Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Forward Reuse Distance

Caching techniques are widely used in the era of cloud computing from applications, such as Web caches to infrastructures, Memcached and memory caches in computer architectures. Prediction of cached data can greatly help improve cache management and performance. The recent advancement of deep learning techniques enables the design of novel intelligent cache replacement policies. In this work, we propose a learning-aided approach to predict future data accesses. We find that a powerful LSTM-based recurrent neural network model can provide high prediction accuracy based on only a cache trace as input. The high accuracy results from a carefully crafted locality-driven feature design. Inspired by the high prediction accuracy, we propose a pseudo OPT policy and evaluate it upon 13 real-world storage workloads from Microsoft Research. Results demonstrate that the new cache policy improves state-of-art practical policies by up to 19.2% and incurs only 2.3% higher miss ratio than OPT on average.
Efficient State Estimation for Gas Networks via Low-Rank Approximations

Modeling physical applications often leads to systems of very high dimension. For such large-scale systems model order reduction has turned out to be an advantageous way to calculate desired solutions with much less computational effort. In this paper we will make use of such a low-rank model to reconstruct the system state by help of the Kalman Filter. This filter is known to be unfeasible for high-dimensional systems. For that reason in literature have risen low-rank Kalman filters which we will compare to our proposed approach. We will study the performance of the methods in terms of quality of the estimate and efficiency at the application of a gas pipeline network.
X-ray evolution of the nova V959 Mon suggests a delayed ejection and a non-radiative shock

X-ray observations of shocked gas in novae can provide a useful probe of the dynamics of the ejecta. Here we report on X-ray observations of the nova V959 Mon, which was also detected in GeV gamma-rays with the Fermi satellite. We find that the X-ray spectra are consistent with a two-temperature plasma model with non-solar abundances. We interpret the X-rays as due to shock interaction between the slow equatorial torus and the fast polar outflow that were inferred from radio observations of V959 Mon. We further propose that the hotter component, responsible for most of the flux, is from the reverse shock driven into the fast outflow. We find a systematic drop in the column density of the absorber between Days 60 and 140, consistent with the expectations for such a picture. We present intriguing evidence for a delay of around 40 days in the expulsion of the ejecta from the central binary. Moreover, we infer a relatively small (a few times 10 M⊙) ejecta mass ahead of the shock, considerably lower than the mass of 10 K gas inferred from radio observations. Finally, we infer that the dominant X-ray shock was likely not radiative at the time of our observations, and that the shock power was considerably higher than the observed X-ray luminosity. It is unclear why high X-ray luminosity, closer to the inferred shock power, is never seen in novae at early times, when the shock is expected to have high enough density to be radiative.
G-CREWE: Graph CompREssion With Embedding for Network Alignment

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However, the process of aligning every pair of nodes between relatively large networks is time-consuming and resource-intensive. In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem. G-CREWE uses node embeddings to align the networks on two levels of resolution, a fine resolution given by the original network and a coarse resolution given by a compressed version, to achieve an efficient and effective network alignment. The framework first extracts node features and learns the node embedding via a Graph Convolutional Network (GCN). Then, node embedding helps to guide the process of graph compression and finally improve the alignment performance. As part of G-CREWE, we also propose a new compression mechanism called MERGE (Minimum DEgRee NeiGhbors ComprEssion) to reduce the size of the input networks while preserving the consistency in their topological structure. Experiments on all real networks show that our method is more than twice as fast as the most competitive existing methods while maintaining high accuracy.
Graph Signal Processing for Machine Learning: A Review and New Perspectives

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on the future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.
X-ray study of the merging galaxy cluster Abell 3411-3412 with XMM-Newton and Suzaku

