Miracle-dz/UniFAR-FTUs huggingface dataset:
Buffered Asynchronous SGD for Byzantine Learning

Distributed learning has become a hot research topic due to its wide application in clusterbased large-scale learning, federated learning, edge computing and so on. Most traditional distributed learning methods typically assume no failure or attack. However, many unexpected cases, such as communication failure and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with failure or attack, has recently attracted much attention. Most existing BL methods are synchronous, which are impractical in some applications due to heterogeneous or offline workers. In these cases, asynchronous BL (ABL) is usually preferred. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL. To the best of our knowledge, BASGD is the first ABL method that can resist non-omniscient attacks without storing any instances on server. Furthermore, we also propose an improved variant of BASGD, called BASGD with momentum (BASGDm), by introducing momentum into BASGD. BASGDm can resist both non-omniscient and omniscient attacks. Compared with those methods which need to store instances on server, BASGD and BASGDm have a wider scope of application. Both BASGD and BASGDm are compatible with various aggregation rules. Moreover, both BASGD and BASGDm are proved to be convergent and be able to resist failure or attack. Empirical results show that our methods significantly outperform existing ABL baselines when there exists failure or attack on workers.
Investigation of the 25 January 2000 East Coast Cyclogenesis

Abstract : On 25 January 2000, a rapidly developing cyclone tracked up the East Coast of the United States. Along with this system, 12 to 18 inches of snow fell on major cities from North Carolina to Washington DC. This snowstorm deserves special consideration because of the poor numerical and human forecasts it received. The goal of this work is to analyze the performance of the Navy models, NOGAPS and COAMPS (West Atlantic) with the 25 January cyclogenesis event. Deficiencies with the model analyses and forecasts are identified and a diagnosis of critical model fields that led to these deficiencies is completed. Preliminary investigation of analyses and NOGAPS forecast runs with the new variational data assimilation system, NAVDAS, concludes the research. The results of the research reveal that NOGAPS poorly forecast storm tracks while COAMPS showed more success. Both NOGAPS and COAMPS produced deficient short range upper-level height forecasts and had difficulty analyzing two prominent jet streaks. NOGAPS was not able to adequately analyze or forecast cold air damming and coastal frontogenesis, while COAMPS was more successful at resolving these features. COAMPS produced better precipitation forecasts than NOGAPS, but still showed deficiencies. Preliminary investigation of NOGAPS using NAVDAS shows promise.
Observationally derived transport diagnostics for the lowermost stratosphere and their application to the GMI chemistry and transport model

Transport from the surface to the lowermost stratosphere (LMS) can occur on timescales of a few months or less, making it possible for short-lived tropospheric pollu- tants to influence stratospheric composition and chemistry. Models used to study this influence must demonstrate the credibility of their chemistry and transport in the upper tropo- sphere and lower stratosphere (UT/LS). Data sets from satel- lite and aircraft instruments measuring CO, O3, N2O, and CO2 in the UT/LS are used to create a suite of diagnostics for the seasonally-varying transport into and within the low- ermost stratosphere, and of the coupling between the tropo- sphere and stratosphere in the extratropics. The diagnostics are used to evaluate a version of the Global Modeling Initia- tive (GMI) Chemistry and Transport Model (CTM) that uses a combined tropospheric and stratospheric chemical mech- anism and meteorological fields from the GEOS-4 general circulation model. The diagnostics derived from N2O and O3 show that the model lowermost stratosphere has realis- tic input from the overlying high latitude stratosphere in all seasons. Diagnostics for the LMS show two distinct layers. The upper layer begins 30 K potential temperature above the tropopause and has a strong annual cycle in its compo- sition. The lower layer is a mixed region 30 K thick near the tropopause that shows no clear seasonal variation in the degree of tropospheric coupling. Diagnostics applied to the GMI CTM show credible seasonally-varying transport in the LMS and a tropopause layer that is realistically coupled to the UT in all seasons. The vertical resolution of the GMI CTM in the UT/LS, 1 km, is sufficient to realistically rep- resent the extratropical tropopause layer. This study demon- strates that the GMI CTM has the transport credibility re- quired to study the impact of tropospheric emissions on the stratosphere.
Locally efficient semiparametric estimators for a class of Poisson models with measurement error

