Miracle-dz/UniFAR-FTUs huggingface dataset:
Online adaptive group-wise sparse NPLS for ECoG neural signal decoding

Objective. Brain-computer interfaces (BCIs) create a new communication pathway between the brain and an effector without neuromuscular activation. BCI experiments highlighted high intra and inter-subjects variability in the BCI decoders. Although BCI model is generally relying on neurological markers generalizable on the majority of subjects, it requires to generate a wide range of neural features to include possible neurophysiological patterns. However, the processing of noisy and high dimensional features, such as brain signals, brings several challenges to overcome such as model calibration issues, model generalization and interpretation problems and hardware related obstacles. Approach. An online adaptive group-wise sparse decoder named Lp-Penalized REW-NPLS algorithm (PREW-NPLS) is presented to reduce the feature space dimension employed for BCI decoding. The proposed decoder was designed to create BCI systems with low computational cost suited for portable applications and tested during offline pseudo-online study based on online closed-loop BCI control of the left and right 3D arm movements of a virtual avatar from the ECoG recordings of a tetraplegic patient. Main results. PREW-NPLS algorithm highlight at least as good decoding performance as REW-NPLS algorithm. However, the decoding performance obtained with PREW-NPLS were achieved thanks to sparse models with up to 64% and 75% of the electrodes set to 0 for the left and right hand models respectively using L1-PREW-NPLS. Significance. The designed solution proposed an online incremental adaptive algorithm suitable for online adaptive decoder calibration which estimate sparse decoding solutions. The PREW-NPLS models are suited for portable applications with low computational power using only small number of electrodes with degrading the decoding performance.
Robust Imitation Learning from Noisy Demonstrations

Learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation learning can be achieved by optimizing a classification risk with a symmetric loss. Based on this theoretical finding, we then propose a new imitation learning method that optimizes the classification risk by effectively combining pseudo-labeling with co-training. Unlike existing methods, our method does not require additional labels or strict assumptions about noise distributions. Experimental results on continuous-control benchmarks show that our method is more robust compared to state-of-the-art methods.
Radiation Problems Accompanying Carrier Production by an Electric Field in the Graphene

A number of physical processes that occur in a flat one-dimensional graphene structure under the action of strong time-dependent electric fields are considered. It is assumed that the Dirac model can be applied to the graphene as a subsystem of the general system under consideration, which includes an interaction with quantized electromagnetic field. The Dirac model itself in the external electromagnetic field (in particular, the behavior of charged carriers) is treated nonperturbatively with respect to this field within the framework of strong-field QED with unstable vacuum. This treatment is combined with a kinetic description of the radiation of photons from the electron-hole plasma created from the vacuum under the action of the electric field. An interaction with quantized electromagnetic field is described perturbatively. A significant development of the kinetic equation formalism is presented. A number of specific results are derived in the course of analytical and numerical study of the equations. We believe that some of predicted effects and properties of considered processes may be verified experimentally. Among these effects, it should be noted a characteristic spectral composition anisotropy of the quantum radiation and a possible presence of even harmonics of the external field in the latter radiation.
A Flatter Loss for Bias Mitigation in Cross-dataset Facial Age Estimation

The most existing studies in the facial age estimation assume training and test images are captured under similar shooting conditions. However, this is rarely valid in real-worlds applications, where training and test sets usually have different characteristics. In this paper, we advocate a cross-dataset protocol for age estimation benchmarking. In order to improve the cross-dataset age estimation performance, we mitigate the inherent bias caused by the learning algorithm itself. To this end, we propose a novel loss function that is more effective for neural network training. The relative smoothness of the proposed loss function is its advantage with regards to the optimisation process performed by stochastic gradient descent (SGD). Compared with existing loss functions, the lower gradient of the proposed loss function leads to the convergence of SGD to a better optimum point, and consequently a better generalisation. The cross-dataset experimental results demonstrate the superiority of the proposed method over the state-of-the-art algorithms in terms of accuracy and generalisation capability.
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation

MOTIVATION Innovative microfluidic systems carry the promise to greatly facilitate spatio-temporal analysis of single cells under well-defined environmental conditions, allowing novel insights into population heterogeneity and opening new opportunities for fundamental and applied biotechnology. Microfluidics experiments, however, are accompanied by vast amounts of data, such as time series of microscopic images, for which manual evaluation is infeasible due to the sheer number of samples. While classical image processing technologies do not lead to satisfactory results in this domain, modern deep learning technologies such as convolutional networks can be sufficiently versatile for diverse tasks, including automatic cell counting as well as the extraction of critical parameters, such as growth rate. However, for successful training, current supervised deep learning requires label information, such as the number or positions of cells for each image in a series; obtaining these annotations is very costly in this setting. RESULTS We propose a novel machine learning architecture together with a specialized training procedure, which allows us to infuse a deep neural network with human-powered abstraction on the level of data, leading to a high-performing regression model that requires only a very small amount of labeled data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated. AVAILABILITY The project is cross-platform, open-source and free (MIT licensed) software. We make the source code available at https://github.com/dstallmann/cell_cultivation_analysis; the data set is available at https://pub.uni-bielefeld.de/record/2945513.
Analytical representation of an iterative formula for a quantum wave impedance determination in a case of a piecewise constant potential.

An analytical solution for a quantum wave impedance in a case of piesewise constant potential was derived. It is in fact an analytical depiction of a well-known iterative method of a quantum wave impedance determination. The expression for a transmission probability as a function of a particle energy for an arbitrary cascad of constant potentials was obtained. The application of obtained results was illustrated on a system of double-well/barrier structures.
Color Image Segmentation Metrics

An automatic image segmentation procedure is an inevitable part of many image analyses and computer vision which deeply affect the rest of the system; therefore, a set of interactive segmentation evaluation methods can substantially simplify the system development process. This entry presents the state of the art of quantitative evaluation metrics for color image segmentation methods by performing an analytical and comparative review of the measures. The decision-making process in selecting a suitable evaluation metric is still very serious because each metric tends to favor a different segmentation method for each benchmark dataset. Furthermore, a conceptual comparison of these metrics is provided at a high level of abstraction and is discussed for understanding the quantitative changes in different image segmentation results.
User-Number Threshold-Based Base Station On/Off Control for Maximizing Coverage Probability

In this study, we investigate the operation of a user-number threshold-based base station (BS) on/off control, in which the BS turns off when the number of users is less than a specific threshold value. This paper presents a space-based analysis of the BS on/off control system to which a stochastic geometric approach is applied. In particular, we derive the approximated closed-form expression of the coverage probability of a homogeneous network (HomNet) with the user-number threshold-based BS on/off control. Moreover, the optimal user-number threshold for maximizing coverage probability is analytically derived. In addition to HomNet, we also derive the approximated closed-form expression of the coverage probability and the optimal user-number thresholds for a heterogeneous network (HetNet). The results show that HetNet, the analysis of which seems intractable, can be analyzed in the form of a linear combination of HomNets with weighted densities. In addition, the optimal user-number threshold of each tier is obtained independently from other tiers. The modeling and analysis presented in this paper are not only limited to the case of user-number threshold-based on/off control, but also applicable to other novel on/off controls with minor modifications. Finally, by comparing the analytical results with the simulated results, the theoretical contributions of this study are validated.
Extracting Procedural Knowledge from Technical Documents

Procedures are an important knowledge component of documents that can be leveraged by cognitive assistants for automation, question-answering or driving a conversation. It is a challenging problem to parse big dense documents like product manuals, user guides to automatically understand which parts are talking about procedures and subsequently extract them. Most of the existing research has focused on extracting flows in given procedures or understanding the procedures in order to answer conceptual questions. Identifying and extracting multiple procedures automatically from documents of diverse formats remains a relatively less addressed problem. In this work, we cover some of this ground by -- 1) Providing insights on how structural and linguistic properties of documents can be grouped to define types of procedures, 2) Analyzing documents to extract the relevant linguistic and structural properties, and 3) Formulating procedure identification as a classification problem that leverages the features of the document derived from the above analysis. We first implemented and deployed unsupervised techniques which were used in different use cases. Based on the evaluation in different use cases, we figured out the weaknesses of the unsupervised approach. We then designed an improved version which was supervised. We demonstrate that our technique is effective in identifying procedures from big and complex documents alike by achieving accuracy of 89%.
Translation and Cross-cultural Adaptation of the Fremantle Back Awareness Questionnaire into Persian language and the assessment of reliability and validity in patients with chronic low back pain

