Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Sparsified Federated Neuroimaging Models via Weight Pruning

Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs -- by pruning the model parameters right before the communication step. Moreover, such a progressive model pruning approach during training can also reduce training times/costs. To this end, we propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.
Baryon density and magnetic field effects on chaos in a $Q \bar Q$ system at finite temperature

Baryon density and magnetic ﬁeld eﬀect on chaos for the holographic dual of a Q ¯ Q system at ﬁnite temperature is studied. A string in an AdS Reissner–Nordstrom background, and in a metric with magnetic ﬁeld near the black-hole horizon is considered and small time-dependent perturbations of the static conﬁgurations are investigated. The proximity to the horizon induces chaos, which is softened increasing the chemical potential or the magnetic ﬁeld. A background geometry including the eﬀect of a dilaton is also examined. The Maldacena, Shenker, and Stanford bound on the Lyapunov exponents characterizing the perturbations is satisﬁed for ﬁnite baryon chemical potential and magnetic ﬁeld and when the dilaton is included in the metric.
Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis

Graphics Processing Units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, researchers have focused on demystifying and evaluating the microarchitecture features of various GPU architectures beyond what vendors reveal. This line of work is necessary to understand the hardware better and build more efficient workloads and applications. Many works have studied the recent Nvidia architectures, such as Volta and Turing, comparing them to their successor, Ampere. However, some microarchitecture features, such as the clock cycles for the different instructions, have not been extensively studied for the Ampere architecture. In this paper, we study the clock cycles per instruction with various data types found in the instruction-set architecture (ISA) of Nvidia GPUs. We measure the clock cycles for PTX ISA instructions and their SASS ISA counterparts using microbench-marks. We further calculate the clock cycles needed to access each memory unit. Moreover, we demystify the new version of the tensor core unit found in the Ampere architecture using the WMMA API and measuring its clock cycles per instruction and throughput for the different data types and input shapes. Our results should guide software developers and hardware architects. Furthermore, the clock cycles per instructions are widely used by performance modeling simulators and tools to model and predict the performance of the hardware.
A HIGH-METALLICITY, HIGH-VELOCITY CLOUD ALONG THE Mrk 421 SIGHT LINE: A TRACER OF COMPLEX M?

We present a new measurement, 0.85–3.5 Z☉, of the metallicity of high-velocity cloud (HVC) Complex M by analyzing ultraviolet spectroscopic observations of the blazar Mrk 421 taken with the Cosmic Origins Spectrograph on the Hubble Space Telescope and the Far-Ultraviolet Spectroscopic Explorer. Although an HVC at VLSR = −131 km s−1 is not visible in 21 cm emission (log NH i < 18.38; 3σ), it is detected in ultraviolet absorption lines of C ii, N i, O i, O vi, Si ii, Si iii, Si iv, Fe ii, and H i. By referencing velocities to the intermediate-velocity cloud at −60 km s−1 and jointly analyzing H i absorption from high-order H i Lyman lines, we measure log NH i = 16.84+0.34−0.13 (1σ) in the HVC. Comparing H i and O i, we find an HVC metallicity [O/H] =0.32+0.22−0.39. Because the sight line passes ∼4° from the HVCs in Complex M, the detected HVC may represent the highest velocity component of the Complex, and our measurements provide a lower limit to its metallicity. The high, possibly super-solar metallicity, together with the low distance, z < 3.5 kpc, above the Galactic plane suggests that Complex M is condensed returning gas from a Galactic fountain.
Multidisciplinary learning through collective performance favors decentralization

Significance Like chefs at a fast-moving restaurant or engineers in a multidisciplinary project, team members often complete separate, interrelated subsets of larger tasks with limited insight into the work of others. These contexts make it difficult for individuals to assess the value of their own contribution to the collective work. Our work shows that despite this obstacle, individuals can still learn from their neighbors when neighbors’ actions influence collective outcomes. Though the effects are modest, we found that teams with more interactions between members perform better when refining their work while teams with fewer interactions perform better when innovating. We also found that across 34 tasks with diverse qualities, teams that decentralize coordination responsibilities outperform those that do not.
Constraint-driven multi-task learning

Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make use of multi-task learning. We implement the state-of-the-art approach and several new strategies to improve search performance. Furthermore, we introduce constraint preservation, a technique that improves overall performance for all approaches. Constraint preservation allows the system to transfer knowledge between updates on the background knowledge set. Consequently, we reduce the amount of repeated work performed by the system. Additionally, constraint preservation allows us to transition from the current state-of-the-art iterative deepening search approach to a more efficient breadth first search approach. Finally, we experiment with curriculum learning techniques and show their potential benefit to the field.
Bitext Mining for Low-Resource Languages via Contrastive Learning

Mining high-quality bitexts for low-resource languages is challenging. This paper shows that sentence representation of language models fine-tuned with multiple negatives ranking loss, a contrastive objective, helps retrieve clean bitexts. Experiments show that parallel data mined from our approach substantially outperform the previous state-of-the-art method on low resource languages Khmer and Pashto.
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling

Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data. However, these models can lead to system failure or undesirable outputs when being exposed to spoken language perturbation or variation in practice. We propose a perturbed semantic structure awareness transferring method for training perturbation-robust slot filling models. Specifically, we introduce two MLM-based training strategies to respectively learn contextual semantic structure and word distribution from unsupervised language perturbation corpus. Then, we transfer semantic knowledge learned from upstream training procedure into the original samples and filter generated data by consistency processing. These procedures aims to enhance the robustness of slot filling models. Experimental results show that our method consistently outperforms the previous basic methods and gains strong generalization while preventing the model from memorizing inherent patterns of entities and contexts.
Collaborative Algorithms for Online Personalized Mean Estimation

We consider an online estimation problem involving a set of agents. Each agent has access to a (personal) process that generates samples from a real-valued distribution and seeks to estimate its mean. We study the case where some of the distributions have the same mean, and the agents are allowed to actively query information from other agents. The goal is to design an algorithm that enables each agent to improve its mean estimate thanks to communication with other agents. The means as well as the number of distributions with same mean are unknown, which makes the task nontrivial. We introduce a novel collaborative strategy to solve this online personalized mean estimation problem. We analyze its time complexity and introduce variants that enjoy good performance in numerical experiments. We also extend our approach to the setting where clusters of agents with similar means seek to estimate the mean of their cluster.
$\frac{\rho}{1-\epsilon}$-approximate pure Nash equilibria algorithms for weighted congestion games and their runtimes

This paper concerns computing approximate pure Nash equilibria in weighted congestion games, which has been shown to be PLS-complete. With the help of ˆΨ-game and approximate potential functions, we propose two algorithms based on best response dynamics, and prove that they eﬃciently compute ρ 1 − ǫ -approximate pure Nash equilibria for ρ = d ! and ρ = 2 · W · ( d +1) 2 · W + d +1 ≤ d +1 , respectively, when the weighted congestion game has polynomial latency functions of degree at most d ≥ 1 and players’ weights are bounded from above by a constant W ≥ 1. This improves the recent work of Feldotto et al. [2017] and Giannakopoulos et al. [2022] that showed eﬃcient algorithms for computing d d + o ( d ) approximate pure Nash equilibria.
A simple and intuitive method to calculate $R_0$ in complex epidemic models

Epidemic models are a valuable tool in the decision making process. Once a mathematical model for an epidemics has been established, the very next step is calculating a mathematical expression for the basic reproductive number, $R_0$, which is the average number of infections caused by an individual that is introduced in a population of susceptibles. Finding a mathematical expression for $R_0$ is important because it allows to analyze the effect of the different parameters in the model on $R_0$ so that we can act on them to keep $R_0<1$, so that the epidemic fades out. In this work we show how to calculate $R_0$ in complicated epidemic models by using only basic concepts of Markov chains.
Improved Scheduling of Morphing Edge Drawing

Morphing edge drawing (MED), a graph drawing technique, is a dynamic extension of partial edge drawing (PED), where partially drawn edges (stubs) are repeatedly stretched and shrunk by morphing. Previous experimental evaluations have shown that the reading time with MED may be shorter than that with PED. The morphing scheduling method limits visual clutter by avoiding crossings between stubs. How-ever, as the number of intersections increases, the overall morphing cycle tends to lengthen in this method, which is likely to have a negative eﬀect on the reading time. In this paper, improved scheduling methods are presented to address this issue. The ﬁrst method shortens the duration of a single cycle by overlapping a part of the current cycle with the succeeding one. The second method duplicates every morph by the allowable number of times in one cycle. The third method permits a speciﬁc number of simultaneous crossings per edge. The eﬀective performances of these methods are demonstrated through experimental evaluations.
Event-based Image Deblurring with Dynamic Motion Awareness

Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself. Complementary information from auxiliary sensors such event sensors are being explored to address these limitations. The latter can record changes in a logarithmic intensity asynchronously, called events, with high temporal resolution and high dynamic range. Current event-based deblurring methods combine the blurry image with events to jointly estimate per-pixel motion and the deblur operator. In this paper, we argue that a divide-and-conquer approach is more suitable for this task. To this end, we propose to use modulated deformable convolutions, whose kernel offsets and modulation masks are dynamically estimated from events to encode the motion in the scene, while the deblur operator is learned from the combination of blurry image and corresponding events. Furthermore, we employ a coarse-to-fine multi-scale reconstruction approach to cope with the inherent sparsity of events in low contrast regions. Importantly, we introduce the first dataset containing pairs of real RGB blur images and related events during the exposure time. Our results show better overall robustness when using events, with improvements in PSNR by up to 1.57dB on synthetic data and 1.08 dB on real event data.
Complex magnetoelectric effect in PFN-PT/CoFe$_{2-x}$Zn$_x$O$_4$ bulk particulate composites

The structural, dielectric, magnetic, and magnetoelectric (ME) properties of particulate composites containing lead-iron niobate and lead titanate piezoelectric 0.94[PbFe 0 . 5 Nb 0 . 5 O 3 ]-0.06[PbTi 0 . 5 O 3 ] (PFN-PT) and Zn-substituted cobalt ferrite magnetostrictive CoFe 2 − x Zn x O 4 (CF 2 − x Z x O); 0.6(PFN-PT) / 0.4(CF 2 − x Z x O), x = 0, 0.025, 0.1, 0.2, 0.3 (with ratio of 60 Wt% ferroelectric and 40 Wt% ferrite); have been investigated. We investigated the ME voltage coe ﬃ cient as a complex quantity for all composite samples using the dynamic piezomagnetic coe ﬃ cient, q ac = ∂λ ac /∂ H . The results reveal that tuning the magnetostrictive phase has a strong e ﬀ ect on the real part of the ME voltage coe ﬃ cient. Doping zinc into cobalt ferrite modiﬁed the magnetic properties of the magnetic phase, such as magnetic anisotropy and coercive ﬁeld, and hence the ME properties. The highest ME coe ﬃ cient value of 12.33 mVcm . Oe was obtained for x = 0.1 at the magnetic ﬁeld of 755 Oe. In addition, the magnetic ﬁeld at which the maximum value of the ME coe ﬃ cient was observed ( H peak ) strongly depends on the value of Zn substitution. The results were interpreted using the magnetic ﬁeld dependence of the CF 2 − x Z x O magnetostriction.
Essential spectrum of a self-adjoint operator on an abstract Hilbert space of Fock type and applications to quantum field Hamiltonians

Abstract We establish general theorems on locating the essential spectrum of a self-adjoint operator of the form A ⊗ I + I ⊗ dΓ(S) + HI on the tensor product H ⊗ F b( K ) of a Hilbert space H and the abstract Boson Fock space F b( K ) over a Hilbert space K , where A is a self-adjoint operator on H bounded from below, dΓ(S) is the second quantization of a nonnegative self-adjoint operator S on K , and HI is a symmetric operator on H ⊗ F b( K ). We then apply the theorems to the generalized spin-boson model by A. Arai and M. Hirokawa (1997, J. Funct. Anal.151, 455–503) and a general class of models of quantum particles coupled to a Bose field including the Pauli–Fierz model in nonrelativistic quantum electrodynamics.
Skew Superelevation Edge Rounding Design Improving Riding Comfort on Freeways

Skew superelevation has proven to be an efficient pavement constructional measure to address hydroplaning phenomena under specific critical superelevation runoff designs. However, this technique has raised concerns about user’s comfort when driving over the skewed edge of the pavement, especially for heavy vehicles and when the traveling speed is higher than the design speed. This paper aims to evaluate the driving comfort outcome on an improved skew superelevation design, where a rounding of the skew edge has been applied. This is achieved through the use of a simulation program which examines the vehicle and driver behavior when passing over it. The simulation program estimates many parameters including the lateral and vertical accelerations (Gy and Gz) imposed on both elements, the vehicle and the driver. This evaluation is made for different alignment designs provided in the freeway design and for a broad range of especially high velocities ranging from 80 to 160 km/h (50–100 mph), as well as for different types of heavy vehicles. Also, the influence on the above parameters has been investigated in respect of the distance between two consecutive skew superelevations, concerning cases of entering and exiting a simple, low-length curve. The outcomes are classified in order to evaluate their variability as affected by each parameter change (speed, geometry, vehicle). Simulation results are compared with thresholds, as provided in the international literature, to ensure driver comfort and a high level of road safety.
Systematic Review of Augmented Reality Training Systems