The presence of measurement error may cause bias in parameter estimation and can lead to incorrect conclusions in data analyses. Despite a large body of literature on general measurement error problems, relatively few works exist to handle Poisson models. In this article we thoroughly study Poisson models with errors in covariates and propose consistent and locally efficient semiparametric estimators. We assess the finite sample performance of the estimators through extensive simulation studies and illustrate the proposed methodologies by analyzing data from the Stroke Recovery in Underserved Populations Study. The Canadian Journal of Statistics 47: 157–181; 2019 © 2019 Statistical Society of Canada
Median Nerve Compression Syndromes. Literature Review and Update

The median nerve is a nervous structure that begins to cross structures at the level of the elbow that might cause compression. The Struthers ligament, lacertus fibrosus, pronator teres, and flexor digitorum superficialis are among them. Finally, the transverse carpal ligament creates another compression site in the wrist. All these structures can develop pathological signs and symptoms of nerve entrapment, which favors nerve functional degradation. Our objective is to provide an update on these median nerve entrapment sites, as well as information on how to establish an accurate diagnosis and provide adequate treatment.
COMPILING, SYNTHESIZING AND ANALYZING EXISTING BOREAL FOREST FIRE HISTORY DATA IN ALASKA

See next page for additional authors Follow this and additional works at: http://digitalcommons.unl. edu/jfspresearch Part of the Forest Biology Commons, Forest Management Commons, Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Other Environmental Sciences Commons, Other Forestry and Forest Sciences Commons, Sustainability Commons, and the Wood Science and Pulp, Paper Technology Commons
How much do I matter? Teacher Beliefs about Education Production *

Teachers in developing countries face a complex education production function where a wide array of inputs that determine student learning are outside their control. The presence of external constraints might suggest to teachers that their effort matters little in influencing student learning, leading to a perception of low returns to effort. These beliefs may predispose teachers to exert low levels of classroom effort, especially on low-performing students in the classroom. I conduct an experimental evaluation of a psycho-social intervention that targets teachers’ beliefs about perceived returns to effort and study the extent to which it impacts teacher effort and student learning. I devise a novel experimental task to elicit teachers’ perceived control beliefs about improving performance of students in the bottom tail of ability distribution. I find that the intervention positively affects teachers’ beliefs about their ability to increase learning, and improves the quality and quantify of teacher effort as reflected in classroom observations.
Comparison of Preoperative Analgesics on the Efficacy of Inferior Alveolar Nerve Block with Patients Having Symptomatic Irreversible Pulpitis: A Double-Blinded, Randomized Controlled Trial

Objective The objective of this study was to evaluate the effectiveness of preoperative analgesics on inferior alveolar nerve blocks (IANB) during root canal treatment in patients with symptomatic irreversible pulpitis of the mandibular molars. Methods This study was a randomized, double-blinded, superiority trial with a parallel study design. A total of 120 subjects with symptomatic irreversible pulpitis were randomly assigned to one of four groups: group A (control, Vitamin E, Evion 400 mg), group B (Diclofenac sodium, Voltral SR100 100 mg), group C (Piroxicam, Feldene 20 mg), and group D (Tramadol, Tramal 50 mg). The patients recorded preoperative pain levels, and after administration of local anaesthesia intraoperative pain levels using the Heft-Parker visual analogue scale before and after the oral administration of the analgesics. Statistical analysis was performed using the Kruskal-Wallis test. Results All the analgesic groups showed a significant effect on the efficacy of the inferior alveolar nerve block in contrast to the control group (p<0.05). However, no significant difference was found between the drug groups on the effectiveness of the inferior alveolar nerve block (p>0.05). No side effects were reported in the present study. Conclusion Preoperative analgesics significantly increase the effectiveness of inferior alveolar nerve block in patients with symptomatic irreversible pulpitis. Therefore, preoperative analgesics should be considered to increase the effectiveness of inferior alveolar nerve block in patients with symptomatic irreversible pulpitis on the mandibular molars.
Quasi-Equivalence between Width and Depth of Neural Networks

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of deep networks. Based on a symmetric consideration, we investigate if the design of artiﬁcial neural networks should have a directional preference, and what the mechanism of interaction is between the width and depth of a network. Inspired by the De Morgan law, we address this fundamental question by establishing a quasi-equivalence between the width and depth of ReLU networks. We formulate two transforms for mapping an arbitrary ReLU network to a wide ReLU network and a deep ReLU network respectively, so that the essentially same capability of the original network can be implemented. Based on our ﬁndings, a deep network has a wide equivalent, and vice versa, subject to an arbitrarily small error
The challenge of time: Clock‐drawing and cognitive function in the elderly

