Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of clinical tremor severity using Rank Consistent Ordinal Regression

Tremor is a key diagnostic feature of Parkinson's Disease (PD), Essential Tremor (ET), and other central nervous system (CNS) disorders. Clinicians or trained raters assess tremor severity with TETRAS scores by observing patients. Lacking quantitative measures, inter- or intra- observer variabilities are almost inevitable as the distinction between adjacent tremor scores is subtle. Moreover, clinician assessments also require patient visits, which limits the frequency of disease progress evaluation. Therefore it is beneficial to develop an automated assessment that can be performed remotely and repeatably at patients' convenience for continuous monitoring. In this work, we proposed to train a deep neural network (DNN) with rank-consistent ordinal regression using 276 clinical videos from 36 essential tremor patients. The videos are coupled with clinician assessed TETRAS scores, which are used as ground truth labels to train the DNN. To tackle the challenge of limited training data, optical flows are used to eliminate irrelevant background and statistic objects from RGB frames. In addition to optical flows, transfer learning is also applied to leverage pre-trained network weights from a related task of tremor frequency estimate. The approach was evaluated by splitting the clinical videos into training (67%) and testing sets (0.33%). The mean absolute error on TETRAS score of the testing results is 0.45, indicating that most of the errors were from the mismatch of adjacent labels, which is expected and acceptable. The model predications also agree well with clinical ratings. This model is further applied to smart phone videos collected from a PD patient who has an implanted device to turn"On"or"Off"tremor. The model outputs were consistent with the patient tremor states. The results demonstrate that our trained model can be used as a means to assess and track tremor severity.
Effective temperatures for single particle system under dichotomous noise

Three different definitions of effective temperature— Tk , Ti and Tr related to kinetic theory, system entropy and response theory, respectively—are applied in the description of a non-equilibrium generalised massive Langevin model in contact with dichotomous noise. The differences between the definitions of T naturally wade out as the reservoir reaches its white-noise limit, approaching Gaussian features. The same framework is employed in its overdamped version as well, showing the loss of inertial contributions to the dynamics of the system also makes the three mentioned approaches for effective temperature equivalent.
Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification

Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems. However, SVMs are likely to perform poorly in the classification of imbalanced data. The rough set theory presents a mathematical tool for inference in nondeterministic cases that provides methods for removing irrelevant information from data. In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data. The first innovation is introducing a new fuzzy rough set-based under-sampling strategy to make the classifier robust in terms of the imbalanced data. For constructing the two proximal hyperplanes in FRLSTSVM, data points from the minority class remain unchanged while a subset of data points in the majority class are selected using a new method. In this model, we embed the weight biases in the LSTSVM formulations to overcome the bias phenomenon in the original twin SVM for the classification of imbalanced data. In order to determine these weights in this formulation, we introduce a new strategy that uses fuzzy rough set theory as the second innovation. Experimental results on the famous imbalanced datasets, compared to the related traditional SVM-based methods, demonstrate the superiority of the proposed FRLSTSVM model in the imbalanced data classification.
Semantic Extractor-Paraphraser based Abstractive Summarization

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploits semantic overlap as opposed to its predecessors that focus more on syntactic information overlap. Our model outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word mover similarity (WMS), establishing the superiority of the proposed system via extensive ablation experiments. We have also challenged the summarization capabilities of the state of the art Pointer Generator Network (PGN), and through thorough experimentation, shown that PGN is more of a paraphraser, contrary to the prevailing notion of a summarizer; illustrating it's incapability to accumulate information across multiple sentences.
mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniques

Multiple-instance learning is a subset of weakly supervised learning where labels are applied to sets of instances rather than the instances themselves. Under the standard assumption, a set is positive only there is if at least one instance in the set which is positive. This paper introduces a series of multiple-instance learning benchmarks generated from MNIST, Fashion-MNIST, and CIFAR10. These benchmarks test the standard, presence, absence, and complex assumptions and provide a framework for future benchmarks to be distributed. I implement and evaluate several multiple-instance learning techniques against the benchmarks. Further, I evaluate the Noisy-And method with label noise and find mixed results with different datasets. The models are implemented in TensorFlow 2.4. 1 and are available on GitHub. The benchmarks are available from PyPi as mil-benchmarks and on GitHub.
Unsteady and inertial dynamics of a small active particle in a fluid

