Miracle-dz/UniFAR-FTUs huggingface dataset:
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring

We prove a new minimax theorem connecting the worst-case Bayesian regret and minimax regret under partial monitoring with no assumptions on the space of signals or decisions of the adversary. We then generalise the information-theoretic tools of Russo and Van Roy (2016) for proving Bayesian regret bounds and combine them with the minimax theorem to derive minimax regret bounds for various partial monitoring settings. The highlight is a clean analysis of `non-degenerate easy' and `hard' finite partial monitoring, with new regret bounds that are independent of arbitrarily large game-dependent constants. The power of the generalised machinery is further demonstrated by proving that the minimax regret for k-armed adversarial bandits is at most sqrt{2kn}, improving on existing results by a factor of 2. Finally, we provide a simple analysis of the cops and robbers game, also improving best known constants.
Towards Machine-assisted Meta-Studies: The Hubble Constant

We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model – a classical technique in natural language processing – has successfully extracted 298 measurements of the Hubble constant, with uncertainties, from the 208 541 available arXiv astrophysics papers. We have also created an artificial neural network classifier to identify papers in arXiv which report novel measurements. From the analysis of our results we find that reporting measurements with uncertainties and the correct units is critical information when distinguishing novel measurements in free text. Our results correctly highlight the current tension for measurements of the Hubble constant and recover the 3. 5σ discrepancy – demonstrating that the tool presented in this paper is useful for meta-studies of astrophysical measurements from a large number of publications.
Learning Differentiable Grammars for Continuous Data

This paper proposes a novel algorithm which learns a formal regular grammar from real-world continuous data, such as videos or other streaming data. Learning latent terminals, non-terminals, and productions rules directly from streaming data allows the construction of a generative model capturing sequential structures with multiple possibilities. Our model is fully differentiable, and provides easily interpretable results which are important in order to understand the learned structures. It outperforms the state-of-the-art on several challenging datasets and is more accurate for forecasting future activities in videos. We plan to open-source the code.
Machine learning classifiers do not improve the prediction of academic risk: Evidence from Australia

Abstract Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administrative datasets, and have contained relatively small sample sizes by ML standards. In this article, we apply popular machine learning models to a large dataset (n = 1.2 million) containing primary and middle school performance on a standardized test given annually to Australian students. We show that machine learning models do not outperform logistic regression for detecting students who will perform in the “below standard” band of achievement upon sitting their next test, even in a large-n setting.
Neural Networks Predict Fluid Dynamics Solutions from Tiny Datasets

In computational fluid dynamics, it often takes days or weeks to simulate the aerodynamic behavior of designs such as jets, spacecraft, or gas turbine engines. One of the biggest open problems in the field is how to simulate such systems much more quickly with sufficient accuracy. Many approaches have been tried; some involve models of the underlying physics, while others are modelfree and make predictions based only on existing simulation data. However, all previous approaches have severe shortcomings or limitations. We present a novel approach: we reformulate the prediction problem to effectively increase the size of the otherwise tiny datasets, and we introduce a new neural network architecture (called a cluster network) with an inductive bias wellsuited to fluid dynamics problems. Compared to state-of-the-art model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster and vastly easier to apply. Moreover, our method outperforms previous model-free approaches.
Minimax Testing of Identity to a Reference Ergodic Markov Chain

We exhibit an efficient procedure for testing, based on a single long state sequence, whether an unknown Markov chain is identical to or $\varepsilon$-far from a given reference chain. We obtain nearly matching (up to logarithmic factors) upper and lower sample complexity bounds for our notion of distance, which is based on total variation. Perhaps surprisingly, we discover that the sample complexity depends solely on the properties of the known reference chain and does not involve the unknown chain at all, which is not even assumed to be ergodic.
Agnostic Federated Learning

