Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Detect Malicious Clients for Robust Federated Learning

Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.
Linearly Convergent Frank-Wolfe with Backtracking Line-Search.

Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor local convergence properties, the Away-steps and Pairwise FW variants have emerged as improved variants with faster convergence. However, these improved variants suffer from two practical limitations: they require at each iteration to solve a 1-dimensional minimization problem to set the step-size and also require the Frank-Wolfe linear subproblems to be solved exactly. In this paper, we propose variants of Away-steps and Pairwise FW that lift both restrictions simultaneously. The proposed methods set the step-size based on a sufficient decrease condition, and do not require prior knowledge of the objective. Furthermore, they inherit all the favorable convergence properties of the exact line-search version, including linear convergence for strongly convex functions over polytopes. Benchmarks on different machine learning problems illustrate large performance gains of the proposed variants.
Some positivity results for toric vector bundles

We give a criterion for a projectivized toric vector bundle to be a Mori dream space and describe its Cox ring using generators and relations. Both of these results are in terms of the matroids of all symmetric powers of the bundle. We also give a criterion for a toric vector bundle to be big and describe several interesting examples of toric vector bundles which highlights how positivity properties for toric vector bundles are more complicated than for toric line bundles.
The Alcock Paczynski test with voids in 21 cm intensity field

Feasibility of the Alcock Paczynski (AP) test by stacking voids in the 21 cm line intensity field is presented. We analyse the IllustrisTNG simulation to obtain the 21 cm signal map. We then randomly distribute particles depending on the 21 cm intensity field to find voids by using publicly available code, VIDE. As in the galaxy clustering, the shape of the stacked void in the 21 cm field is squashed along the line of sight due to the peculiar velocities in redshift space, although it becomes spherical in real space. The redshift-space distortion for the stacked void weakly depends on redshift and we show that the dependency can be well described by the linear prediction, with the amplitude of the offset being free parameters. We find that the AP test using the stacked voids in a 21 cm intensity map is feasible and the parameter estimation on Ωm and w is unbiased.
Analysis and Optimization of an Intelligent Reflecting Surface-Assisted System With Interference

In this article, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). We investigate two cases of CSI at the BSs, namely, the instantaneous CSI case and the statistical CSI case, and apply Maximum Ratio Transmission (MRT) based on the complete CSI and the CSI of the Line-of-sight (LoS) components, respectively. Different costs on channel estimation and beamforming adjustment are incurred in the two CSI cases. First, we obtain a tractable expression of the average rate in the instantaneous CSI case and a tractable expression of the ergodic rate in the statistical CSI case. We also provide sufficient conditions for the average rate in the instantaneous CSI case to surpass the ergodic rate in the statistical CSI case, at any phase shifts. Then, we maximize the average rate and ergodic rate, both with respect to the phase shifts, leading to two non-convex optimization problems. For each problem, we obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD) to obtain a stationary point under arbitrary system parameters. Next, in each CSI case, we provide sufficient conditions under which the optimal quasi-static phase shift design is beneficial, compared to the system without IRS. Finally, we numerically verify the analytical results and demonstrate notable gains of the proposal solutions over existing ones. To the best of our knowledge, this is the first work that considers optimal quasi-static phase shift design for an IRS-assisted system in the presence of interference.
Robust saliency maps with decoy-enhanced saliency score

Saliency methods help to make deep neural network predictions more interpretable by identifying particular features, such as pixels in an image, that contribute most strongly to the network's prediction. Unfortunately, recent evidence suggests that many saliency methods perform poorly when gradients are saturated or in the presence of strong inter-feature dependence or noise injected by an adversarial attack. In this work, we propose to infer robust saliency scores by integrating the saliency scores of a set of decoys with a novel decoy-enhanced saliency score, in which the decoys are generated by either solving an optimization problem or blurring the original input. We theoretically analyze that our method compensates for gradient saturation and considers joint activation patterns of pixels. We also apply our method to three different CNNs---VGGNet, AlexNet, and ResNet trained on ImageNet data set. The empirical results show both qualitatively and quantitatively that our method outperforms raw scores produced by three existing saliency methods, even in the presence of adversarial attacks.
Effects of symmetry energy on the radius and tidal deformability of neutron stars in the relativistic mean-field model