Context. Previous Chandra observations of the Abell 3411-3412 merging galaxy cluster system revealed an outbound bullet-like sub-cluster in the northern part and many surface brightness edges at the southern periphery, where multiple diffuse sources are also reported from radio observations. Notably, a southeastern radio relic associated with fossil plasma from a radio galaxy and with a detected X-ray edge provides direct evidence of shock re-acceleration. The properties of the reported surface brightness features have yet to be constrained from a thermodynamic viewpoint. Aims. We use the XMM-Newton and Suzaku observations of Abell 3411-3412 to reveal the thermodynamical nature of the previously reported re-acceleration site and other X-ray surface brightness edges. We also aim to investigate the temperature profile in the low-density outskirts with Suzaku data. Methods. We performed both imaging and spectral analysis to measure the density jump and the temperature jump across multiple known X-ray surface brightness discontinuities. We present a new method to calibrate the vignetting function and spectral model of the XMM-Newton soft proton background. Archival Chandra, Suzaku, and ROSAT data are used to estimate the cosmic X-ray background and Galactic foreground levels with improved accuracy compared to standard blank sky spectra. Results. At the southeastern edge, temperature jumps revealed by both XMM-Newton and Suzaku point to a ℳ ∼ 1.2 shock, which agrees with the previous result from surface brightness fits with Chandra. The low Mach number supports the re-acceleration scenario at this shock front. The southern edge shows a more complex scenario, where a shock and the presence of stripped cold material may coincide. There is no evidence for a bow shock in front of the northwestern “bullet” sub-cluster. The Suzaku temperature profiles in the southern low-density regions are marginally higher than the typical relaxed cluster temperature profile. The measured value kT500 = 4.84 ± 0.04 ± 0.19 keV with XMM-Newton and kT500 = 5.17 ± 0.07 ± 0.13 keV with Suzaku are significantly lower than previously inferred from Chandra.
Scheme for sub-shot-noise transmission measurement using a time-multiplexed single-photon source

A rather promising result from optical quantum metrology is the ability to achieve sub-shot-noise performance in transmission/absorption measurements. This is due to the significantly lower uncertainty in light intensity of quantum beams with respect to their classical counterparts. In this work, we simulate results from an experiment that uses a multiplexed single-photon source based on pair generation by continuous spontaneous parametric down conversion (SPDC) and a subsequent time multiplexing set-up with a binary division strategy, considering several types of experimental losses. In such source, the sub-Poissonian statistics of the output signal is the key for achieving sub-shot-noise performance. We compare the numerical results with two paradigmatic limits: the shot-noise limit (achieved with coherent sources) and the ultimate quantum limit (achieved with an ideal photon-number Fock state). We also investigate conditions in which threshold detectors can be used and the effect of input light fluctuations in the measurement error. Results show that sub-shot-noise performance can be achieved, even without using number-resolving detectors, with improvement factors that range from 1.5 to 2. This technique would allow transmission/absorption measurements with reasonable uncertainty using ultra-low light intensity and minimum disruption of biological or other fragile samples.
Load Plus Communication Balancing in Contiguous Partitions for Distributed Sparse Matrices: Linear-Time Algorithms

We study partitioning to parallelize multiplication of one or more dense vectors by a sparse matrix (SpMV or SpMM). We consider contiguous partitions, where the rows (or columns) of the matrix are split into $K$ parts without reordering. We present exact and approximate contiguous partitioning algorithms that minimize the runtime of the longest-running processor under cost models that combine work factors and hypergraph communication factors. This differs from traditional graph or hypergraph partitioning models which minimize total communication under a work balance constraint. We address regimes where partitions of the row space and column space are expected to match (the symmetric case) or are allowed to differ (the nonsymmetric case). Our algorithms use linear space. Our exact algorithm runs in linear time when $K^2$ is sublinear. Our $(1 + \epsilon)$-approximate algorithm runs in linear time when $K\log(1/\epsilon)$ is sublinear. We combine concepts from high-performance computing and computational geometry. Existing load balancing algorithms optimize a linear model of per-processor work. We make minor adaptations to optimize arbitrary nonuniform monotonic increasing or decreasing cost functions which may be expensive to evaluate. We then show that evaluating our model of communication is equivalent to planar dominance counting. We specialize Chazelle's dominance counting algorithm to points in the bounded integer plane and generalize it to trade reduced construction time for increased query time, since our partitioners make very few queries. Our algorithms split the original row (or column) ordering into parts to optimize diverse cost models. Combined with reordering or embedding techniques, our algorithms might be used to build more general heuristic partitioners, as they can optimally round one-dimensional embeddings of direct $K$-way noncontiguous partitioning problems.
The search for failed supernovae with the Large Binocular Telescope: N6946-BH1, still no star

We present new Large Binocular Telescope, Hubble Space Telescope, and Spitzer Space Telescope data for the failed supernova candidate N6946-BH1. We also report an unsuccessful attempt to detect the candidate with Chandra. The ∼300 000 $\, \mathrm{L}_\odot$ red supergiant progenitor underwent an outburst in 2009 and has since disappeared in the optical. In the LBT data from 2008 May through 2019 October, the upper limit on any increase in the R-band luminosity of the source is $2000 \, \mathrm{L}_\odot$. HST and Spitzer observations show that the source continued to fade in the near-IR and mid-IR, fading by approximately a factor of 2 between 2015 October and 2017 September to 2900 $\, \mathrm{L}_\odot$ at Hband (F160W). Models of the spectral energy distribution are inconsistent with a surviving star obscured either by an ongoing wind or dust formed in the transient. The disappearance of N6946-BH1 remains consistent with a failed supernova, but the post-failure phenomenology requires further theoretical study.
THE USE OF SPACE BY WINTERING SPARROWS

