Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Advanced Features in CUDA Unified Memory

CUDA Unified Memory improves the GPU pro- grammability and also enables GPU memory oversubscription. Recently, two advanced memory features, memory advises and asynchronous prefetch, have been introduced. In this work, we evaluate the new features on two platforms that feature different CPUs, GPUs, and interconnects. We derive a benchmark suite for the experiments and stress the memory system to evaluate both in-memory and oversubscription performance. The results show that memory advises on the Intel-Volta/Pascal- PCIe platform bring negligible improvement for in-memory exe- cutions. However, when GPU memory is oversubscribed by about 50%, using memory advises results in up to 25% performance improvement compared to the basic CUDA Unified Memory. In contrast, the Power9-Volta-NVLink platform can substantially benefit from memory advises, achieving up to 34% performance gain for in-memory executions. However, when GPU memory is oversubscribed on this platform, using memory advises increases GPU page faults and results in considerable performance loss. The CUDA prefetch also shows different performance impact on the two platforms. It improves performance by up to 50% on the Intel-Volta/Pascal-PCI-E platform but brings little benefit to the Power9-Volta-NVLink platform.
A Domain-agnostic, Noise-resistant Evolutionary Variational Quantum Eigensolver for Hardware-efficient Optimization in the Hilbert Space

Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms for Hamiltonian simulation, such as the Variational Quantum Eigensolver (VQE), use fixed preconstructed ansatzes, limiting their general applicability and accuracy. Thus, variational forms—the quantum circuits that implement ansatzes —are either crafted heuristically or by encoding domainspecific knowledge. In this paper, we present an Evolutionary Variational Quantum Eigensolver (EVQE), a novel variational algorithm that uses evolutionary programming techniques to minimize the expectation value of a given Hamiltonian by dynamically generating and optimizing an ansatz. The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardwareefficient ansatzes that are up to 18.6× shallower and use up to 12× fewer CX gates than results obtained with a unitary coupled cluster ansatz. EVQE demonstrates significant noiseresistant properties, obtaining results in noisy simulation with at least 3.6× less error than VQE using any tested ansatz configuration. We successfully evaluated EVQE on a real 5qubit IBMQ quantum computer. The experimental results, which we obtained both via simulation and on real quantum hardware, demonstrate the effectiveness of EVQE for generalpurpose optimization on the quantum computers of the present and near future.
Mitigating the Performance-Efficiency Tradeoff in Resilient Memory Disaggregation

Memory disaggregation has received attention in recent years as a promising idea to reduce the total cost of ownership (TCO) of memory in modern datacenters. However, relying on remote memory expands an application's failure domain and makes it susceptible to tail latency variations. In attempts to making disaggregated memory resilient, stateof-the-art solutions face the classic tradeoff between performance and efficiency: some double the memory overhead of disaggregation by replicating to remote memory, while many others limit performance by replicating to the local disk. We present Hydra, a configurable, erasure-coded resilience mechanism for common memory disaggregation solutions. It can transparently handle uncertainties arising from remote failures, evictions, memory corruptions, and stragglers from network imbalance with a significantly better performance-efficiency tradeoff than the state-of-the-art. We design a fine-tuned data path to achieve single us read/write latency to remote memory, develop decentralized algorithms for cluster-wide memory management, and analyze how to select parameters to mitigate independent and correlated uncertainties. Our integration of Hydra with two major memory disaggregation systems and evaluation on a 50-machine RDMA cluster demonstrates that it achieves the best of both worlds: it improves the latency and throughput of memory-intensive applications by up to 64.78X and 20.61X, respectively, over the state-of-the-art disk backup-based solution. At the same time, it provides performance similar to that of in-memory replication with 1.6X lower memory overhead.
Constraining the cosmic ray spectrum in the vicinity of the supernova remnant W28: from sub-GeV to multi-TeV energies