This study compares the ‘clock‐drawing test’ to two standardized cognitive screens in an elderly population. A newly developed hierarchical classification of errors in clock‐drawing correlated well with the severity of scores on both cognitive and screens and also demonstrated acceptable standards for sensitivity and specificity. Clock‐drawing appears to be a practical test which can be useful for clinicians as a screen for cognitive impairment in old age and possibly as a marker of change in cognitive status.
AN ENVIRONMENTAL ASSESSMENT FRAMEWORK WITH SYSTEMATIC REGIONAL AND TIME SCENARIOS

A multiregional and scenario-based framework for environmental assessment is presented. Applied to energy production systems, this model clearly shows wide variations in various impact categories. An illustration of these variations is provided by the modeling of a concentrated solar power plant. Results show most of the environmental impacts are embodied in infrastructure; hence impacts are also strongly related to the temporal development of the background electricity mix. We conclude that, whereas greenhouse gas emissions are significantly lowered in comparison with fossil-fuel counterparts, material requirements are higher for this kind of technology, and even increasing with time.
Investigation of Cognitive Impairment in the Course of Post-COVID Syndrome

(1) Background: The study presents results from an investigation of cognitive impairment in patients hospitalized in the first psychiatric clinic in Bulgaria to treat patients with COVID-19 during the pandemic period between 2020 and 2022. One hundred and twenty patients who had recovered from acute COVID-19 infection (up to 12 weeks ago) and had no previous history of cognitive impairment participated in the study. In 23 of them (19.17%), disturbance of cognitive functioning was observed. (2) Methods: All 23 patients underwent neuropsychological (Luria’s test, Platonov’s Maze test, MMSE, Boston Naming test) and neuroimaging examinations. Only seven of them had evidence of cortical atrophy on CT/MRI images. The most significantly demonstrative image of one of those patients is presented. (3) Results: The neuropsychological testing results of both groups show a certain decrease in fixation and memory retention as well as in the range, concentration, distribution and switching of attention. Deviations from the norm on the MMSE, as well as on the Boston Naming Test, were found in the group of patients with cortical atrophy (mild to moderate aphasia). Neuroprotective agents such as Citicoline, Piracetam and Memantine were prescribed to the patients with evident cortical atrophy. After 3 months, positive results of the neuropsychological examination were reported in both groups. (4) Conclusions: Although there are limited data on the benefit of prescribing pro-cognitive agents in the post-COVID period, our clinical experience suggests that it might be useful in the recovery process from the infection’s consequences on cognition for patients with brain pathology.
A Weighted Covariate Balancing Method of Estimating Causal Effects in Case-Control Studies

Propensity score methods have dominated the estimation of treatment effects based on observational data and particularly in the health and medical sciences. We propose a weighting method based on rank-based Mahalanobis distance, namely the covariate balancing rank-based Mahalanobis distance method, to estimate causal effects for observational data. Using Monte Carlo simulations, under different data structures and type of outcome variables, the proposed method is shown to have better performance, in terms of bias reduction and treatment effect estimation. Specifically, under the generalized linear model framework, we simulated datasets based on the Lalonde-PSID study, for linear link function; while datasets were simulated based on the Lindner study, for non-linear link functions. We further apply the proposed method to data extracted from the Nigeria Demographic Health Survey (2013), to investigate the effect of educational exposure on ideal family size among married couples in Nigeria. The proposed method is a viable alternative method that can improve covariates balance, bias reduction, and efficient estimation of treatment effects.
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks

Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature. On the other hand, low-rank training, as an alternative way to train low-rank CNNs from scratch, has been exploited little yet. Unlike low-rank compression, low-rank training does not need pre-trained full-rank models, and the entire training phase is always performed on the low-rank structure, bringing attractive benefits for practical applications. However, the existing low-rank training solutions still face several challenges, such as a considerable accuracy drop and/or still needing to update full-size models during the training. In this paper, we perform a systematic investigation on low-rank CNN training. By identifying the proper low-rank format and performance-improving strategy, we propose ELRT, an efficient low-rank training solution for high-accuracy, high-compactness, low-rank CNN models. Our extensive evaluation results for training various CNNs on different datasets demonstrate the effectiveness of ELRT.
Durum in a changing climate - drought stress during growing seasons in Syria.