Differences in microhabitat utilization by sparrows wintering in southeastern Arizona, USA may be sufficient to explain their coexistence. The species either forage in different mac- rohabitats or in the same habitat at different distances from tree or shrub cover. Seed size partitioning within habitats is sufficient to explain coexistence only in one case where the seed size distribution is bimodal. The species that forage in plains grassland habitat differ by foraging in concentric rings about tree or shrub cover. The behavior of each of these species appears to enhance predator avoidance and these behaviors change in a regular pattern with distance to cover both within and between species. Species foraging close to cover are social and tend to be conspicuous both in behavior and morphol- ogy. Two different strategies adapt species to avoiding predators at great distances to cover. In dense grassland, with increasing distance to shrub or tree cover, species in the genus Ammodramus are increasingly solitary and cryptic. In sparse grassland, Chestnut-collared Longspurs are social and cryptic on the ground but conspicuous in flight.
Diffusive Hydrogenation Reactions of CO Embedded in Amorphous Solid Water at Elevated Temperatures ∼70 K

The surface processes on interstellar dust grains have an important role in the chemical evolution in molecular clouds. Hydrogenation reactions on ice surfaces have been extensively investigated and are known to proceed at low temperatures mostly below 20 K. In contrast, information about the chemical processes of molecules within an ice mantle is lacking. In this work, we investigated diffusive hydrogenation reactions of carbon monoxide (CO) embedded in amorphous solid water (ASW) as a model case and discovered that the hydrogenation of CO efficiently proceeds to yield H2CO and CH3OH even above 20 K when CO is buried beneath ASW. The experimental results suggest that hydrogen atoms diffuse through the cracks of ASW and have a sufficient residence time to react with embedded CO. The hydrogenation reactions occurred even at temperatures up to ∼70 K. Cracks collapse at elevated temperatures but the occurrence of hydrogenation reactions means that the cracks would not completely disappear and remain large enough for penetration by hydrogen atoms. Considering the hydrogen-atom fluence in the laboratory and molecular clouds, we suggest that the penetration of hydrogen and its reactions within the ice mantle occur in astrophysical environments.
RAC1 Inhibition Targets Amyloid Precursor Protein Processing by γ-Secretase and Decreases Aβ Production in Vitro and in Vivo*

β-Amyloid peptides (Aβ) that form the senile plaques of Alzheimer disease consist mainly of 40- and 42-amino acid (Aβ 40 and Aβ 42) peptides generated from the cleavage of the amyloid precursor protein (APP). Generation of Aβ involves β-secretase and γ-secretase activities and is regulated by membrane trafficking of the proteins involved in Aβ production. Here we describe a new small molecule, EHT 1864, which blocks the Rac1 signaling pathways. In vitro, EHT 1864 blocks Aβ 40 and Aβ 42 production but does not impact sAPPα levels and does not inhibit β-secretase. Rather, EHT 1864 modulates APP processing at the level of γ-secretase to prevent Aβ 40 and Aβ 42 generation. This effect does not result from a direct inhibition of the γ-secretase activity and is specific for APP cleavage, since EHT 1864 does not affect Notch cleavage. In vivo, EHT 1864 significantly reduces Aβ 40 and Aβ 42 levels in guinea pig brains at a threshold that is compatible with delaying plaque accumulation and/or clearing the existing plaque in brain. EHT 1864 is the first derivative of a new chemical series that consists of candidates for inhibiting Aβ formation in the brain of AD patients. Our findings represent the first pharmacological validation of Rac1 signaling as a target for developing novel therapies for Alzheimer disease.
Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress

Psychological distress is a significant and growing issue in society. In particular, depression and anxiety are leading causes of disability that often go undetected or late-diagnosed. Automatic detection, assessment, and analysis of behavioural markers of psychological distress can help improve identification and support prevention and early intervention efforts. Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse, which is partly due to the limited available datasets and difficulty in automatically extracting useful body features. To enable our research, we have collected and analyzed a new dataset containing full body videos for interviews and self-reported distress labels. We propose a novel approach to automatically detect self-adaptors and fidgeting, a subset of self-adaptors that has been shown to correlate with psychological distress. We perform analysis on statistical body gestures and fidgeting features to explore how distress levels affect behaviors. We then propose a multi-modal approach that combines different feature representations using Multi-modal Deep Denoising Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our proposed model, combining audio-visual features with detected fidgeting behavioral cues, can successfully predict depression and anxiety in the dataset.
5G Network Planning Under Service and EMF Constraints: Formulation and Solutions

