Miracle-dz/UniFAR-FTUs huggingface dataset:
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization

The success of gradient descent in ML and especially for learning neural networks is remarkable and robust. In the context of how the brain learns, one aspect of gradient descent that appears biologically difficult to realize (if not implausible) is that its updates rely on feedback from later layers to earlier layers through the same connections. Such bidirected links are relatively few in brain networks, and even when reciprocal connections exist, they may not be equi-weighted. Random Feedback Alignment (Lillicrap et al., 2016), where the backward weights are random and fixed, has been proposed as a bio-plausible alternative and found to be effective empirically. We investigate how and when feedback alignment (FA) works, focusing on one of the most basic problems with layered structure -- low-rank matrix factorization. In this problem, given a matrix $Y_{n\times m}$, the goal is to find a low rank factorization $Z_{n \times r}W_{r \times m}$ that minimizes the error $\|ZW-Y\|_F$. Gradient descent solves this problem optimally. We show that FA converges to the optimal solution when $r\ge \mbox{rank}(Y)$. We also shed light on how FA works. It is observed empirically that the forward weight matrices and (random) feedback matrices come closer during FA updates. Our analysis rigorously derives this phenomenon and shows how it facilitates convergence of FA*, a closely related variant of FA. We also show that FA can be far from optimal when $r<\mbox{rank}(Y)$. This is the first provable separation result between gradient descent and FA. Moreover, the representations found by gradient descent and FA can be almost orthogonal even when their error $\|ZW-Y\|_F$ is approximately equal. As a corollary, these results also hold for training two-layer linear neural networks when the training input is isotropic, and the output is a linear function of the input.
Optimal consumption and portfolio selection with Epstein–Zin utility under general constraints

The paper investigates the consumption-investment problem for an investor with Epstein-Zin utility in an incomplete market. Closed, not necessarily convex, constraints are imposed on strategies. The optimal consumption and investment strategies are characterized via a quadratic backward stochastic differential equation (BSDE). Due to the stochastic market environment, the solution to this BSDE is unbounded and thereby the BMO argument breaks down. After establishing the martingale optimality criterion, by delicately selecting Lyapunov functions, the verification theorem is ultimately obtained. Besides, several examples and numerical simulations for the optimal strategies are provided and illustrated.
Automatic Semantic Segmentation of the Lumbar Spine. Clinical Applicability in a Multi-parametric and Multi-centre MRI study

One of the major di ﬃ culties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the e ﬀ ect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a classes label to each pixel of an image. Classes were deﬁned by radiologists and correspond to di ﬀ erent structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to deﬁne the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to di ﬀ erent strategies
Compact Semisimple 2-Categories

Working over an arbitrary field, we define compact semisimple 2-categories, and show that every compact semisimple 2-category is equivalent to the 2-category of separable module 1-categories over a finite semisimple tensor 1-category. Then, we prove that, over an algebraically closed field or a real closed field, compact semisimple 2-categories are finite. Finally, we explain how a number of key results in the theory of finite semisimple 2-categories over an algebraically closed field of characteristic zero can be generalized to compact semisimple 2-categories.
Online Estimation and Optimization of Utility-Based Shortfall Risk

Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, where samples from the underlying loss distribution are available one-at-a-time. We cast the UBSR estimation problem as a root finding problem, and propose stochastic approximation-based estimations schemes. We derive non-asymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent based algorithm for UBSR optimization, and derive non-asymptotic bounds on its convergence.
Fairness Testing of Deep Image Classification with Adequacy Metrics

As deep image classification applications, e.g., face recognition, become increasingly prevalent in our daily lives, their fairness issues raise more and more concern. It is thus crucial to comprehensively test the fairness of these applications before deployment. Existing fairness testing methods suffer from the following limitations: 1) applicability, i.e., they are only applicable for structured data or text without handling the high-dimensional and abstract domain sampling in the semantic level for image classification applications; 2) functionality, i.e., they generate unfair samples without providing testing criterion to characterize the model's fairness adequacy. To fill the gap, we propose DeepFAIT, a systematic fairness testing framework specifically designed for deep image classification applications. DeepFAIT consists of several important components enabling effective fairness testing of deep image classification applications: 1) a neuron selection strategy to identify the fairness-related neurons; 2) a set of multi-granularity adequacy metrics to evaluate the model's fairness; 3) a test selection algorithm for fixing the fairness issues efficiently. We have conducted experiments on widely adopted large-scale face recognition applications, i.e., VGGFace and FairFace. The experimental results confirm that our approach can effectively identify the fairness-related neurons, characterize the model's fairness, and select the most valuable test cases to mitigate the model's fairness issues.
FAIRLEARN: Configurable and Interpretable Algorithmic Fairness