Background: Chronic low back pain (LBP) causes some neuroplastic changes in the brain, which result in body perception impairment. The Fremantle Back Awareness Questionnaire (FreBAQ) is a suggested tool for the diagnosis and evaluation of back perception in people with LBP. The aim of this study is to translate and cross culturally adapt the FreBAQ into Persian language and to assess its reliability and validity in patients with chronic LBP (CLBP). Materials and Methods: Fifty people with CLBP and fifty healthy people participated in this study. To evaluate the discriminant validity, we assessed the ability of the FreBAQ to discriminate between people with and without LBP. After an interval of 1 week, 25 patients with CLBP completed the questionnaire in the retest session. Data obtained from the first test administration were used for internal consistency and data obtained from repeated testing were used for test–retest reliability. Construct validity was assessed by investigating a correlation between the FreBAQ with the Roland–Morris Disability Questionnaire (RDQ), Visual Analog Scale, Pain Catastrophizing Scale (PCS), Hospital Anxiety and Depression Scale, and Tampa Scale of Kinesiophobia. In addition, the construct validity of Persian FreBAQ was measured by factor analysis. Results: The test–retest reliability of the questionnaire was confirmed by intraclass correlation coefficient = 0.96. Cronbach's alpha was 0.74 for Persian FreBAQ. The standard error of measurement and minimal detectable change were 0.91 and 2.52, respectively. Construct validity was demonstrated by statistically significant relationship between the Persian FreBAQ and questionnaires of PCS (P < 0.001) and RDQ (P = 0.01). Conclusion: The Persian version of FreBAQ is a valid and reliable measurement tool for evaluating back perception changes in Persian-speaking patients with LBP.
Waste Product of Fruits and Vegetables Processing as A Source of Dietary Fibre: A Review

Consumers are increasingly aware of diet related health problems, therefore demanding natural ingredients which are expected to be safe and healthpromoting. By-products of plant food processing represent a major disposal problem for the industry concerned, but they are also promising sources of compounds which may be used because of their favourable technological or nutritional properties. These days, dietary fibres obtained from fruits and vegetables processing are given increasing attention because of the beneucial physiological euects they may exert on human health. Dietary ubre includes a number of components, and each of them shows speciuc properties. The components of major importance are cellulose, hemicellulose, lignin and pectins. By-product from fruits and vegetables processing contains a large amount of dietary fibre and can be used for value addition due to their good functional properties. The purpose of this review is to highlight the potential of selected by-products as a source of dietary fibre.
Supertagging-based Parsing with Linear Context-free Rewriting Systems

We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constituents establish a new state of the art. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. We describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure that transforms lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is evaluated on the English Discontinuous Penn Treebank and the German treebanks Negra and Tiger.
Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

This article presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundred attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm—as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.
Decaying dark matter, the 
H0
 tension, and the lithium problem

It has long been known that the sharpened tension between the observed and inferred values of the Hubble constant $H_0$ can be alleviated if a fraction of dark matter particles of type $\chi$ were produced non-thermally in association with photons $\gamma$ through the decays of a heavy and relatively long-lived state, viz. $X \to \chi + \gamma$. It was recently proposed that this model can also resolve the longstanding lithium problem if $M= 4$ MeV and $m = 0.04$ keV, where $M$ and $m$ are respectively the masses of $X$ and $\chi$. We calculate the general functional form describing the number of equivalent light neutrino species $\Delta N_{eff}$ carried by $\chi$. We show that to resolve the $H_0$ tension at the $1\sigma$ level a 55% correction in $m$ is needed and that the required $\Delta N_{eff}$ is excluded at 95% CL by Planck data. We argue in favor of a more complex model of dynamical dark matter to relax cosmological anomalies.
ConjNLI: Natural Language Inference over Conjunctive Sentences

Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic. Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge. Hence, we introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced. These sentences contain single and multiple instances of coordinating conjunctions ("and", "or", "but", "nor") with quantifiers, negations, and requiring diverse boolean and non-boolean inferences over conjuncts. We find that large-scale pre-trained language models like RoBERTa do not understand conjunctive semantics well and resort to shallow heuristics to make inferences over such sentences. As some initial solutions, we first present an iterative adversarial fine-tuning method that uses synthetically created training data based on boolean and non-boolean heuristics. We also propose a direct model advancement by making RoBERTa aware of predicate semantic roles. While we observe some performance gains, ConjNLI is still challenging for current methods, thus encouraging interesting future work for better understanding of conjunctions. Our data and code are publicly available at: this https URL
Dynamical transitions in aperiodically kicked tight-binding models

If a localized quantum state in a tight-binding model with structural aperiodicity is subject to noisy evolution, then it is generally expected to result in diffusion and delocalization. In this work, it is shown that the localized phase of the kicked Aubry-Andr\'e-Harper (AAH) model is robust to the effects of noisy evolution, for long times, provided that some kick is delivered once every time period. However, if strong noisy perturbations are applied by randomly missing kicks, a sharp dynamical transition from a ballistic growth phase at initial times to a diffusive growth phase for longer times is observed. Such sharp transitions are seen even in translationally invariant models. These transitions are related to the existence of flat bands, and using a 2-band model we obtain analytical support for these observations. The diffusive evolution at long times has a mechanism similar to that of a random walk. The time scale at which the sharp transition takes place is related to the characteristics of noise. Remarkably, the wavepacket evolution scales with the noise parameters. Further, using kick sequence modulated by a 'coin toss', it is argued that the correlations in the noise are crucial to the observed sharp transitions.
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

Much of modern day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences to simplified versions. However, adding content can often be useful when difficult concepts and reasoning need to be explained. In this work, we present the first data-driven study of content addition in document simplification, which we call elaborative simplification. We introduce a new annotated dataset of 1.3K instances of elaborative simplification and analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. We establish baselines for elaboration generation using large scale pre-trained language models, and illustrate that considering contextual specificity during generation can improve performance. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
Variational Dynamic Mixtures

Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and cause the generative model to predict mode-averaged dynamics. Mode-averaging is problematic since many real-world sequences are highly multi-modal, and their averaged dynamics are unphysical (e.g., predicted taxi trajectories might run through buildings on the street map). To better capture multi-modality, we develop variational dynamic mixtures (VDM): a new variational family to infer sequential latent variables. The VDM approximate posterior at each time step is a mixture density network, whose parameters come from propagating multiple samples through a recurrent architecture. This results in an expressive multi-modal posterior approximation. In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets from different domains.
Proximal Policy Gradient: PPO with Policy Gradient

In this paper, we propose a new algorithm PPG (Proximal Policy Gradient), which is close to both VPG (vanilla policy gradient) and PPO (proximal policy optimization). The PPG objective is a partial variation of the VPG objective and the gradient of the PPG objective is exactly same as the gradient of the VPG objective. To increase the number of policy update iterations, we introduce the advantage-policy plane and design a new clipping strategy. We perform experiments in OpenAI Gym and Bullet robotics environments for ten random seeds. The performance of PPG is comparable to PPO, and the entropy decays slower than PPG. Thus we show that performance similar to PPO can be obtained by using the gradient formula from the original policy gradient theorem.
POND: Pessimistic-Optimistic oNline Dispatch

This paper considers constrained online dispatch with unknown arrival, reward and constraint distributions. We propose a novel online dispatch algorithm, named POND, standing for Pessimistic-Optimistic oNline Dispatch, which achieves $O(\sqrt{T})$ regret and $O(1)$ constraint violation. Both bounds are sharp. Our experiments on synthetic and real datasets show that POND achieves low regret with minimal constraint violations.