Miracle-dz/UniFAR-FTUs huggingface dataset:
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits

Logistic Bandits have recently attracted substantial attention, by providing an uncluttered yet challenging framework for understanding the impact of non-linearity in parametrized bandits. It was shown by Faury et al. (2020) that the learning-theoretic difficulties of Logistic Bandits can be embodied by a large (sometimes prohibitively) problem-dependent constant $\kappa$, characterizing the magnitude of the reward's non-linearity. In this paper we introduce a novel algorithm for which we provide a refined analysis. This allows for a better characterization of the effect of non-linearity and yields improved problem-dependent guarantees. In most favorable cases this leads to a regret upper-bound scaling as $\tilde{\mathcal{O}}(d\sqrt{T/\kappa})$, which dramatically improves over the $\tilde{\mathcal{O}}(d\sqrt{T}+\kappa)$ state-of-the-art guarantees. We prove that this rate is minimax-optimal by deriving a $\Omega(d\sqrt{T/\kappa})$ problem-dependent lower-bound. Our analysis identifies two regimes (permanent and transitory) of the regret, which ultimately re-conciliates Faury et al. (2020) with the Bayesian approach of Dong et al. (2019). In contrast to previous works, we find that in the permanent regime non-linearity can dramatically ease the exploration-exploitation trade-off. While it also impacts the length of the transitory phase in a problem-dependent fashion, we show that this impact is mild in most reasonable configurations.
Curvature bounce in general relativity: background and primordial spectrum

Recent data suggest that the Universe could be positively curved. Combined with an inflationary stage, this might lead to a curvature bounce instead of the Big Bang. The background evolution is presented, as a function of the parameters controlling the cosmic evolution. The primordial tensor spectrum is also calculated and possible observational footprints of the model are underlined. Several potentials are considered and general remarks are made about “naturalness” in this context.
Robust and Consistent Estimation of Word Embedding for Bangla Language by fine-tuning Word2Vec Model

Word embedding or vector representation of word holds syntactical and semantic characteristics of a word which can be an informative feature for any machine learning-based models of natural language processing. There are several deep learning-based models for the vectorization of words like word2vec, fasttext, gensim, glove, etc. In this study, we analyze word2vec model for learning word vectors by tuning different hyper-parameters and present the most effective word embedding for Bangla language. For testing the performances of different word embeddings generated by fine-tuning of word2vec model, we perform both intrinsic and extrinsic evaluations. We cluster the word vectors to examine the relational similarity of words for intrinsic evaluation and also use different word embeddings as the feature of news article classifier for extrinsic evaluation. From our experiment, we discover that the word vectors with 300 dimensions, generated from “skip-gram” method of word2vec model using the sliding window size of 4, are giving the most robust vector representations for Bangla language.
Effective Distant Supervision for Temporal Relation Extraction

A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where the temporal relations are made explicit in text, then mask out those explicit cues, forcing a model trained on this data to learn other signals. We demonstrate that a pre-trained Transformer model is able to transfer from the weakly labeled examples to human-annotated benchmarks in both zero-shot and few-shot settings, and that the masking scheme is important in improving generalization.
Molecular characterization of ageratum enation virus and DNA-satellites associated with yellowing and leaf curl symptoms on mulberry in Pakistan

Abstract The whitefly-transmitted begomoviruses (family Geminiviridae) infect dicotyledonous plants and occur in tropical and sub-tropical regions. Although most commonly found in herbaceous plants, recently begomoviruses have increasingly been identified in woody plants. Leaf samples from five mulberry (Morus alba L.) plants with leaf yellowing and curling symptoms were collected in Lahore (Pakistan) and shown by PCR to be associated with a begomovirus, an alphasatellite and a betasatellite. The complete sequences of two begomovirus clones, as well as an alphasatellite clone and a betasatellite clone, were determined. The begomovirus clones were shown to be isolates of ageratum enation virus (AEV), a virus most commonly identified in weeds but increasingly being identified in crops such as tomato, soybean and fenugreek. Analyses of the sequences of the alphasatellite and betasatellite clones showed them to be isolates of Guar leaf curl alphasatellite and Papaya leaf curl betasatellite, respectively. Both the virus and the alphasatellite sequences showed evidence of a recombination. This is the first report of the weed-infecting monopartite begomovirus AEV, and associated satellites, infecting the woody plant mulberry.
The MKID Exoplanet Camera for Subaru SCExAO