The radii and tidal deformabilities of neutron stars are investigated in the framework of relativistic mean-field (RMF) model with different density-dependent behaviors of symmetry energy. To study the effects of symmetry energy on the properties of neutron stars, an $\omega$ meson and $\rho$ meson coupling term is included in a popular RMF Lagrangian, i.e. the TM1 parameter set, which is used for the widely used supernova equation of state (EoS) table. The coupling constants relevant to the vector-isovector meson, $\rho$, are refitted by a fixed symmetry energy at subsaturation density and its slope at saturation density, while other coupling constants remain the same as the original ones in TM1 so as to update the supernova EoS table. The radius and mass of maximum neutron stars are not so sensitive to the symmetry energy in these family TM1 parameterizations. However, the radii at intermediate mass region are strongly correlated with the slope of symmetry energy. Furthermore, the dimensionless tidal deformabilities of neutron stars are also calculated within the associated Love number. We find that its value at $1.4 M_\odot$ has a linear correlation to the slope of symmetry energy being different from the previous studied. With the latest constraints of tidal deformabilities from GW170817 event, the slope of symmetry energy at nuclear saturation density should be smaller than $60$ MeV in the family TM1 parameterizations. This fact supports the usage of lower symmetry energy slope for the update supernova EoS, which is applicable to simulations of neutron star merger. Furthermore, the analogous analysis are also done within the family IUFSU parameter sets. It is found that the correlations between the symmetry energy slope with the radius and tidal deformability at $1.4 M_\odot$ have very similar linear relations in these RMF models.
AutoQ: Automated Kernel-Wise Neural Network Quantization

Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy. The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead. To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs. However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large. The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results. It is difficult for even deep reinforcement learning (DRL) Deep Deterministic Policy Gradient (DDPG)-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy. In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer. Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06\%, and decrease the inference energy consumption by 50.69\%, while achieving the same inference accuracy.
Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. EEG-based motor imagery paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a singlearm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using the ERA-CNN.
Novel Entity Discovery from Web Tables

When working with any sort of knowledge base (KB) one has to make sure it is as complete and also as up-to-date as possible. Both tasks are non-trivial as they require recall-oriented efforts to determine which entities and relationships are missing from the KB. As such they require a significant amount of labor. Tables on the Web on the other hand are abundant and have the distinct potential to assist with these tasks. In particular, we can leverage the content in such tables to discover new entities, properties, and relationships. Because web tables typically only contain raw textual content we first need to determine which cells refer to which known entities—a task we dub table-to-KB matching. This first task aims to infer table semantics by linking table cells and heading columns to elements of a KB. We propose a feature-based method and on two public test collections we demonstrate substantial improvements over the state-of-the-art in terms of precision whilst also improving recall. Then second task builds upon these linked entities and properties to not only identify novel ones in the same table but also to bootstrap their type and additional relationships. We refer to this process as novel entity discovery and, to the best of our knowledge, it is the first endeavor on mining the unlinked cells in web tables. Our method identifies not only out-of-KB (“novel”) information but also novel aliases for in-KB (“known”) entities. When evaluated using three purpose-built test collections, we find that our proposed approaches obtain a marked improvement in terms of precision over our baselines whilst keeping recall stable.
Event triggering control for dynamical systems with designable minimum inter-event time

This paper presents a class of event-triggering rules for dynamical control systems with guaranteed positive minimum inter-event time (MIET). We first propose an event-based function design with guaranteed control performance under a clock-like variable for general nonlinear systems, and later specify them to general linear systems. Compared to the existing static and dynamic triggering mechanisms, the proposed triggering rules hold the robust global event-separation property, and can be easily implemented on practical digital platform. Namely, it is shown that the minimum inter-event time can be flexibly adapted to the various hardware limitations. Finally, several numerical simulations are given to illustrate the theoretical results.
PR ] 2 F eb 2 02 0 A Berry-Esseen bound of order 1 / √ n for martingales