Context. Supernova remnants interacting with molecular clouds are ideal laboratories to study the acceleration of particles at shock waves and their transport and interactions in the surrounding interstellar medium. Aims. Here, we focus on the supernova remnant W28, which over the years has been observed in all energy domains from radio waves to very-high-energy gamma rays. The bright gamma-ray emission detected from molecular clouds located in its vicinity revealed the presence of accelerated GeV and TeV particles in the region. An enhanced ionization rate has also been measured by means of millimeter observations, but such observations alone cannot tell us whether the enhancement is due to low-energy (MeV) cosmic rays (either protons or electrons) or the X-ray photons emitted by the shocked gas. The goal of this study is to determine the origin of the enhanced ionization rate and to infer from multiwavelength observations the spectrum of cosmic rays accelerated at the supernova remnant shock in an unprecedented range spanning from MeV to multi-TeV particle energies. Methods. We developed a model to describe the transport of X-ray photons into the molecular cloud, and we fitted the radio, millimeter, and gamma-ray data to derive the spectrum of the radiating particles. Results. The contribution from X-ray photons to the enhanced ionization rate is negligible, and therefore the ionization must be due to cosmic rays. Even though we cannot exclude a contribution to the ionization rate coming from cosmic-ray electrons, we show that a scenario where cosmic-ray protons explain both the gamma-ray flux and the enhanced ionization rate provides the most natural fit to multiwavelength data. This strongly suggests that the intensity of CR protons is enhanced in the region for particle energies in a very broad range covering almost six orders of magnitude: from ≲100 MeV up to several tens of TeV.
Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity

The relation between language and thought has occupied linguists for at least a century. Neo-Whorfianism, a weak version of the controversial Sapir-Whorf hypothesis, holds that our thoughts are subtly influenced by the grammatical structures of our native language. One area of investigation in this vein focuses on how the grammatical gender of nouns affects the way we perceive the corresponding objects. For instance, does the fact that key is masculine in German (der Schlussel), but feminine in Spanish (la llave) change the speakers' views of those objects? Psycholinguistic evidence presented by Boroditsky et al. (2003, §4) suggested the answer might be yes: When asked to produce adjectives that best described a key, German and Spanish speakers named more stereotypically masculine and feminine ones, respectively. However, recent attempts to replicate those experiments have failed (Mickan et al., 2014). In this work, we offer a computational analogue of Boroditsky et al. (2003, §4)'s experimental design on 9 languages, finding evidence against neo-Whorfianism.
Transductive Parsing for Universal Decompositional Semantics

We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing UDS graph structure, and show that our parser can perform comparably while additionally performing attribute prediction.
Penalization via global functionals of optimal-control problems for dissipative evolution

We consider an optimal control problem for an abstract nonlinear dissipative evolution equation. The differential constraint is penalized by augmenting the target functional by a nonnegative global-in-time functional which is null-minimized in the evolution equation is satisfied. Different variational settings are presented, leading to the convergence of the penalization method for gradient flows, noncyclic and semimonotone flows, doubly nonlinear evolutions, and GENERIC systems.
Hierarchical quantum master equation approach to current fluctuations in nonequilibrium charge transport through nanosystems

We present a hierarchical quantum master equation (HQME) approach, which allows the numerically exact evaluation of higher-order current cumulants in the framework of full counting statistics for nonequilibrium charge transport in nanosystems. The novel methodology is exemplarily applied to a model of vibrationally coupled electron transport in a molecular nanojunction. We investigate the influence of cotunneling on avalanchelike transport, in particular in the nonresonant transport regime, where we find that inelastic cotunneling acts as a trigger process for resonant avalanches, which manifests itself as finite temperature oscillations in the current fluctuations. We also demonstrate that the correction to the elastic noise upon opening of the inelastic transport channel is nonuniversal and strongly affected by the nonequilibrium excitation of the vibration.
Optimal Sensing for Fish School Identification

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information.
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels

When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach is that training should be generally carried out from scratch for each new channel. To cope with this problem, prior works considered joint training over multiple channels with the aim of finding a single pair of encoder and decoder that works well on a class of channels. As a result, joint training ideally mimics the operation of non-coherent transmission schemes. In this paper, we propose to obviate the limitations of joint training via meta-learning: Rather than training a common model for all channels, meta-learning finds a common initialization vector that enables fast training on any channel. The approach is validated via numerical results, demonstrating significant training speed-ups, with effective encoders and decoders obtained with as little as one iteration of Stochastic Gradient Descent.
emerge – empirical constraints on the formation of passive galaxies