Recent augmented reality (AR) advancements have enabled the development of effective training systems, especially in the medical, rehabilitation, and industrial fields. However, it is unclear from the literature what the intrinsic value of AR to training is and how it differs across multiple application fields. In this work, we gathered and reviewed the prototypes and applications geared towards training the intended user's knowledge, skills, and abilities. Specifically, from IEEE Xplore plus other digital libraries, we collected 64 research papers present in high-impact publications about augmented reality training systems (ARTS). All 64 papers were then categorized according to the training method used, and each paper's evaluations were identified by validity. The summary of the results shows trends in the training methods and evaluations that incorporate ARTS in each field. The narrative synthesis illustrates the different implementations of AR for each of the training methods. In addition, examples of the different evaluation types of the current ARTS are described for each of the aforementioned training methods. We also investigated the different training strategies used by the prevailing ARTS. The insights gleaned from this review can suggest standards for designing ARTS regarding training strategy, and recommendations are provided for the implementation and evaluation of future ARTS.
Molecular Basis of Mink ACE2 Binding to SARS-CoV-2 and Its Mink-Derived Variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a broad range of hosts. Mink-derived SARS-CoV-2 can transmit back to humans. ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted between humans and minks, and some mutations in the spike (S) protein, especially in the receptor-binding domain (RBD), have been identified in mink-derived viruses. Here, we examined binding of the mink angiotensin-converting enzyme 2 (ACE2) receptor to mink-derived and important human-originating variants, and we demonstrated that most of the RBD variants increased the binding affinities to mink ACE2 (mkACE2). Cryo-electron microscopy structures of the mkACE2-RBD Y453F (with a Y-to-F change at position 453) and mkACE2-RBD F486L complexes helped identify the key residues that facilitate changes in mkACE2 binding affinity. Additionally, the data indicated that the Y453F and F486L mutations reduced the binding affinities to some human monoclonal antibodies, and human vaccinated sera efficiently prevented infection of human cells by pseudoviruses expressing Y453F, F486L, or N501T RBD. Our findings provide an important molecular mechanism for the rapid adaptation of SARS-CoV-2 in minks and highlight the potential influence of the main mink-originating variants for humans. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a broad range of hosts. Mink-derived SARS-CoV-2 can transmit back to humans. There is an urgent need to understand the binding mechanism of mink-derived SARS-CoV-2 variants to mink receptor. In this study, we identified all mutations in the receptor-binding domain (RBD) of spike (S) protein from mink-derived SARS-CoV-2, and we demonstrated the enhanced binding affinity of mink angiotensin-converting enzyme 2 (ACE2) to most of the mink-derived RBD variants as well as important human-originating RBD variants. Cryo-electron microscopy structures revealed that the Y453F and F486L mutations enhanced the binding forces in the interaction interface. In addition, Y453F and F486L mutations reduced the binding affinities to some human monoclonal antibodies, and the SARS-CoV-2 pseudoviruses with Y453F, F486L, or N501T mutations were neutralized by human vaccinated sera. Therefore, our results provide valuable information for understanding the cross-species transmission mechanism of SARS-CoV-2.
Draft Genome Sequences of Three Nitrogen-Fixing Strains Isolated from Soil Cooled for Growing Temperate Root Crops in a Tropical Climate

Nitrogen-fixing bacteria can form a symbiotic relationship with several members of the major plant groups and with fungi. Here, we present the draft genome sequences of three nitrogen-fixing strains isolated from soil cooled for growing temperate root crops in a tropical climate. ABSTRACT Nitrogen-fixing bacteria can form a symbiotic relationship with several members of the major plant groups and with fungi. Here, we present the draft genome sequences of three nitrogen-fixing strains isolated from soil cooled for growing temperate root crops in a tropical climate.