Miracle-dz/UniFAR-FTUs huggingface dataset:
Replication in Data Grids: Metrics and Strategies

We focus in this report on two main axes. The first is dedicated to the study of the effect of replicas distribution on data grid performances. In this respect, our main contributions are as follows: 1) An overview of replication strategies mainly from the viewpoints of the considered parameters in their associated steps as well as the used metrics in the literature for their evaluation. 2) A study of the impact of placement strategies on data grid performance which motivated the analysis of the effect of the replicas distribution quality on the performance results of replication strategies. 3) The proposal of new evaluation metrics dedicated to the evaluation of the distribution quality. 4) The setting of an objective evaluation of replication strategies which is based on a beforehand assessment of the distribution quality. The second axis is mainly dedicated to exploiting results of data mining techniques to enhance performances of replication strategies. With respect to this axis, we mainly concentrate on the following contributions listed below: 1) The study of the strengths and the drawbacks of the main replication strategies based on data mining techniques and how these latter are applied in this context. 2) The proposal of a new guideline to data mining application in the context of data grid replication strategies. 3) The proposal of a new algorithm for mining maximal frequent correlated patterns. The input of this algorithm is obtained through a preliminary step focusing on how to adapt the required grid concepts to the data mining algorithm. 4) The design and the implementation of a new replication strategy based on a data mining technique, and more precisely correlated patterns.
Back-action evading measurement of the collective mode of a Bose–Einstein condensate

In this paper, we investigate theoretically the back-action evading measurement of the collective mode of an interacting atomic Bose-Einstein condensate (BEC) trapped in an optical cavity which is driven coherently by a pump laser with a modulated amplitude. It is shown that for a specified kind of amplitude modulation of the driving laser, one can measure a generalized quadrature of the collective mode of the BEC indirectly through the output cavity field with a negligible backaction noise in the good-cavity limit. Nevertheless, the on-resonance added noise of measurement is suppressed below the standard quantum limit (SQL) even in the bad cavity limit. Moreover, the measurement precision can be controlled through the s-wave scattering frequency of atomic collisions.
AdvCodec: Towards A Unified Framework for Adversarial Text Generation

While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio) to explore the model vulnerabilities, generating \emph{adversarial text} in the discrete domain is still challenging. The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP) tasks. In particular, we propose a tree-based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation. A tree-based decoder is then applied to ensure the grammar correctness of the generated text. It also enables the flexibility of making manipulations on different levels of text, such as sentence (AdvCodec(sent)) and word (AdvCodec(word)) levels. We consider multiple attacking scenarios, including appending an adversarial sentence or adding unnoticeable words to a given paragraph, to achieve the arbitrary targeted attack. To demonstrate the effectiveness of the proposed method, we consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that AdvCodec generated adversarial text can successfully attack the neural models without misleading the human. In particular, our attack causes a BERT-based sentiment classifier accuracy to drop from 0.703$ to 0.006, and a BERT-based QA model's F1 score to drop from 88.62 to 33.21 (with best targeted attack F1 score as 46.54). Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective way to examine the robustness of existing NLP models.
Saliency Based Fire Detection Using Texture and Color Features

Due to industry deployment and extension of urban areas, early warning systems have an essential role in giving emergency. Fire is an event that can rapidly spread and cause injury, death, and damage. Early detection of fire could significantly reduce these injuries. Video-based fire detection is a low cost and fast method in comparison with conventional fire detectors. Most available fire detection methods have a high false-positive rate and low accuracy. In this paper, we increase the accuracy by using spatial and temporal features. Captured video sequences are divided into Spatio-temporal blocks. Then a saliency map and combination of color and texture features are used for detecting fire regions. We use the HSV color model as a spatial feature and LBP-TOP for temporal processing of fire texture. Fire detection tests on publicly available datasets have shown the robustness and accuracy of the algorithm.
Jucys-Murphy elements of partition algebras for the rook monoid

