6%) as those by the state-of-the-art. Moreover, COMET covers 3.4% more library branches than those by existing techniques. Finally, COMET detects 32 new bugs in the latest version of eight popular DL libraries, including TensorFlow and MXNet, with 21 of them confirmed by DL library developers and seven of those confirmed bugs have been fixed by developers.
GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition

—Gait is one of the most important biometric modalities that can be used to identify a person from walking postures. Existing gait recognition methods either directly establish Global Feature Representation (GFR) from original gait sequences or generate Local Feature Representation (LFR) from several local parts. However, GFR tends to neglect local details of human postures as the receptive ﬁelds become larger in the deeper network layers. Although LFR allows the network to focus on the detailed posture information of each local region, it neglects the relations among different local parts and thus only exploits limited local information of several speciﬁc regions. To solve these issues, we propose a global-local based gait recognition network, named GaitGL, to generate more discriminative feature representations. To be speciﬁc, a novel Global and Local Convolutional Layer (GLCL) is developed to take full advantage of both global visual information and local region details in each layer. GLCL is a dual-branch structure that consists of a GFR extractor and a mask-based LFR extractor. GFR extractor aims to extract contextual information, e.g. , the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions. In addition, we introduce a novel mask-based strategy to improve the local feature extraction capability. Speciﬁcally, we design pairs of complementary masks to randomly occlude feature maps, and then train our mask-based LFR extractor on various occluded feature maps. In this manner, the LFR extractor will learn to fully exploit local information. Extensive experiments on four popular used datasets, CASIA-B, OU-MVLP, GREW and Gait3D, demonstrate that GaitGL achieves better performance than state-of-the-art gait recognition methods. The average rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and 63.8%, respectively, signiﬁcantly outperforming the competing methods. The proposed method has won the ﬁrst prize in two competitions: Human Identiﬁcation at a Distance (HID) 2020 [1] and HID 2021 [2]. The source code has been released at https://github.com/bb12346/GaitGL.
Multi-User Downlink Beamforming Using Uplink Downlink Duality With CEQs for Frequency Selective Channels

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.
Effects of Graph Convolutions in Multi-layer Networks

Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classification problem of a non-linearly separable Gaussian mixture model coupled with a stochastic block model. First, we show that a single graph convolution expands the regime of the distance between the means where multi-layer networks can classify the data by a factor of at least $1/\sqrt[4]{\mathbb{E}{\rm deg}}$, where $\mathbb{E}{\rm deg}$ denotes the expected degree of a node. Second, we show that with a slightly stronger graph density, two graph convolutions improve this factor to at least $1/\sqrt[4]{n}$, where $n$ is the number of nodes in the graph. Finally, we provide both theoretical and empirical insights into the performance of graph convolutions placed in different combinations among the layers of a network, concluding that the performance is mutually similar for all combinations of the placement. We present extensive experiments on both synthetic and real-world data that illustrate our results.
Implicit Two-Tower Policies

We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states. By explicitly disentangling action from state processing in the policy stack, we achieve two main goals: substantial computational gains and better performance. Our architectures are compatible with both: discrete and continuous action spaces. By conducting tests on 15 environments from OpenAI Gym and DeepMind Control Suite, we show that ITT-architectures are particularly suited for blackbox/evolutionary optimization and the corresponding policy training algorithms outperform their vanilla unstructured implicit counterparts as well as commonly used explicit policies. We complement our analysis by showing how techniques such as hashing and lazy tower updates, critically relying on the two-tower structure of ITTs, can be applied to obtain additional computational improvements.
The Effects of Land-use Change on Brucellosis Transmission in the Greater Yellowstone Ecosystem: A Three Species Mathematical-Epidemiological Model

This paper models brucellosis transmission between elk, cattle, and bison, of high conservation value, in the Greater Yellowstone Ecosystem (GYE). It aims to show how landscape changes in the GYE concomitantly impact brucellosis prevalence in the three species. The approach allows us to see how landscape changes in one location influence disease prevalence in populations elsewhere. The model uses the fact that the populations are configured in such a way that elk are an intermediary for disease transmission between cattle and bison. Using landscape ecology metrics applied to the habitat overlaps between elk and cattle and between elk and bison, the landscape parameters are varied to determine how disease propagates throughout the ecosystem as land-use change occurs. Result aim to provide insights into how land management used for the control of disease spread between cattle and elk, may impact (and be impacted by) disease prevalence in bison.
On Chaotic Behavior of Ash Attractors