Durum wheat is mainly produced under rainfed but often sub-optimal moisture conditions in the Mediterranean basin. In the current study, a mapping population (drought×salt tolerant cultivar) was developed and the progeny examined for their ability to cope with moisture limiting conditions in Syria in two growing seasons. At the beginning of the second growing season, extreme reduced precipitation resulted in an extension of the days to heading and maturity and lead to a highly significant depletion in plant height, yield, harvest index and thousand grain weight. Quantitative trait loci (QTL) analyses revealed 15 highly significant QTL on chromosomes 1B, 2A, 2B, 3A, 4A, 4B, 5A, 5B, 7A and 7B. For grain yield in total five different QTL were found for the two growing seasons. In contrast, highly significant QTL for plant height (LOD=27) appeared on chromosome 4B in both growing seasons and explained a phenotypic variance up to 65%. Summarising, QTL for drought tolerance under ‘natural’ appearing moisture limiting conditions could be detected in durum wheat. However, strong interactions between genetic and environmental factors will challenge breeding programmes on drought stress for the future.
Supplementary Materials for Elastic Aggregation for Federated Optimization

However, this average method may not be suitable for a real scenario in federated learning, for that training data may be collected randomly. The momentum approach described in paper is more flexible for us to do an online accumulating of parameter sensitivity. Also, we find that the momentum approach can achieve a better performance and is more robust for different τ under different tasks compared with the average approach. We also conduct an experiment on synthetic federated dataset of CIFAR-10. We show the inspired percentage of parameters during training in Fig. 1. It indicates that momentum approach prefers to keep more parameters to be restricted from client drifting. However, the average approach prefers to inspire more parameters to explore a better distribution. In federated learning, we are more eager to solve the client-drift problem caused by non-IID-ness via restricting parameters. The final accuracy in Tab. 1 also indicates a better performance of momentum approach.
ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

We propose ImGeoNet, a multi-view image-based 3D object detection framework that models a 3D space by an image-induced geometry-aware voxel representation. Unlike previous methods which aggregate 2D features into 3D voxels without considering geometry, ImGeoNet learns to induce geometry from multi-view images to alleviate the confusion arising from voxels of free space, and during the inference phase, only images from multiple views are required. Besides, a powerful pre-trained 2D feature extractor can be leveraged by our representation, leading to a more robust performance. To evaluate the effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The results demonstrate that ImGeoNet outperforms the current state-of-the-art multiview image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy. In addition, ImGeoNet shows great data efficiency by achieving results comparable to ImVoxelNet with 100 views while utilizing only 40 views. Furthermore, our studies indicate that our proposed image-induced geometry-aware representation can enable image-based methods to attain superior detection accuracy than the seminal point cloud-based method, VoteNet, in two practical scenarios: (1) scenarios where point clouds are sparse and noisy, such as in ARKitScenes, and (2) scenarios involve diverse object classes, particularly classes of small objects, as in the case in ScanNet200. Project page: https://ttaoretw.github. io/imgeonet.
Parameter Estimation Procedures for Deep Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement

Aiming at exploiting temporal correlations across consecutive time frames in the short-time Fourier transform (STFT) domain, multi-frame algorithms for single-microphone speech enhancement have been proposed. Typically, the multi-frame filter coefficients are either estimated directly using deep neural networks or a certain filter structure is imposed, e.g., the multi-frame minimum variance distortionless response (MFMVDR) filter structure. Recently, it was shown that integrating the fully differentiable MFMVDR filter into an end-to-end supervised learning framework employing temporal convolutional networks (TCNs) allows for a high estimation accuracy of the required parameters, i.e., the speech inter-frame correlation vector and the interference covariance matrix. In this paper, we investigate different covariance matrix structures, namely Hermitian positive-definite, Hermitian positive-definite Toeplitz, and rank-1. The main differences between the considered matrix structures lie in the number of parameters that need to be estimated by the TCNs as well as the required linear algebra operations. For example, assuming a rank-1 matrix structure, we show that the MFMVDR filter can be written as a linear combination of the TCN outputs, significantly reducing computational complexity. In addition, we consider a covariance matrix estimation procedure based on recursive smoothing. Experimental results on the deep noise suppression challenge dataset show that the estimation procedure using the Hermitian positive-definite matrix structure yields the best performance, closely followed by the rank-1 matrix structure at a much lower complexity. Furthermore, imposing the MFMVDR filter structure instead of directly estimating the multi-frame filter coefficients slightly but consistently improves the speech enhancement performance.