Renz [13] has established a rate of convergence 1/ √ n in the central limit theorem for martingales with some restrictive conditions. In the present paper a modification of the methods, developed by Bolthausen [2] and Grama and Haeusler [6], is applied for obtaining the same convergence rate for a class of more general martingales. An application to linear processes is discussed. To cite this article: A. Name1, A. Name2, C. R. Acad. Sci. Paris, Ser. I 340 (2005). Résumé Une limite de Berry-Esseen de l′ordre 1/ √ n pour les martingales. Renz [13] a établi un taux de convergence 1/ √ n dans le théorème de la limite centrale pour les martingales avec certaines conditions restrictives. Dans le présent article, une modification des méthodes, développées par Bolthausen [2] et Grama et Haeusler [6], a appliqué pour obtenir le même taux de convergence pour une classe de martingales plus générales. Une application aux processus linéaires est discutée. Pour citer cet article : A. Name1, A. Name2, C. R. Acad. Sci. Paris, Ser. I 340 (2005).
Different carboxyl‐rich alicyclic molecules proxy compounds select distinct bacterioplankton for oxidation of dissolved organic matter in the mesopelagic Sargasso Sea

Marine dissolved organic matter (DOM) varies in its recalcitrance to rapid microbial degradation. DOM of varying recalcitrance can be exported from the ocean surface to depth by subduction or convective mixing and oxidized over months to decades in deeper seawater. Carboxyl‐rich alicyclic molecules (CRAM) are characterized as a major component of recalcitrant DOM throughout the oceanic water column. The oxidation of CRAM‐like compounds may depend on specific bacterioplankton lineages with oxidative enzymes capable of catabolizing complex molecular structures like long‐chain aliphatics, cyclic alkanes, and carboxylic acids. To investigate the interaction between bacteria and CRAM‐like compounds, we conducted microbial remineralization experiments using several compounds rich in carboxyl groups and/or alicyclic rings, including deoxycholate, humic acid, lignin, and benzoic acid, as proxies for CRAM. Mesopelagic seawater (200 m) from the northwest Sargasso Sea was used as media and inoculum and incubated over 28 d. All amendments demonstrated significant DOC removal (2–11 μmol C L−1) compared to controls. Bacterioplankton abundance increased significantly in the deoxycholate and benzoic acid treatments relative to controls, with fast‐growing Spongiibacteracea, Euryarcheaota, and slow‐growing SAR11 enriched in the deoxycholate treatment and fast‐growing Alteromonas, Euryarcheaota, and Thaumarcheaota enriched in the benzoic acid treatment. In contrast, bacterioplankton grew slower in the lignin and humic acid treatments, with oligotrophic SAR202 becoming significantly enriched in the lignin treatment. Our results indicate that the character of the CRAM proxy compounds resulted in distinct bacterioplankton removal rates of DOM and affected specific lineages of bacterioplankton capable of responding.
Compliance of Nasal Continuous Positive Airway Pressure in Patients with Obstructive Sleep Apnea Syndrome

Objectives: Nasal continuous positive airway pressure (nCPAP) is the treatment of choice and has been shown to reduce the frequency of nocturnal respiratory events, improve sleep architecture, and decrease daytime sleepiness in patients with obstructive sleep apnea syndrome (OSAS). However, little is known about the compliance of nCPAP treatment in Korea. Our objective was to look into the nCPAP compliance and examine the factors influencing it. Methods: We reviewed the records of one hundred and twenty consecutive patients with OSAS referred for nocturnal polysomnography with nCPAP pressure titration during the period of January 1995 through April 1999 to the Seoul National University Hospital. We performed a telephone interview and obtained data from eighty-three patients. Results: In sixty patients who had accepted nCPAP treatment, twenty-six patients (43.3%) were still using nCPAP device, while thirty-four patients (56.7%) stopped using it. Fifteen patients (25%) were using nCPAP device everyday. In thirty-four patients who discontinued nCPAP use, twenty-five patients (73.5%) did within the first three months, and thirty-one (91.2%) within the first year. Significant predictor of long-term nCPAP use was the presence of subjective daytime sleepiness before nCPAP application. Conclusions: Long-term compliance with nCPAP treatment appears to be associated with the presence of subjective daytime sleepiness before nCPAP application. Long-term compliance with nCPAP may be mostly predicted from the usage pattern within the first three months of use.
Quantifying Cloud Adjustments and the Radiative Forcing due to Aerosol-Cloud Interactions in Satellite Observations of Warm Marine Clouds