The asymptotic sectional hyperbolicity is a weak notion of hyperbolicity that extends properly the sectional-hyperbolicity and includes the Rovella attractor as a archetypal example. The main feature of this definition is the existence of arbitrarily large hyperbolic times for points outside the stable manifolds of the singularities. In this paper we will prove that any attractor associated to a $C^1$ vector field $X$ on a three-dimensional manifold satisfying this kind of hyperbolicity is rescaling-expansive and presents sensitiveness respect to initial conditions.
Estimating the Time-Varying Structures of the Fama-French Multi-Factor Models

Abstract: This study examines the time-varying structures of Fama–French multi-factor models (Fama and French (1993, 2015, 2016, 2018)) using Ito et al.’s (2022) generalized least squares-based time-varying multivariate model. Specifically, we employ 25 benchmark portfolios for the U.S., Japan, and Europe to estimate time-varying parameters in those models, with a focus on time stability. We find that model parameters change over time, with differences in time stability among the countries/regions.
Towards precision particle background estimation for future x-ray missions: correlated variability between Chandra ACIS and AMS

A science goal of many future x-ray observatories is mapping the cosmic web through deep exposures of faint diffuse sources. Such observations require low background and the best possible knowledge of the remaining unrejected background. The dominant contribution to the background above 1-2 keV is from Galactic cosmic ray protons. Their flux and spectrum are modulated by the solar cycle but also by solar activity on shorter timescales. Understanding this variability may prove crucial to reducing background uncertainty for ESA’s Athena x-ray observatory and other missions with large collecting area. We examine the variability of the particle background as measured by ACIS on the Chandra x-ray observatory and compare that variability to that measured by the alpha magnetic spectrometer (AMS), a precision particle detector on the ISS. We show that cosmic ray proton variability measured by AMS is well matched to the ACIS background and can be used to estimate proton energies responsible for the background. We discuss how this can inform future missions.
Quantum and classical spins on the spatially distorted kagomé lattice: Applications to volborthiteCu3V2O7(OH)2∙2H2O

In Volborthite, spin-1/2 moments form a distorted Kagom\'e lattice, of corner sharing isosceles triangles with exchange constants $J$ on two bonds and $J'$ on the third bond. We study the properties of such spin systems, and show that despite the distortion, the lattice retains a great deal of frustration. Although sub-extensive, the classical ground state degeneracy remains very large, growing exponentially with the system perimeter. We consider degeneracy lifting by thermal and quantum fluctuations. To linear (spin wave) order, the degeneracy is found to stay intact. Two complementary approaches are therefore introduced, appropriate to low and high temperatures, which point to the same ordered pattern. In the low temperature limit, an effective chirality Hamiltonian is derived from non-linear spin waves which predicts a transition on increasing $J'/J$, from $\sqrt 3\times \sqrt 3$ type order to a new ferrimagnetic {\em striped chirality} order with a doubled unit cell. This is confirmed by a large-N approximation on the O($n$) model on this lattice. While the saddle point solution produces a line degeneracy, $O(1/n)$ corrections select the non-trivial wavevector of the striped chirality state. The quantum limit of spin 1/2 on this lattice is studied via exact small system diagonalization and compare well with experimental results at intermediate temperatures. We suggest that the very low temperature spin frozen state seen in NMR experiments may be related to the disconnected nature of classical ground states on this lattice, which leads to a prediction for NMR line shapes.
What can we Learn by Predicting Accuracy?

This paper seeks to answer the following question: "What can we learn by predicting accuracy? ". Indeed, classification is one of the most popular tasks in machine learning, and many loss functions have been developed to maximize this non-differentiable objective function. Unlike past work on loss function design, which was guided mainly by intuition and theory before being validated by experimentation, here we propose to approach this problem in the opposite way: we seek to extract knowledge by experimentation. This data-driven approach is similar to that used in physics to discover general laws from data. We used a symbolic regression method to automatically find a mathematical expression highly correlated with a linear classifier’s accuracy. The formula discovered on more than 260 datasets of embeddings has a Pearson’s correlation of 0.96 and a r2 of 0.93. More interestingly, this formula is highly explainable and confirms insights from various previous papers on loss design. We hope this work will open new perspectives in the search for new heuristics leading to a deeper understanding of machine learning theory.
Multigrid reduction-in-time convergence for advection problems: A Fourier analysis perspective