Kudryavtseva and Mazorchuk exhibited Schur–Weyl duality between the rook monoid algebra [Formula: see text] and the subalgebra [Formula: see text] of the partition algebra [Formula: see text] acting on [Formula: see text]. In this paper, we consider a subalgebra [Formula: see text] of [Formula: see text] such that there is Schur–Weyl duality between the actions of [Formula: see text] and [Formula: see text] on [Formula: see text]. This paper studies the representation theory of partition algebras [Formula: see text] and [Formula: see text] for rook monoids inductively by considering the multiplicity free tower [Formula: see text] Furthermore, this inductive approach is established as a spectral approach by describing the Jucys–Murphy elements and their actions on the canonical Gelfand–Tsetlin bases, determined by the aforementioned multiplicity free tower, of irreducible representations of [Formula: see text] and [Formula: see text]. Also, we describe the Jucys–Murphy elements of [Formula: see text] which play a central role in the demonstration of the actions of Jucys–Murphy elements of [Formula: see text] and [Formula: see text].
How to Teach AI to Play Bell Non-Local Games: Reinforcement Learning

Motivated by the recent success of reinforcement learning in games such as Go and Dota2, we formulate Bell non-local games as a reinforcement learning problem. Such a formulation helps us to explore Bell non-locality in a range of scenarios. The measurement settings and the quantum states are selected by the learner randomly in the beginning. Still, eventually, it starts understanding the underlying patterns and discovers an optimal (or near-optimal) quantum configuration corresponding to the task at hand. We provide a proof of principle approach to learning quantum configurations for violating various Bell inequalities. The algorithm also works for non-convex optimization problems where convex methods fail, thus offering a possibility to explore optimal quantum configurations for Bell inequalities corresponding to large quantum networks. We also implement a hybrid quantum-classical variational algorithm with reinforcement learning.
DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Neural network pruning is one of the most popular methods of accelerating the inference of deep convolutional neural networks (CNNs). The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time. However, we argue that they are not equivalent if parallel computing is considered. Given that filter-level pruning only prunes filters in layers and computations in a layer usually run in parallel, most computations reduced by filter-level pruning usually run in parallel with the un-reduced ones. Thus, the acceleration ratio of filter-level pruning is limited. To get a higher acceleration ratio, it is better to prune redundant layers because computations of different layers cannot run in parallel. In this paper, we propose our Discrimination based Block-level Pruning method (DBP). Specifically, DBP takes a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and removes redundant blocks according to the discrimination of their output features. As a result, DBP achieves a considerable acceleration ratio by reducing the depth of CNNs. Extensive experiments show that DBP has surpassed state-of-the-art filter-level pruning methods in both accuracy and acceleration ratio. Our code will be made available soon.
Unveiling the Importance of Magnetic Fields in the Evolution of Dense Clumps Formed at the Waist of Bipolar H ii Regions: A Case Study of Sh 2-201 with JCMT SCUBA-2/POL-2

We present the properties of magnetic fields (B fields) in two clumps (clump 1 and clump 2), located at the waist of the bipolar H ii region Sh 2-201, based on James Clerk Maxwell Telescope SCUBA-2/POL-2 observations of 850 μm polarized dust emission. We find that B fields in the direction of the clumps are bent and compressed, showing bow-like morphologies, which we attribute to the feedback effect of the H ii region on the surface of the clumps. Using the modified Davis–Chandrasekhar–Fermi method, we estimate B-field strengths of 266 and 65 μG for clump 1 and clump 2, respectively. From virial analyses and critical mass ratio estimates, we argue that clump 1 is gravitationally bound and could be undergoing collapse, whereas clump 2 is unbound and stable. We hypothesize that the interplay of the thermal pressure imparted by the H ii region, the B-field morphologies, and the various internal pressures of the clumps (such as magnetic, turbulent, and gas thermal pressures) has the following consequences: (a) formation of clumps at the waist of the H ii region; (b) progressive compression and enhancement of the B fields in the clumps; (c) stronger B fields that will shield the clumps from erosion by the H ii region and cause pressure equilibrium between the clumps and the H ii region, thereby allowing expanding ionization fronts to blow away from the filament ridge, forming bipolar H ii regions; and (d) stronger B fields and turbulence that will be able to stabilize the clumps. A study of a larger sample of bipolar H ii regions would help to determine whether our hypotheses are widely applicable.
Joint Forward-Backward Visual Odometry for Stereo Cameras

Visual odometry is a widely used technique in the field of robotics and automation to keep a track on the location of a robot using visual cues alone. In this paper, we propose a joint forward-backward visual odometry framework by combining both, the forward motion and backward motion estimated from stereo cameras. The basic framework of LIBVIOS2 is used here for pose estimation as it can run in real-time on standard CPUs. The complementary nature of errors in the forward and backward mode of visual odometry helps in providing a refined motion estimation upon combining these individual estimates. In addition, two reliability measures, that is, forward-backward relative pose error and forward-backward absolute pose error have been proposed for evaluating visual odometry frameworks on its own without the requirement of any ground truth data. The proposed scheme is evaluated on the KITTI visual odometry dataset. The experimental results demonstrate improved accuracy of the proposed scheme over the traditional odometry pipeline without much increase in the system overload.
Modelling basketball players' performance and interactions between teammates with a regime switching approach.