Aerosol–cloud interactions and their resultant forcing remains one of the largest sources of uncertainty in future climate scenarios. The effective radiative forcing due to aerosol–cloud interactions (ERFaci) is a combination of two different effects, namely how aerosols modify cloud brightness (RFaci, intrinsic) and how cloud extent reacts to aerosol (cloud adjustments CA; extrinsic). Using satellite observations of warm clouds from the NASA ATrain constellation from 2007 to 2010 along with MERRA2 Reanalysis and aerosol from the SPRINTARS model, we evaluate the ERFaci in warm, marine clouds and its components, the RFaciwarm and CAwarm, while accounting for the liquid water path and local environment. We estimate the ERFaciwarm to be −0.32± 0.16 Wm−2. The RFaciwarm dominates the ERFaciwarm contributing 80 % (−0.21±0.15 Wm−2), while the CAwarm enhances this cooling by 20 % (−0.05± 0.03 Wm−2). Both the RFaciwarm and CAwarm vary in magnitude and sign regionally and can lead to opposite, negating effects under certain environmental conditions. Without considering the two terms separately and without constraining cloud–environment interactions, weak regional ERFaciwarm signals may be erroneously attributed to a damped susceptibility to aerosol.
Knowledge, attitude, and practice of childbearing women toward epidural anesthesia during normal vaginal delivery in Alsanayeah Primary Health Care in Khamis Mushait

Background: Labor pain is a severe form of agony that females experience while giving birth. A lot of pregnant women prefer epidural anesthesia (EA) to avoid labor pain. Objective: This study focuses on women's general awareness about EA during the childbearing age. Methods: A cross-sectional, hospital-based study was conducted using a self-administered questionnaire. The study included all females of childbearing age getting routine antenatal care. The survey was designed to measure their awareness toward EA. A total of 328 females participated in the study. Of these, 205 (62.5%) women showed a low level of knowledge toward EA. Data were analyzed using the Chi-square test and Independent Samples t-test. Results: Participants in our study included 328 women of childbearing age. Most women (172 [52.4%]) participating in the study were aged between 30 and 40 years. In terms of education, 204 (62.2%) women had university-level education. Two hundred and ninety (88.4%) women were multipara. The majority of women (205 [62.5%]) had a low-level knowledge about EA with a significant relationship between the level of perception and primary education, as well as postgraduation with P < 0.023 and P < 0.001, respectively. Also, previous EA with pregnancy significantly related to the level of knowledge with P < 0.001. Through past pregnancies, 106 (32.3%) women had experience with EA. Of these, EA caused complications in only 13 (12.3%) women and side effects in 29 (27.4%) women. Conclusion: The majority of women of childbearing age had limited knowledge about the benefits and complications associated with EA. During the antenatal visit, it is essential to educate all women about EA. This could be done by the obstetrician, anesthesiologist, or midwives and/or through flyers and brochures.
Efficacy of noninvasive hemoglobin measurement by pulse co-oximetry in neonates

Background: Hemoglobin (Hb) measurement is one of the most commonly performed laboratory tests in neonates. The advent of new technologies enables us to carry out noninvasive Hb measurement, without having to prick the neonate, whereby reducing iatrogenic blood loss. There have been only few studies trying to ascertain the relationship between noninvasive Hb and conventional Hb in neonates. Against this background, this study was undertaken to evaluate the efficacy of noninvasive spectrophotometric Hb (SpHb) measurement by the pulse co-oximetry in neonates in comparison to laboratory Hb (Lab-Hb) and to study the influence of factors such as level of Hb and serum bilirubin on same. Materials and Methods: A cross-sectional study was carried out in the postnatal ward and neonatal intensive care unit of a tertiary hospital in Southern India. Hemodynamically stable admitted neonates (n = 100) had their SpHb estimation done using the Masimo Radical-7 Pulse Co-oximeter at the time they were being sampled for Hb. The paired data was then analyzed for the relationship between SpHb and Lab-Hb. Results: The mean ± standard deviation g/dL for Lab-Hb and SpHb was 16.21 ± 1.92 and 15.45 ± 1.28, respectively. There was a reasonable positive correlation between Lab-Hb and SpHb. (Pearson correlation: R = 0.714; P < 0.001). Bland–Altman analysis between Lab-Hb and SpHb revealed a bias (precision) to be 0.763 (1.349). The best agreement with respect to Hb levels was noticed in the Lab-Hb range of 12–18 g/dl and there was no significant influence of hyperbilirubinemia on the results. Conclusion: SpHb by the pulse co-oximetry is an efficacious method of assessing Hb trend among neonates.