We present constraints on the formation and evolution of early-type galaxies (ETGs) with the empirical model EMERGE. The parameters of this model are adjusted so that it reproduces the evolution of stellar mass functions, specific star formation rates, and cosmic star formation rates since $z\approx10$ as well as 'quenched' galaxy fractions and correlation functions. We find that at fixed halo mass present-day ETGs are more massive than late-type galaxies, whereas at fixed stellar mass ETGs populate more massive halos in agreement with lensing results. This effect naturally results from the shape and scatter of the stellar-to-halo mass relation and the galaxy formation histories. The ETG stellar mass assembly is dominated by 'in-situ' star formation below a stellar mass of $3\times10^{11}\mathrm{M}_\odot$ and by merging and accretion of 'ex-situ' formed stars at higher mass. The mass dependence is in tension with current cosmological simulations. Lower mass ETGs show extended star formation towards low redshift in agreement with recent estimates from IFU surveys. All ETGs have main progenitors on the 'main sequence of star formation' with the 'red sequence' appearing at $z \approx 2$. Above this redshift, over 95 per cent of the ETG progenitors are star-forming. More than 90 per cent of $z \approx 2$ 'main sequence' galaxies with $m_* > 10^{10}\mathrm{M}_\odot$ evolve into present-day ETGs. Above redshift 6, more than 80 per cent of the observed stellar mass functions above $10^{9}\mathrm{M}_\odot$ can be accounted for by ETG progenitors with $m_* > 10^{10}\mathrm{M}_\odot$. This implies that current and future high redshift observations mainly probe the birth of present-day ETGs. The source code and documentation of EMERGE are available at this http URL.
IPO: Interior-point Policy Optimization under Constraints

In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the objective with logarithmic barrier functions, inspired by the interior-point method. Our proposed method is easy to implement with performance guarantees and can handle general types of cumulative multi-constraint settings. We conduct extensive evaluations to compare our approach with state-of-the-art baselines. Our algorithm outperforms the baseline algorithms, in terms of reward maximization and constraint satisfaction.
Orthogonal Nonnegative Tucker Decomposition

In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrangian function to solve the optimization problem. The convergence of the algorithm is given. We employ ONTD on the image data sets from the real world applications including face recognition, image representation, hyperspectral unmixing. Numerical results are shown to illustrate the effectiveness of the proposed algorithm.
A Theory of Extended Working Memory and its Role in Online Conversation Dynamics

Caused by biological limits on cognition, information overload has shown to impact behavior of online social media users, affecting behavior in online conversations. We combine concepts from working memory theory with the extended self theory to formulate a novel theory of extended working memory. This theory is implemented as a mechanistic, agent-based model of information storage and attention loss under information overload and validated on the discussions of a cyrptocurrency community on Twitter. Results indicate that volume and participant count in individual conversations are sensitive to the rate of attention loss and extended working memory capacity. Further experiments allow extended working memory to replicate and explain several online conversation phenomena from the literature, previously lacking a mechanistic explanation. Reconceptualizing information spread through working memory and extended self theories, allows us to move beyond analogies to biologically-inspired viral spread, into richer representations of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media.
Improving Singing Voice Separation with the Wave-U-Net Using Minimum Hyperspherical Energy

In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation. The Wave-U-Net is a recent deep network architecture that operates directly on the time domain. The standard Wave-U- Net is trained with data augmentation and early stopping to prevent overfitting. Minimum hyperspherical energy (MHE) regularization has recently proven to increase generalization in image classification problems by encouraging a diversified filter configuration. In this work, we apply MHE regularization to the 1D filters of the Wave-U-Net. We evaluated this approach for separating the vocal part from mixed music audio recordings on the MUSDB18 dataset. We found that adding MHE regularization to the loss function consistently improves singing voice separation, as measured in the Signal to Distortion Ratio on test recordings, leading to the current best time-domain system for singing voice extraction.
Transverse magneto-optical Kerr effect enhanced at the bound states in the continuum

We calculate theoretically the transverse magneto-optical Kerr effect (TMOKE) in the periodically patterned waveguide made from a diluted magnetic semiconductor. It is demonstrated that the TMOKE is resonantly enhanced when the incident wave is in resonance with the bound state in continuum, arising when the quality factor of the waveguide mode increases due to the far-field interference. Our results uncover the potential of all-dielectric and semiconductor nanostructures for resonant magneto-optics.
Two-Step Sound Source Separation: Training On Learned Latent Targets