Basketball players' performance measurement is of critical importance for a broad spectrum of decisions related to training and game strategy. Despite this recognized central role, the main part of the studies on this topic focus on performance level measurement, neglecting other important characteristics, such as variability. In this paper, shooting performance variability is modeled with a Markov Switching dynamic, assuming the existence of two alternating performance regimes. Then, the relationships between each player's variability and the lineup composition is modeled as an ARIMA process with covariates and described with network analysis tools, in order to extrapolate positive and negative interactions between teammates
Quasistatic evolution for dislocation-free finite plasticity

We investigate quasistatic evolution in finite plasticity under the assumption that the plastic strain is compatible. This assumption is well-suited to describe the special case of dislocation-free plasticity and entails that the plastic strain is the gradient of a plastic deformation map. The total deformation can be then seen as the composition of a plastic and an elastic deformation. This opens the way to an existence theory for the quasistatic evolution problem featuring both Lagrangian and Eulerian variables. A remarkable trait of the result is that it does not require second-order gradients.
Dispel: Byzantine SMR with Distributed Pipelining

Byzantine State Machine Replication (SMR) is a long studied topic that received increasing attention recently with the advent of blockchains as companies are trying to scale them to hundreds of nodes. Byzantine SMRs try to increase throughput by either reducing the latency of consensus instances that they run sequentially or by reducing the number of replicas that send messages to others in order to reduce the network usage. Unfortunately, the former approach makes use of resources in burst whereas the latter requires CPU-intensive authentication mechanisms. In this paper, we propose a new Byzantine SMR called Dispel (Distributed Pipeline) that allows any node to distributively start new consensus instances whenever they detect sufficient resources locally. We evaluate the performance of Dispel within a single datacenter and across up to 380 machines over 3 continents by comparing it against four other SMRs. On 128 nodes, Dispel speeds up HotStuff, the Byzantine fault tolerant SMR being integrated within Facebook's blockchain, by more than 12 times. In addition, we also test Dispel under isolated and correlated failures and show that the Dispel distributed design is more robust than HotStuff. Finally, we evaluate Dispel in a cryptocurrency application with Bitcoin transactions and show that this SMR is not the bottleneck.
Comparison of Escherichia Coli Strains Recovered From Human Cystitis and Pyelonephritis Infections in Transurethrally Challenged Mice

Editorial Comment: Nonsteroidal anti-inflammatory drugs are often used to potentiate the effect of narcotics. The authors compared nonsteroidal anti-inflammatory drugs and acetaminophen as an adjunct to morphine for postoperative pain control. Interestingly, acetaminophen was more effective in this trial. There was a statistically significant difference between the 2 treatments but the number of patients studied was small. Undoubtedly, there will be continued evaluation of the best drug combination for pain control after surgery, especially considering the emergence of new analgesics. Joseph A. Smith, Jr., M.D.
Recommendations and user agency: the reachability of collaboratively-filtered information

Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. In this work, we consider directly the information availability problem through the lens of user recourse. Using ideas of reachability, we propose a computationally efficient audit for top-N linear recommender models. Furthermore, we describe the relationship between model complexity and the effort necessary for users to exert control over their recommendations. We use this insight to provide a novel perspective on the user cold-start problem. Finally, we demonstrate these concepts with an empirical investigation of a state-of-the-art model trained on a widely used movie ratings dataset.
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.
The Arbitrarily Varying Channel With Colored Gaussian Noise