We present the MKID Exoplanet Camera (MEC), a z through J band (800–1400 nm) integral field spectrograph located behind The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) at the Subaru Telescope on Maunakea that utilizes Microwave Kinetic Inductance Detectors (MKIDs) as the enabling technology for high contrast imaging. MEC is the first permanently deployed near-infrared MKID instrument and is designed to operate both as an IFU, and as a focal plane wavefront sensor in a multi-kHz feedback loop with SCExAO. The read noise free, fast time domain information attainable by MKIDs allows for the direct probing of fast speckle fluctuations that currently limit the performance of most high contrast imaging systems on the ground and will help MEC achieve its ultimate goal of reaching contrasts of 10−7 at 2 λ/D. Here we outline the instrument details of MEC including the hardware, firmware, and data reduction and analysis pipeline. We then discuss MEC’s current on-sky performance and end with future upgrades and plans.
Local SGD for Saddle-Point Problems

GAN is one of the most popular and commonly used neural network models. When the model is large and there is a lot of data, the learning process can be delayed. The standard way out is to use multiple devices. Therefore, the methods of distributed and federated training for GANs are an important question. But from an optimization point of view, GANs are nothing more than a classical saddle-point problem: $\min_x \max_y f(x,y)$. Therefore, this paper focuses on the distributed optimization of the smooth stochastic saddle-point problems using Local SGD. We present a new algorithm specifically for our problem -- Extra Step Local SGD. The obtained theoretical bounds of communication rounds are $\Omega(K^{2/3} M^{1/3})$ in strongly-convex-strongly-concave case and $\Omega(K^{8/9} M^{4/9})$ in convex-concave (here $M$ -- number of functions (nodes) and $K$ - number of iterations).
Asymptotic Behavior of Adversarial Training in Binary Classification

It is widely known that several machine learning models are susceptible to adversarial attacks i.e., small adversarial perturbations applied to data points causing the model to misclassify the data. Adversarial training using empirical risk minimization methods, is the state-of-the-art method for defense against adversarial attacks. Despite being successful, several problems in understanding generalization performance of adversarial training remain open. In this paper, we derive precise theoretical predictions for the performance of adversarial training in binary linear classification. We consider the modern high-dimensional regime where the dimension of data grows with the size of the training dataset at a constant ratio. Our results provide exact asymptotics for the performance of estimators obtained by adversarial training with $\ell_q$-norm bounded perturbations ($q \ge 1$) and for binary labels and Gaussian features. These sharp predictions enable us to explore the role of various factors including over-parametrization ratio, data model and attack budget on the performance of adversarial training.
Controlling secondary flows in Taylor-Couette flow using inhomogeneous boundary conditions

Taylor-Couette (TC) flow, the flow between two independently rotating and co-axial cylinders is commonly used as a canonical model for shear flows. Unlike plane Couette or channel flow, pinned secondary flows can be found in TC flow. These are known as Taylor rolls and drastically affect the flow behaviour and the resulting drag. We study the possibility of modifying these secondary structures using patterns of stress-free and no-slip boundary conditions on the inner cylinder. For this, we perform direct numerical simulations of narrow-gap TC flow with pure inner cylinder rotation at four different shear Reynolds numbers up to $Re_s=3\times 10^4$. We find that one-dimensional streamwise patterns do not have a significant effect on the flow topology, and that the resulting torque is a large fraction ($\sim80-90\%$) of torque in the fully no-slip case. One-dimensional spanwise decrease the torque more, and for certain wavelengths disrupt the rolls by interfering with the existing Reynolds stresses that generate secondary structures. For $Re\geq 10^4$, this disruption leads to a smaller torque than what would be expected from simple boundary layer effects and the resulting effective slip length and slip velocity. We find that two-dimensional checkerboard patterns do not affect the flow or the torque substantially, but two-dimensional spiral inhomogeneities can move around the pinned secondary flows as they induce persistent axial velocities. We quantify the roll's movement for various angles and the widths of the spiral pattern, and find a non-monotonic behaviour as a function of pattern angle and pattern wavelength.
Aggregate-Driven Trace Visualizations for Performance Debugging