A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients. We argue that, with the existing training and inference, federated models can be biased towards different clients. Instead, we propose a new framework of agnostic federated learning, where the centralized model is optimized for any target distribution formed by a mixture of the client distributions. We further show that this framework naturally yields a notion of fairness. We present data-dependent Rademacher complexity guarantees for learning with this objective, which guide the definition of an algorithm for agnostic federated learning. We also give a fast stochastic optimization algorithm for solving the corresponding optimization problem, for which we prove convergence bounds, assuming a convex loss function and hypothesis set. We further empirically demonstrate the benefits of our approach in several datasets. Beyond federated learning, our framework and algorithm can be of interest to other learning scenarios such as cloud computing, domain adaptation, drifting, and other contexts where the training and test distributions do not coincide.
Self-Supervised Visual Representations for Cross-Modal Retrieval

Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset.
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data. One proposal to scale OoD detection to high-dimensional data is to learn a tractable likelihood approximation of the training distribution, and use it to reject unlikely inputs. However, likelihood models on natural data are themselves susceptible to OoD errors, and even assign large likelihoods to samples from other datasets. To mitigate this problem, we propose Generative Ensembles, which robustify density-based OoD detection by way of estimating epistemic uncertainty of the likelihood model. We present a puzzling observation in need of an explanation -- although likelihood measures cannot account for the typical set of a distribution, and therefore should not be suitable on their own for OoD detection, WAIC performs surprisingly well in practice.
Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks

We propose a novel opportunistic ambient backscatter communication (ABC) framework for radio frequency (RF)-powered cognitive radio (CR) networks. The proposed framework considers opportunistic spectrum sensing integrated with ABC and harvest-then-transmit (HTT) operation strategies. Novel analytic expressions are derived for the average throughput, average energy consumption and energy efficiency in the considered set up. In addition, we formulate an optimization problem to maximize the energy efficiency of the CR system operating in mixed ABC- and HTT-modes, subject to primary interference and energy harvesting constraints. Next, we determine the optimal set of parameters which in turn comprise the optimal detection threshold, and the optimal degree of tradeoff between the CR system operating in the ABC- and HTT-modes. We present extensive numerical results to corroborate our analysis and to demonstrate the performance gain of the proposed model in terms of energy efficiency.
Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints

In this paper, we introduce a novel concept for learning of the parameters in a neural network. Our idea is grounded on modeling a learning problem that addresses a trade-off between (i) satisfying local objectives at each node and (ii) achieving desired data propagation through the network under (iii) local propagation constraints. We consider two types of nonlinear transforms which describe the network representations. One of the nonlinear transforms serves as activation function. The other one enables a locally adjusted, deviation corrective components to be included in the update of the network weights in order to enable attaining target specific representations at the last network node. Our learning principle not only provides insight into the understanding and the interpretation of the learning dynamics, but it offers theoretical guarantees over decoupled and parallel parameter estimation strategy that enables learning in synchronous and asynchronous mode. Numerical experiments validate the potential of our approach on image recognition task. The preliminary results show advantages in comparison to the state-of-the-art methods, w.r. t. the learning time and the network size while having competitive recognition accuracy.
Testosterone Promotes Glioblastoma Cell Proliferation, Migration, and Invasion Through Androgen Receptor Activation

Glioblastomas (GBM) are the most frequent and aggressive human brain tumors due to their high capacity to migrate and invade normal brain tissue. Epidemiological data report that GBM occur in a greater proportion in men than in women (3:2), suggesting the participation of sex hormones in the development of these tumors. It has been reported an increase in testosterone (T) levels in patients with GBM. In addition, androgen receptor (AR) is overexpressed in human GBM, and genetic silencing of AR, and its pharmacological inhibition, induce GBM cell death in vivo and in vitro. However, the role of T in proliferation, migration and invasion in human GBM cell lines has not been evaluated. We observed that T increased the number of U87, U251, and D54 cells derived from human GBM due to an increase in cell proliferation. This induction was blocked with flutamide, an antagonist of AR. T also induced migration and invasion of GBM cells that flutamide partially blocked. These data suggest that T through AR contributes to the progression of GBM by promoting proliferation, migration, and invasion.
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points

In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions. This result subverts the classical belief that SGD requires at least $O(\epsilon^{-4})$ stochastic gradient computations for obtaining an $(\epsilon,O(\epsilon^{0.5}))$-approximate second-order stationary point. Such SGD rate matches, up to a polylogarithmic factor of problem-dependent parameters, the rate of most accelerated nonconvex stochastic optimization algorithms that adopt additional techniques, such as Nesterov's momentum acceleration, negative curvature search, as well as quadratic and cubic regularization tricks. Our novel analysis gives new insights into nonconvex SGD and can be potentially generalized to a broad class of stochastic optimization algorithms.
Extragonadal FSHR Expression and Function—Is It Real?