We address the arbitrarily varying channel (AVC) with colored Gaussian noise. The work consists of three parts. First, we study the general discrete AVC with fixed parameters, a model that combines the AVC and the time-varying channel. We determine both the deterministic code capacity and the random code capacity. Super-additivity is demonstrated, showing that the deterministic code capacity can be strictly larger than the weighted sum of the parametric capacities. In the second part, we consider the arbitrarily varying Gaussian product channel (AVGPC). Hughes and Narayan characterized the random code capacity through min-max optimization leading to a “double” water filling solution. As in the case of the standard Gaussian AVC, the deterministic code capacity is discontinuous in the input constraint, and depends on which of the input or state constraint is higher. As opposed to Shannon’s classic water filling solution, it is observed that deterministic coding using independent scalar codes is suboptimal for the AVGPC. Finally, we establish the capacity of the AVC with colored Gaussian noise, where double water filling is performed in the frequency domain. The analysis relies on our preceding results, on the AVC with fixed parameters and the AVGPC.
Exact minimax risk for linear least squares, and the lower tail of sample covariance matrices

The first part of this paper is devoted to the decision-theoretic analysis of random-design linear prediction. It is known that, under boundedness constraints on the response (and thus on regression coefficients), the minimax excess risk scales, up to constants, as $\sigma^2 d / n$ in dimension $d$ with $n$ samples and noise $\sigma^2$. Here, we study the expected excess risk with respect to the full linear class. We show that the ordinary least squares estimator is exactly minimax optimal in the well-specified case for every distribution of covariates. Further, we express the minimax risk in terms of the distribution of \emph{statistical leverage scores} of individual samples. We deduce a precise minimax lower bound of $\sigma^2d/(n-d+1)$ for general covariate distribution, which nearly matches the risk for Gaussian design. We then obtain nonasymptotic upper bounds on the minimax risk for covariates that satisfy a "small ball"-type regularity condition, which scale as $(1+o(1))\sigma^2d/n$ as $d=o(n)$, both in the well-specified and misspecified cases. Our main technical contribution is the study of the lower tail of the smallest singular value of empirical covariance matrices around $0$. We establish a lower bound on this lower tail, valid for any distribution in dimension $d \geq 2$, together with a matching upper bound under a necessary regularity condition. Our proof relies on the PAC-Bayesian technique for controlling empirical processes, and extends an analysis of Oliveira devoted to a different part of the lower tail. Equivalently, our upper bound shows that the operator norm of the inverse sample covariance matrix has bounded $L^q$ norm up to $q \asymp n$, and our lower bound implies that this exponent is unimprovable. Finally, we show that the regularity condition naturally holds for independent coordinates.
Picosecond spin-orbit torque switching of ferrimagnets

Spintronics provides an efficient platform for realizing non-volatile memory and logic devices. In these systems, data is stored in the magnetization of magnetic materials, and magnetization is switched in the writing process. In conventional spintronic devices, ferromagnetic materials are used which have a magnetization dynamics timescale of around the nanoseconds, setting a limit for the switching speed. Increasing the magnetization switching speed has been one of the challenges in spintronic research. In this work we take advantage of the ultrafast magnetization dynamics in ferrimagnetic materials instead of ferromagnets, and we use femtosecond laser pulses and a photoconductive Auston switch to create picosecond current pulses for switching the ferrimagnet. By anomalous Hall and magneto-optic Kerr (MOKE) measurement, we demonstrate the robust picosecond SOT driven magnetization switching of ferrimagnetic GdFeCo. The time-resolved MOKE shows more than 50 GHz magnetic resonance frequency of GdFeCo, indicating faster than 20 ps spin dynamics and tens of picosecond SOT switching speed. Our work provides a promising route to realize picosecond operation speed for non-volatile magnetic memory and logic applications.
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario.

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train convolutional neural networks (CNN) to classify image patches, but they are computationally expensive. In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection. Specifically, we utilize the state-of-the-art two-stage object detection method, the Faster-RCNN with Feature Pyramid Network (FPN) as the baseline and propose a novel comparison detector to deal with the limited data problem. The key idea is that classify the proposals by comparing with the reference samples of each category in object detection. In addition, we propose to learn the reference samples of the background from data instead of manually choosing them by some heuristic rules. Experimental results show that the proposed Comparison Detector yields significant improvement on the small dataset, achieving a mean Average Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improving about 20 points compared to the baseline. Moreover, Comparison Detector improved AR by 4.6 points and achieved marginally better performance in terms of mAP compared with baseline model when training on the medium dataset. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code is available at https://github.com/kuku-sichuan/ComparisonDetector.