The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world. While the positive impact of the algorithms has been tremendous, there is a need to mitigate any bias arising from either training samples or implicit assumptions made about the data samples. This need becomes critical when algorithms are used in automated decision making systems that can hugely impact people’s lives. Many approaches have been proposed to make learning algorithms fair by detecting and mitigating bias in different stages of optimization. However, due to a lack of a universal deﬁ-nition of fairness, these algorithms optimize for a particular interpretation of fairness which makes them limited for real world use. Moreover, an underlying assumption that is common to all algorithms is the apparent equivalence of achieving fairness and removing bias. In other words, there is no user deﬁned criteria that can be incorporated into the optimization procedure for producing a fair algorithm. Motivated by these shortcomings of existing methods, we propose the FAIRLEARN procedure that produces a fair algorithm by incorporating user constraints into the optimization procedure. Furthermore, we make the process interpretable by estimating the most predictive features from data. We demonstrate the efﬁcacy of our approach on several real world datasets using different fairness criteria. Reproducibility: All source codes, and experimental data are available in our Github repository 1
Cognitive constraints on advance planning of sentence intonation

Pitch peaks tend to be higher at the beginning of longer than shorter sentences (e.g., ‘A farmer is pulling donkeys’ vs ‘A farmer is pulling a donkey and goat’), whereas pitch valleys at the ends of sentences are rather constant for a given speaker. These data seem to imply that speakers avoid dropping their voice pitch too low by planning the height of sentence-initial pitch peaks prior to speaking. However, the length effect on sentence-initial pitch peaks appears to vary across different types of sentences, speakers and languages. Therefore, the notion that speakers plan sentence intonation in advance due to the limitations in low voice pitch leaves part of the data unexplained. Consequently, this study suggests a complementary cognitive account of length-dependent pitch scaling. In particular, it proposes that the sentence-initial pitch raise in long sentences is related to high demands on mental resources during the early stages of sentence planning. To tap into the cognitive underpinnings of planning sentence intonation, this study adopts the methodology of recording eye movements during a picture description task, as the eye movements are the established approximation of the real-time planning processes. Measures of voice pitch (Fundamental Frequency) and incrementality (eye movements) are used to examine the relationship between (verbal) working memory (WM), incrementality of sentence planning and the height of sentence-initial pitch peaks.
LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks

We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We model the volumetric function by tiling space into blocks, and representing the function over each block by shifts of a coordinate-based, or implicit, neural network. Inputs to the network include both spatial coordinates and a latent vector per block. We represent the latent vectors using coefficients of the region-adaptive hierarchical transform (RAHT) used in the MPEG geometry-based point cloud codec G-PCC. The coefficients, which are highly compressible, are rate-distortion optimized by back-propagation through a rate-distortion Lagrangian loss in an auto-decoder configuration. The result outperforms the transform in the current standard, RAHT, by 2–4 dB and a recent non-volumetric method, Deep-PCAC, by 2–5 dB at the same bit rate. This is the first work to compress volumetric functions represented by local coordinate-based neural networks. As such, we expect it to be applicable beyond point clouds, for example to compression of high-resolution neural radiance fields.
Differentially Private Federated Learning on Heterogeneous Data

Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) protecting the privacy of participating users. In this work, we propose a novel FL approach (DP-SCAFFOLD) to tackle these two challenges together by incorporating Differential Privacy (DP) constraints into the popular SCAFFOLD algorithm. We focus on the challenging setting where users communicate with a"honest-but-curious"server without any trusted intermediary, which requires to ensure privacy not only towards a third-party with access to the final model but also towards the server who observes all user communications. Using advanced results from DP theory, we establish the convergence of our algorithm for convex and non-convex objectives. Our analysis clearly highlights the privacy-utility trade-off under data heterogeneity, and demonstrates the superiority of DP-SCAFFOLD over the state-of-the-art algorithm DP-FedAvg when the number of local updates and the level of heterogeneity grow. Our numerical results confirm our analysis and show that DP-SCAFFOLD provides significant gains in practice.
TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video