Expression of the follicle-stimulating hormone receptor (FSHR), besides gonadal tissues, has recently been detected in several extragonadal normal and tumorous tissues, including different types of primary and metastatic cancer and tumor vessel endothelial cells (TVEC). The suggested FSH actions in extragonadal tissues include promotion of angiogenesis, myometrial contractility, skeletal integrity, and adipose tissue accumulation. Non-malignant cells within cancer tissue have been shown to be devoid of FSHR expression, which implies a potential role of FSHR as a diagnostic, prognostic, or even a therapeutic tool. There are shared issues between several of the published reports questioning the validity of some of the conclusion. Firstly, protein expression of FSHR was performed solely with immunohistochemistry (IHC) using either an unavailable “in house” FSHR323 monoclonal antibody or poorly validated polyclonal antibodies, usually without additional methodological quality control and confirmations. Secondly, there is discrepancy between the hardly traceable or absent FSHR gene amplification/transcript data and non-reciprocal strong FSHR protein immunoreactivity. Thirdly, the pharmacological high doses of recombinant FSH used in in vitro studies also jeopardizes the physiological or pathophysiological meaning of the findings. We performed in this review a critical analysis of the results presenting extragonadal expression of FSHR and FSH action, and provide a rationale for the validation of the reported results using additional more accurate and sensitive supplemental methods, including in vivo models and proper positive and negative controls.
Mini Review: Antimicrobial Control of Chlamydial Infections in Animals: Current Practices and Issues

Chlamydia are a genus of successful obligate intracellular pathogens spread across humans, wildlife, and domesticated animals. The most common species reported in livestock in this genus are Chlamydia abortus, Chlamydia psittaci, Chlamydia suis, and Chlamydia pecorum. Chlamydial infections trigger a series of inflammatory disease-related sequelae including arthritis, conjunctivitis, pneumonia, and abortion. Other bacteria in the phylum Chlamydiae have also been reported in livestock and wildlife but their impact on animal health is less clear. Control of chlamydial infections relies on the use of macrolides, fluoroquinolones, and tetracyclines. Tetracycline resistance (TETR) reported for porcine C. suis strains in association with the use of tetracycline feed is a potentially significant concern given experimental evidence highlighting that the genetic elements inferring TETR may be horizontally transferred to other chlamydial species. As documented in human Chlamydia trachomatis infections, relapse of infections, bacterial shedding post-antibiotic treatment, and disease progression despite chlamydial clearance in animals have also been reported. The identification of novel chlamydiae as well as new animal hosts for previously described chlamydial pathogens should place a renewed emphasis on basic in vivo studies to demonstrate the efficacy of existing and new antimicrobial treatment regimes. Building on recent reviews of antimicrobials limited to C. trachomatis and C. suis, this review will explore the use of antimicrobials, the evidence and factors that influence the treatment failure of chlamydial infections in animals and the future directions in the control of these important veterinary pathogens.
Modeling Probabilistic Flooding in VANETs for Optimal Rebroadcast Probabilities

Probabilistic flooding is commonly considered in vehicular ad-hoc networks (VANETs) to alleviate the celebrated broadcast storm problem. Heuristics and simulations have so far proved to be an efficient way with which to design probabilistic information dissemination solutions with demonstrated effectiveness. Supplementing with mathematical analysis for both design and evaluation is expected to generalize the results, and increase the confidence in the proposed solutions. So far, this has been limited due to the difficulties associated with the underlying stochastic dependencies. In this paper, we introduce simple models of single and multiple lane roads to design effective probabilistic flooding schemes for VANETs. The proposed analytical framework offers a novel and generic tool which can cover any number of lanes, vehicle transmission range, and density. We derive difference equations whose solutions yield the probability of all vehicles receiving the emergency warning message as a function of the rebroadcast probability, the number of neighbors of each vehicle, and the dissemination distance. The models are validated using simulations, realistically representing both the traffic and networking aspects, and are used as a baseline to design two schemes for information dissemination which employ probabilistic flooding. The one attempts direct calculation of the required parameters whereas the other adaptively regulates the rebroadcast probability based on the vehicle speed. The schemes are evaluated using simulations and are found to achieve significant performance improvements compared to blind flooding with respect to the achieved reachability, end-to-end delay, and number of rebroadcasts, comparable to the performance achieved by optimal flooding, obtained via brute force.
Falsified Data Attack on Backpressure-based Traffic Signal Control Algorithms