Performance issues in cloud systems are hard to debug. Distributed tracing is a widely adopted approach that gives engineers visibility into cloud systems. Existing trace analysis approaches focus on debugging single request correctness issues but not debugging single request performance issues. Diagnosing a performance issue in a given request requires comparing the performance of the offending request with the aggregate performance of typical requests. Effective and efficient debugging of such issues faces three challenges: (i) identifying the correct aggregate data for diagnosis; (ii) visualizing the aggregated data; and (iii) efficiently collecting, storing, and processing trace data. We present TraVista, a tool designed for debugging performance issues in a single trace that addresses these challenges. TraVista extends the popular single trace Gantt chart visualization with three types of aggregate data - metric, temporal, and structure data, to contextualize the performance of the offending trace across all traces.
Identification of Blood-based Biomarkers for Early Stage Parkinson's Disease

Parkinson's disease (PD) affects millions of people worldwide and causes symptoms such as bradykinesia and disrupted speech. Parkinson's disease is known to be characterized by the mass death of dopaminergic neurons in the substantia nigra region. In the status quo, PD is often diagnosed at late stages because obvious motor symptoms appear after the disease has progressed far. It is advantageous to diagnose PD before the onset of motor symptoms because treatments are often more effective at early stages. While motor symptoms usually manifest when over 50% of dopaminergic neurons in the substantia nigra are already lost, molecular signatures of PD may be present at early stages in patient blood. This study aimed to analyze several gene expression studies' data for commonly differentially expressed genes (DEGs) in the blood of early stage PD patients. 147 DEGs were identified in at least two out of three datasets and passed cut-off criteria. A protein interaction network for the DEGs was constructed and various tools were used to identify network characteristics and hub genes. PANTHER analysis revealed that the biological process "cellular response to glucagon stimulus" was overrepresented by almost 21 times among the DEGs and "lymphocyte differentiation" by 5.98 times. Protein catabolic processes and protein kinase functions were also overrepresented. ESR1, CD19, SMAD3, FOS, CXCR5, and PRKACA may be potential biomarkers and warrant further study. Overall, the findings of the present study provide insights on molecular mechanisms of PD and provide greater confidence on which genes are differentially expressed in PD. The results also are additional evidence for the role of the immune system in PD, a topic that is gaining interest in the PD research community.
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Objective. Brain–computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. Approach. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic and magnetoencephalographic data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. Main results. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α 2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Significance. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
Attention-Guided Network for Iris Presentation Attack Detection

Convolutional Neural Networks (CNNs) are being increasingly used to address the problem of iris presentation attack detection. In this work, we propose attention-guided iris presentation attack detection (AG-PAD) to augment CNNs with attention mechanisms. Two types of attention modules are independently appended on top of the last convolutional layer of the backbone network. Specifically, the channel attention module is used to model the inter-channel relationship between features, while the position attention module is used to model inter-spatial relationship between features. An element-wise sum is employed to fuse these two attention modules. Further, a novel hierarchical attention mechanism is introduced. Experiments involving both a JHU-APL proprietary dataset and the benchmark LivDet-Iris-2017 dataset suggest that the proposed method achieves promising results. To the best of our knowledge, this is the first work that exploits the use of attention mechanisms in iris presentation attack detection.
Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot

Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we first consider incorporating operational space visual and haptic information into a reinforcement learning (RL) method to solve the target uncertainty problems in unstructured environments. Moreover, we propose a novel idea of introducing a proactive action to solve a partially observable Markov decision process (POMDP) problem. With these two ideas, our method can either adapt to reasonable variations in unstructured environments or improve the sample efficiency of policy learning. We evaluated our method on a task that involved inserting a random-access memory (RAM) using a torque-controlled robot and tested the success rates of different baselines used in the traditional methods. We proved that our method is robust and can tolerate environmental variations.
XLVIN: eXecuted Latent Value Iteration Nets