Timely handgun detection is a crucial problem to improve public safety; nevertheless, the effectiveness of many surveillance systems still depends of finite human attention. Much of the previous research on handgun detection is based on static image detectors, leaving aside valuable temporal information that could be used to improve object detection in videos. To improve the performance of surveillance systems, a real-time temporal handgun detection system should be built. Using Temporal Yolov5, an architecture based on Quasi-Recurrent Neural Networks, temporal information is extracted from video to improve the results of handgun detection. Moreover, two publicly available datasets are proposed, labeled with hands, guns, and phones. One containing 2199 static images to train static detectors, and another with 5960 frames of videos to train temporal modules. Additionally, we explore two temporal data augmentation techniques based on Mosaic and Mixup. The resulting systems are three temporal architectures: one focused in reducing inference with a mAP$_{50:95}$ of 55.9, another in having a good balance between inference and accuracy with a mAP$_{50:95}$ of 59, and a last one specialized in accuracy with a mAP$_{50:95}$ of 60.2. Temporal Yolov5 achieves real-time detection in the small and medium architectures. Moreover, it takes advantage of temporal features contained in videos to perform better than Yolov5 in our temporal dataset, making TYolov5 suitable for real-world applications. The source code is publicly available at https://github.com/MarioDuran/TYolov5.
Fatigue analyses of the prototype Francis runners based on site measurements and simulations

With the increasing development of solar power and wind power which give an unstable output to the electrical grid, hydropower is required to give a rapid and flexible compensation, and the hydraulic turbines have to operate at off-design conditions frequently. Prototype Francis runners suffer from strong vibrations induced by high pressure pulsations at part load, low part load, speed-no-load and during start-stops and load rejections. Fatigue and damage may be caused by the alternating stress on the runner blades. Therefore, it becomes increasingly important to carry out fatigue analysis and life time assessment of the prototype Francis runners, especially at off-design conditions. This paper presents the fatigue analyses of the prototype Francis runners based on the strain gauge site measurements and numerical simulations. In the case of low part load, speed-no-load and transient events, since the Francis runners are subjected to complex hydraulic loading, which shows a stochastic characteristic, the rainflow counting method is used to obtain the number of cycles for various dynamic amplitude ranges. From middle load to full load, pressure pulsations caused by Rotor-stator- Interaction become the dominant hydraulic excitation of the runners. Forced response analysis is performed to calculate the maximum dynamic stress. The agreement between numerical and experimental stresses is evaluated using linear regression method. Taking into account the effect of the static stress on the S-N curve, the Miner's rule, a linear cumulative fatigue damage theory, is employed to calculate the damage factors of the prototype Francis runners at various operating conditions. The relative damage factors of the runners at different operating points are compared and discussed in detail.
Stall inception in a transonic axial fan

Abstract The current paper reports a numerical investigation of stall inception in a transonic compressor rotor, NASA Rotor-67, by using the whole flow passages in the computations. Surface roughness is added to one of the blades in order to trigger rotating stall. During stall inception, the tip clearance vortex moved away from the suction surface of the roughened blade's upper neighbour, leading to vortex breakdown. The stall cell was found to propagate opposite the direction of the blade rotation at about 30 per cent of rotational speed. The effect of air bleeding on stabilizing the compressor is also studied in this work. The mean mass flowrate removed from the bleed valves was ∼1.2 per cent of the mainstream flowrate. This amount of bleeding was found to effectively suppress the stalling disturbances.
Bats can reach 3626 m a.s.l. in Papua New Guinea: altitudinal range extensions for six rainforest bat species

Abstract Bats represent an important, but poorly known component of mammal diversity in Papua New Guinea (PNG). Our surveys in two altitudinal rainforest gradients recorded 43 bat species of which six (Dobsonia minor, D. praedatrix, Hipposideros calcaratus, H. maggietaylorae, Miniopterus australis, Miniopterus sp.) fell outside of their known altitudinal ranges. This enlargement could reflect the lack of past sampling, or a genuine range extension, potentially in response to climate change. Our study highlights the importance of baseline data on the altitudinal distribution of vertebrates, including bats, in PNG for the monitoring of their response to climate change and anthropogenic disturbance.
Testing AGV Mobility Control Method for MANET Coverage Optimization using Procedural Generation