In urban transportation, scheduling algorithms in traffic signal control (TSC) are important for achieving high throughput and low latency traffic flow, lowering accidents, and reducing emissions. As new scheduling algorithms are being developed particularly to leverage and accommodate connected and autonomous vehicles, there is increased potential for cyber-attacks on TSC that can undermine the benefits of new algorithms. Attackers can learn the behavior of scheduling algorithms and launch attacks to get scheduling priority and/or to create traffic panic and congestion. These attacks can compromise the system and significantly increase traffic delay and make TSC completely ineffective. In this paper, we compare the performance of different backpressure-based scheduling algorithms when they are under attack. We consider four different backpressure-based schemes, namely, delay-based, queue-based, sum-of-delay-based, and hybrid scheme that combines delay-based and queue-based schemes. We consider time spoofing attacks where individual vehicles arriving at an intersection can alter their arrival times. Through detailed simulation analysis we show that while the delay-based scheme has better fairness performance, it is more vulnerable to time spoofing attacks than the other schemes. We explore drawbacks of the delay-based scheme under different scenarios including non-homogeneous arrivals both for isolated intersection as well as multiple intersections. This study throws light on how to prevent time spoofing attacks on next generation TSC.
360° Semantic File System: Augmented Directory Navigation for Nonhierarchical Retrieval of Files

The organization of files in any desktop computer has been an issue since their inception. The file systems that are available today organize files in a strict hierarchy that facilitates their retrieval either through navigation, clicking directories and sub-directories, in a tree-like structure or by searching (which allows for finding of the desired files using a search tool). Research studies show that the users rarely (4%–15%) use the latter approach, thus leaving navigation as the main mechanism for retrieving files. However, navigation does not allow a user to retrieve files nonhierarchically, which makes it limited in terms of time, human effort, and cognitive overload. To mitigate this issue, several semantic file systems (SFSs) have been periodically proposed that have made the nonhierarchical navigation of files possible by exploiting some basic semantics but no more than that. None of these systems consider aspects such as time, location, file movement, content similarity, and territory together with learning from user file retrieval behaviors in identifying the desired file and accessing it in less time and with minimum human and cognitive efforts. Moreover, most of the available SFSs replace the existing file system metaphor, which is normally not acceptable to users. To mitigate these issues, this research paper proposes 360° SFS that exploits the SFS ontology to capture all the possible relevant file metadata and learns from user browsing behaviors to semantically retrieve the desired files both easily and timely. Based on user studies, the evaluation results show that the proposed 360° SFS outperforms the existing traditional directory navigation and recently open files.
Flow Based Anomaly Detection in Software Defined Networking: A Deep Learning Approach With Feature Selection Method

Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential in shaping the future of networking by separating control plane from data plane. OpenFlow is the first and most widely used protocol that makes this separation possible in the first place. As a newly emerged technology, SDN has its inherent security threats that can be eliminated or at least mitigated by securing the OpenFlow controller that manages flow control in SDN. A flow based anomaly detection method in OpenFlow controller using Deep Neural Network (DNN) have been approached in this research. Hence, in this exploration, we propose a combined Gated Recurrent Unit Long Short Term Memory (GRU-LSTM) Network intrusion detection system. In order to improve the classifier performance, an appropriate ANOVA F-Test and Recursive feature Elimination (RFE) (ANOVA F-RFE) feature selection method also have been applied. The proposed approach is tested using the benchmark dataset NSL-KDD. A subset of complete dataset with convenient feature selection ensures the highest accuracy of 87% with GRU-LSTM Model. Experimental results show that deep-learning approach with feature selection method offers high potential for flow-based anomaly detection in OpenFlow controller.