A long-standing issue in the parallel-in-time community is the poor convergence of standard iterative parallel-in-time methods for hyperbolic partial differential equations (PDEs), and for advection-dominated PDEs more broadly. Here, a local Fourier analysis (LFA) convergence theory is derived for the two-level variant of the iterative parallel-in-time method of multigrid reduction-in-time (MGRIT). This closed-form theory allows for new insights into the poor convergence of MGRIT for advection-dominated PDEs when using the standard approach of rediscretizing the fine-grid problem on the coarse grid. Specifically, we show that this poor convergence arises, at least in part, from inadequate coarse-grid correction of certain smooth Fourier modes known as characteristic components, which was previously identified as causing poor convergence of classical spatial multigrid on steady-state advection-dominated PDEs. We apply this convergence theory to show that, for certain semi-Lagrangian discretizations of advection problems, MGRIT convergence using rediscretized coarse-grid operators cannot be robust with respect to CFL number or coarsening factor. A consequence of this analysis is that techniques developed for improving convergence in the spatial multigrid context can be re-purposed in the MGRIT context to develop more robust parallel-in-time solvers. This strategy has been used in recent work to great effect; here, we provide further theoretical evidence supporting the effectiveness of this approach.
Optimal Measurement of Drone Swarm in RSS-Based Passive Localization With Region Constraints

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing optimal sensor placement strategies mainly work on the cases without any constraints on the sensor locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., maximizing the determinant of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed-form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.
A Comprehensive Study of Galaxies at z ∼ 9–16 Found in the Early JWST Data: Ultraviolet Luminosity Functions and Cosmic Star Formation History at the Pre-reionization Epoch

We conduct a comprehensive study on dropout galaxy candidates at z ∼ 9–16 using the first 90 arcmin2 James Webb Space Telescope (JWST) Near Infrared Camera images taken by the early release observations (ERO) and early release science programs. With the JWST simulation images, we find that a number of foreground interlopers are selected with a weak photo-z determination (Δχ 2 > 4). We thus carefully apply a secure photo-z selection criterion (Δχ 2 > 9) and conventional color criteria with confirmations of the ERO Near Infrared Spectrograph spectroscopic redshifts, and obtain a total of 23 dropout galaxies at z ∼ 9–16, including two candidates at zphot=16.25−0.46+0.24 and 16.41−0.55+0.66 . We perform thorough comparisons of dropout galaxies found in our work with recent JWST studies, and conclude that our galaxy sample is reliable enough for statistical analyses. We derive the UV luminosity functions at z ∼ 9–16, and confirm that our UV luminosity functions at z ∼ 9 and 12 agree with those determined by other Hubble Space Telescope and JWST studies. The cosmic star formation rate (SFR) density decreases from z ∼ 9 to 12, and perhaps to 16, but the densities at z ∼ 12–16 are higher than the constant star formation efficiency model. Interestingly, there are six bright galaxy candidates at z ∼ 10–16 with M UV < −19.5 mag and M * ∼ 108−9 M ⊙. Because a majority (∼80%) of these galaxies show no signatures of active galactic nuclei in their morphologies, the high cosmic SFR densities and the existence of these UV-luminous galaxies are explained by the lack of suppression of star formation by the UV background radiation at the pre-reionization epoch and/or an efficient UV radiation production by a top-heavy initial mass function with Population III–like star formation.
Broadband X-Ray Spectroscopy and Estimation of Spin of the Galactic Black Hole Candidate GRS 1758–258

We present the results of a broadband (0.5–78 keV) X-ray spectral study of the persistent Galactic black hole X-ray binary GRS 1758–258 observed simultaneously by Swift and NuSTAR. Fitting with an absorbed power-law model revealed a broad Fe line and reflection hump in the spectrum. We used different flavors of the relativistic reflection model for the spectral analysis. All models indicate the spin of the black hole in GRS 1758–258 is >0.92. The source was in the low hard state during the observation, with the hot electron temperature of the corona estimated to be kT e ∼ 140 keV. The black hole is found to be accreting at ∼1.5% of the Eddington limit during the observation, assuming the black hole mass of 10 M ⊙ and distance of 8 kpc.
Abnormal subanalytic distributions and minimal rank Sard Conjecture

We present a description of singular horizontal curves of a totally nonholonomic analytic distribution in term of the projections of the orbits of some isotropic subanalytic singular distribution defined on the nonzero annihilator of the initial distribution in the cotangent bundle. As a by-product of our first result, we obtain, under an additional assumption on the constructed subanalytic singular distribution, a proof of the minimal rank Sard conjecture in the analytic case. It establishes that from a given point the set of points accessible through singular horizontal curves of minimal rank, which corresponds to the rank of the distribution, has Lebesgue measure zero.