In industrial applications continuous wireless connectivity of mobile clients can rarely by guaranteed. Lack of communication negatively impacts the performance of industrial automation systems, e.g. Automated Guided Vehicle (AGV) fleets. Utilizing industrial Mobile Ad-hoc NETworks (MANETs) and adaptive positioning systems can reduce the number of disconnections in these AGV fleets. Therefore the performance of the mobile systems (e.g. AGV fleet) is improved and factory efficiency increased. In this work procedural simulation is used to examine wireless communication in industrial applications. This methods enables the observation of the interaction of mobility control system, network status and robotic system performance independently from a specific environment or scenario. Novel insights on the effectiveness of ad-hoc communication in industrial applications and the correlation of AGV fleet connectedness and AGV fleet transport performance are presented. Additionally a control method is proposed, which improves the network coverage of an industrial MANET and efficiency of AGV fleets.
Engraftment of Allotransplanted Tumor Cells in Adult rag2 Mutant Xenopus tropicalis

Modelling human genetic diseases and cancer in lab animals has been greatly aided by the emergence of genetic engineering tools such as TALENs and CRISPR/Cas9. We have previously demonstrated the ease with which genetically engineered Xenopus models (GEXM) can be generated. This included the induction of autochthonous tumour formation by injection of early embryos with Cas9 recombinant protein loaded with sgRNAs targeting multiple tumour suppressor genes. What has been lacking so far is the possibility to propagate the induced cancers via transplantation. In this paper we describe the generation of a rag2-/- knock-out line in Xenopus tropicalis that is deficient in functional T- and B-cells. This line was validated by means of an allografting experiment with a primary tp53-/- donor tumour. In addition, we optimized available protocols for sub-lethal gamma irradiation of X. tropicalis froglets. Irradiated animals also allowed stable, albeit transient, engraftment of transplanted tp53-/- tumour cells. The novel X. tropicalis rag2-/- line and the irradiated wild type froglets will further expand the experimental toolbox in this diploid amphibian, and help to establish it as a versatile and relevant model for exploring human cancer.
RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

5G enhanced Mobile broadband (eMBB) aims to provide users with a peak data rate of 20 Gbps in the Radio Access Network (RAN). However, since most Congestion Control Algorithms (CCAs) rely on startup and probe phases to discover the bottleneck bandwidth, they cannot quickly utilize the available RAN bandwidth and adapt to fast capacity changes without introducing large delay increase, especially when multiple flows are sharing the same Radio Link Control (RLC) buffer. To tackle this issue, we propose RAPID, a RAN-aware proxy-based flow control mechanism that prevents CCAs from overshooting more than the available RAN capacity while allowing near optimal link utilization. Based on analysis of up-to-date radio information using Multi-access Edge Computing (MEC) services and packet arrival rates, RAPID is able to differentiate slow interactive flows from fast download flows and allocate the available bandwidth accordingly. Our experiments with concurrent Cubic and BBR flows show that RAPID can reduce delay increase by a factor of 10 to 50 in both Line-of-Sight (LOS) and Non-LOS (NLOS) conditions while preserving high throughput.
Asymmetrical Property of the Subproportionality of Weighting Function in Prospect Theory: Is It Real and How Can It Be Achieved?

An asymmetrical property of the probability weighting function, namely, subproportionality, was derived from observations. Subproportionality can provide a reasonable explanation for accommodating the Allais paradox and, therefore, deserves replication for its high impact. The present study aimed to explore the mechanism of subproportionality by comparing the two completely opposite decision mechanisms: prospect theory and equate-to-differentiate theory. Results revealed that the underlying mechanism supports the prediction of equate-to-differentiate theory but not prospect theory in the diagnostic stimuli condition. Knowledge regarding which intra-dimensional difference between Options A and B is greater, not knowledge regarding which option’s overall prospect value is greater, indeed predicts option preference. Our findings may deepen current understanding on the mechanisms behind the simple risky choice with a single-non-zero outcome. Additionally, these findings will hopefully encourage subsequent researchers to take a fresh look at the Allais paradox.
Statistical Model Of Road Traffic Crashes Data In Anambra State, Nigeria: A Poisson Regression Approach

Road traffic crashes are count (discrete) in nature. When modeling discrete data for characteristics and prediction of events, it is appropriate using the Poisson Regression Model. However, the condition that the mean and variance of the Poisson are equal, poses a great constraint, hence necessitating the use of the Generalized Poisson Regression (GPR) and the Negative Binomial Regression (NBR) models, which do not require these constraints that the mean and the variance be equal, as proxies. Data on Road traffic crashes from the Anambra State Command of the Federal Road Safety Commission (FRSC), Nigeria were analyzed using these three methods, the results from the two proxies are compared using the Akaike Information Criterion (AIC) with GPR showing an AIC value of 3508.595 and the NBR showing an AIC value of 2742. Having shown a smaller AIC value, the NBR was considered a better model when analyzing road traffic crashes in Anambra State, Nigeria.