In this paper, we propose a two-step training procedure for source separation via a deep neural network. In the first step we learn a transform (and it’s inverse) to a latent space where masking-based separation performance using oracles is optimal. For the second step, we train a separation module that operates on the previously learned space. In order to do so, we also make use of a scale-invariant signal to distortion ratio (SI-SDR) loss function that works in the latent space, and we prove that it lower-bounds the SI-SDR in the time domain. We run various sound separation experiments that show how this approach can obtain better performance as compared to systems that learn the transform and the separation module jointly. The proposed methodology is general enough to be applicable to a large class of neural network end-to-end separation systems.
Is graph-based feature selection of genes better than random?

Gene interaction graphs aim to capture various relationships between genes and represent decades of biology research. When trying to make predictions from genomic data, those graphs could be used to overcome the curse of dimensionality by making machine learning models sparser and more consistent with biological common knowledge. In this work, we focus on assessing whether those graphs capture dependencies seen in gene expression data better than random. We formulate a condition that graphs should satisfy to provide a good prior knowledge and propose to test it using a `Single Gene Inference' (SGI) task. We compare random graphs with seven major gene interaction graphs published by different research groups, aiming to measure the true benefit of using biologically relevant graphs in this context. Our analysis finds that dependencies can be captured almost as well at random which suggests that, in terms of gene expression levels, the relevant information about the state of the cell is spread across many genes.
Stellar Proton Event-induced surface radiation dose as a constraint on the habitability of terrestrial exoplanets

The discovery of terrestrial exoplanets orbiting in habitable zones around nearby stars has been one of the significant developments in modern astronomy. More than a dozen such planets, like Proxima Centauri b and TRAPPIST-1 e, are in close-in configurations and their proximity to the host star makes them highly sensitive to stellar activity. Episodic events such as flares have the potential to cause severe damage to close-in planets, adversely impacting their habitability. Flares on fast rotating young M stars occur up to 100 times more frequently than on G-type stars which makes their planets even more susceptible to stellar activity. Stellar Energetic Particles (SEPs) emanating from Stellar Proton Events (SPEs) cause atmospheric damage (erosion and photochemical changes), and produce secondary particles, which in turn results in enhanced radiation dosage on planetary surfaces. We explore the role of SPEs and planetary factors in determining planetary surface radiation doses. These factors include SPE fluence and spectra, and planetary column density and magnetic field strength. Taking particle spectra from 70 major solar events (observed between 1956 and 2012) as proxy, we use the GEANT4 Monte Carlo model to simulate SPE interactions with exoplanetary atmospheres, and we compute surface radiation dose. We demonstrate that in addition to fluence, SPE spectrum is also a crucial factor in determining the surface radiation dose. We discuss the implications of these findings in constraining the habitability of terrestrial exoplanets.
Cross-task pre-training for acoustic scene classification

Acoustic scene classification(ASC) and acoustic event detection(AED) are different but related tasks. Acoustic scenes can be shaped by occurred acoustic events which can provide useful information in training ASC tasks. However, most of the datasets are provided without either the acoustic event or scene labels. Therefore, We explored cross-task pre-training mechanism to utilize acoustic event information extracted from the pre-trained model to optimize the ASC task. We present three cross-task pre-training architectures and evaluated them in feature-based and fine-tuning strategies on two datasets respectively: TAU Urban Acoustic Scenes 2019 dataset and TUT Acoustic Scenes 2017 dataset. Results have shown that cross-task pre-training mechanism can significantly improve the performance of ASC tasks and the performance of our best model improved relatively 9.5% in the TAU Urban Acoustic Scenes 2019 dataset, and also improved 10% in the TUT Acoustic Scenes 2017 dataset compared with the official baseline.
The Swax Benchmark: Attacking Biometric Systems with Wax Figures

A face spoofing attack occurs when an intruder attempts to impersonate someone who carries a gainful authentication clearance. It is a trending topic due to the increasing demand for biometric authentication on mobile devices, high-security areas, among others. This work introduces a new database named Sense Wax Attack dataset (SWAX), comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations. Experiments performed with baseline methods show that despite the progress in recent years, advanced spoofing methods are still vulnerable to high-quality violation attempts.