We target the planning of a 5G cellular network under 5G service and ElectroMagnetic Fields (EMFs) constraints. We initially model the problem with a mixed integer linear programming (MILP) formulation. The pursued objective is a weighed function of next-generation Node-B (gNB) installation costs and 5G service coverage level from a massive multiple input multiple output (MIMO) system. In addition, we precisely model restrictive EMF constraints and we integrate scaling parameters to estimate the power radiated by 5G gNBs. Since the considered planning problem is NP-Hard, and therefore very challenging to be solved even for small problem instances, we design an efficient heuristic, called PLATEA, to practically solve it. Results, obtained over a realistic scenario that includes EMF exposure from pre-5G technologies (e.g., 2G, 3G, 4G), prove that the cellular planning selected by PLATEA ensures 5G service and restrictive EMF constraints. However, we demonstrate that the results are strongly affected by: i) the relative weight between gNB installation costs and 5G service coverage level; ii) the scaling parameters to estimate the exposure generated by 5G gNBs; iii) the amount of exposure from pre-5G technologies; and iv) the adopted frequency reuse scheme.
On Learning Universal Representations Across Languages

Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, existing approaches essentially capture the co-occurrence among tokens through involving the masked language model (MLM) objective with token-level cross entropy. In this work, we extend these approaches to learn sentence-level representations, and show the effectiveness on cross-lingual understanding and generation. We propose Hierarchical Contrastive Learning (HiCTL) to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence. We conduct evaluations on three benchmarks: language understanding tasks (QQP, QNLI, SST-2, MRPC, STS-B and MNLI) in the GLUE benchmark, cross-lingual natural language inference (XNLI) and machine translation. Experimental results show that the HiCTL obtains an absolute gain of 1.0%/2.2% accuracy on GLUE/XNLI as well as achieves substantial improvements of +1.7-+3.6 BLEU on both the high-resource and low-resource English-to-X translation tasks over strong baselines. We will release the source codes as soon as possible.
Language Modelling for Source Code with Transformer-XL

It has been found that software, like natural language texts, exhibits "naturalness", which can be captured by statistical language models. In recent years, neural language models have been proposed to represent the naturalness of software through deep learning. In this paper, we conduct an experimental evaluation of state-of-the-art neural language models for source code, including RNN-based models and Transformer-XL based models. Through experiments on a large-scale Python code corpus, we find that the Transformer-XL model outperforms RNN-based models (including LSTM and GRU models) in capturing the naturalness of software, with far less computational cost.
Dynamical structure factors of a two-dimensional Fermi superfluid within random phase approximation

Based on random phase approximation, we numerically calculate dynamical structure factors of a balanced two-dimensional (2D) Fermi superfluid, and discuss their energy, momentum and interaction strength dependence in the 2D BEC–BCS crossover. At a small transferred momentum, a stable Higgs mode is observed in the unitary 2D Fermi superfluid gas where the particle–hole symmetry is not satisfied. Stronger interaction strength will make the visibility of the dispersion of Higgs mode harder to be observed. We also discuss the dimension effect and find that the signal of the Higgs mode in two dimension is more obvious than that in 3D case. At a large transferred momentum regime, stronger interaction strength will induce the weight of the molecules excitation increasing, while in verse the atomic one decreasing, which shows the pairing information of Fermi superfluid. The theoretical results qualitatively agree with the corresponding quantum Monte Carlo data.
A Consensus Protocol for e-Democracy

Given that Proof-of-Work (PoW) and Proof-of-Stake (PoS) are plutocratic, and other common consensus protocols are mostly permission-based, we look for a consensus protocol that will suit the needs of e-Democracy. In particular, what we need is a distributed ledger that will record and, to the possible extent, execute the public will. We propose a combination of any given permission-based protocol together with a trust graph between the nodes, which supplies the required permission for new nodes. As a result, the consensus protocol reaches consensus at every iteration between a known list of agents and then updates this list between iterations. This paper is based on prior work that shows the conditions under which a community can grow while maintaining a bounded number of byzantines. It combines a permission-based consensus protocol (such as pBFT) with a community expansion algorithm (such as the one in the prior work) to arrive at a consensus protocol in which the set of agents can change in time, while being sybil-resilient.