Value Iteration Networks (VINs) have emerged as a popular method to incorporate planning algorithms within deep reinforcement learning, enabling performance improvements on tasks requiring long-range reasoning and understanding of environment dynamics. This came with several limitations, however: the model is not incentivised in any way to perform meaningful planning computations, the underlying state space is assumed to be discrete, and the Markov decision process (MDP) is assumed fixed and known. We propose eXecuted Latent Value Iteration Networks (XLVINs), which combine recent developments across contrastive self-supervised learning, graph representation learning and neural algorithmic reasoning to alleviate all of the above limitations, successfully deploying VIN-style models on generic environments. XLVINs match the performance of VIN-like models when the underlying MDP is discrete, fixed and known, and provides significant improvements to model-free baselines across three general MDP setups.
Multiplex Cell Fate Tracking by Flow Cytometry

Measuring differences in cell cycle progression is often essential to understand cell behavior under different conditions, treatments and environmental changes. Cell synchronization is widely used for this purpose, but unfortunately, there are many cases where synchronization is not an option. Many cell lines, patient samples or primary cells cannot be synchronized, and most synchronization methods involve exposing the cells to stress, which makes the method incompatible with the study of stress responses such as DNA damage. The use of dual-pulse labelling using EdU and BrdU can potentially overcome these problems, but the need for individual sample processing may introduce a great variability in the results and their interpretation. Here, we describe a method to analyze cell proliferation and cell cycle progression by double staining with thymidine analogues in combination with fluorescent cell barcoding, which allows one to multiplex the study and reduces the variability due to individual sample staining, reducing also the cost of the experiment.
An Adiabatic Theorem for Policy Tracking with TD-learning

We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time. Our results apply a new adiabatic theorem that bounds the mixing time of time-inhomogeneous Markov chains. We derive finite-time bounds for tabular temporal difference learning and $Q$-learning when the policy used for training changes in time. To achieve this, we develop bounds for stochastic approximation under asynchronous adiabatic updates.
Measuring the ‘I don’t know’ Problem through the Lens of Gricean Quantity

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice’s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’t know’ problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, ‘I don’t know’ is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the ‘I don’t know’ problem, which has been addressed but not analyzed by prior work.
An Approach to Evaluating Learning Algorithms for Decision Trees

Learning algorithms produce software models for realising critical classification tasks. Decision trees models are simpler than other models such as neural network and they are used in various critical domains such as the medical and the aeronautics. Low or unknown learning ability algorithms does not permit us to trust the produced software models, which lead to costly test activities for validating the models and to the waste of learning time in case the models are likely to be faulty due to the learning inability. Methods for evaluating the decision trees learning ability, as well as that for the other models, are needed especially since the testing of the learned models is still a hot topic. We propose a novel oracle-centered approach to evaluate (the learning ability of) learning algorithms for decision trees. It consists of generating data from reference trees playing the role of oracles, producing learned trees with existing learning algorithms, and determining the degree of correctness (DOE) of the learned trees by comparing them with the oracles. The average DOE is used to estimate the quality of the learning algorithm. the We assess five decision tree learning algorithms based on the proposed approach.
Some Theoretical Results Concerning Time-varying Nonparametric Regression with Local Stationary Regressors and Error.

With regard to a three-step estimation procedure, proposed without theoretical discussion by Li and You in Journal of Applied Statistics and Management, for a nonparametric regression model with time-varying regression function, local stationary regressors and time-varying AR(p) (tvAR(p)) error process , we established all necessary asymptotic properties for each of estimator. We derive the convergence rate and asymptotic normality of the preliminary estimation of nonparametric regression function, establish the asymptotic distribution of time-varying coefficient functions in the error term, and present the asymptotic property of the refined estimation of nonparametric regression function. In addition, with regard to the ULASSO method for variable selection and constant coefficient detection for error term structure, we show that the ULASSO estimator can identify the true error term structure consistently. We conduct two simulation studies to illustrate the finite sample performance of the estimators and validate our theoretical discussion on the properties of the estimators.