It is well known that the reversibility of Stokes flow makes it difficult for small microorganisms to swim. Inertial effects break this reversibility, allowing new mechanisms of propulsion and feeding. Therefore it is important to understand the effects of unsteady and fluid inertia on the dynamics of microorganisms in flow. In this work, we show how to translate known inertial effects for non-motile organisms to motile ones, from passive to active particles. The method relies on a principle used earlier by Legendre and Magnaudet (1997) to deduce inertial corrections to the lift force on a bubble from the inertial drag on a solid sphere, using the fact that small inertial effects are determined by the far field of the disturbance flow. The method allows for example to compute the inertial effect of unsteady fluid accelerations on motile organisms, and the inertial forces such organisms experience in steady shear flow. We explain why the method fails to describe the effect of convective fluid inertia.
Overlooked centrifugal and Coriolis forces in the Atmosphere due to the Earth's motion in the solar system

The slow revolution of the Earth and Moon around their barycentrum does not induce Coriolis accelerations. On the other hand, the motion of Sun and Earth is a rotation with Coriolis forces which appear not to have been calculated yet, nor have the inertial accelerations within the system of motion of all three celestial bodies. It is the purpose of this contribution to evaluate the related Coriolis and centrifugal terms and to compare them to the available atmospheric standard terms. It is a main result that the revolution is of central importance in the combined dynamics of Earth, Moon and Sun. Covariant flow equations are well known tools for dealing with such complicated flow settings. They are used here to quantify the effects of the Earth's revolution around the Earth-Moon barycenter and its rotation around the Sun on the atmospheric circulation. It is found that the motion around the Sun adds time dependent terms to the standard Coriolis forces. The related centrifugal accelerations are presented. A major part of these accelerations is balanced by the gravitational attraction by Moon and Sun, but important unbalanced contributions remain. New light on the consequences of the Earth's revolution is shed by repeating the calculations for a rotating Earth-Moon pair. It is found that the revolution complicates the atmospheric dynamics. 1 Veronica.Egger@biologie.uni-regensburg.de 2 Klaus.Fraedrich@mpimet.mpg. de
Universal role of commuting in the reduction of social assortativity in cities

Millions commute to work every day in cities and interact with colleagues, customers, providers, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. Home and work locations are identified from geolocated tweets that enable us to infer the socio-economic status of individuals. Our results show that an above-median commuting distance in cities is associated with more diverse individual networks in terms of connected peers and their income. The degree that distant commutes link neighborhoods of different socio-economic backgrounds greatly varies by city size and structure. However, we find that above-median commutes are associated with a nearly uniform, moderate reduction of social tie assortativity across the top 50 US cities suggesting a universal role of commuting in integrating disparate social networks in cities. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.
Spectroscopy on the AΠ← XΣ Transition of Buffer-Gas Cooled AlCl

Aluminum monochloride (AlCl) has been proposed as an excellent candidate for laser cooling. Here we present absorption spectroscopy measurements on the AΠ← XΣ transition in AlCl inside a cryogenic helium buffer-gas beam cell. The high resolution absorption data enables a rigorous, quantitative comparison with our high-level ab initio calculations of the electronic and rovibronic energies, providing a comprehensive picture of the AlCl quantum structure. The combination of high resolution spectral data and theory permits the evaluation of spectroscopic constants and associated properties, like equilibrium bond length, with an order of magnitude higher precision. Based on the measured molecular equilibrium constants of the AΠ state, we estimate a Franck-Condon factor of the AΠ← XΣ of 99.88%, which confirms that AlCl is amenable to laser cooling.
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation

In a hybrid speech model, both voiced and unvoiced components can coexist in a segment. Often, the voiced speech is regarded as the deterministic component, and the unvoiced speech and additive noise are the stochastic components. Typically, the speech signal is considered stationary within fixed segments of 20-40 ms, but the degree of stationarity varies over time. For decomposing noisy speech into its voiced and unvoiced components, a fixed segmentation may be too crude, and we here propose to adapt the segment length according to the signal local characteristics. The segmentation relies on parameter estimates of a hybrid speech model and the maximum a posteriori (MAP) and log-likelihood criteria as rules for model selection among the possible segment lengths, for voiced and unvoiced speech, respectively. Given the optimal segmentation markers and the estimated statistics, both components are estimated using linear filtering. A codebook-based approach differentiates between unvoiced speech and noise. A better extraction of the components is possible by taking into account the adaptive segmentation, compared to a fixed one. Also, a lower distortion for voiced speech and higher segSNR for both components is possible, as compared to other decomposition methods.
Study of charge density waves in suspended 2H-TaS2 and 2H-TaSe2 by nanomechanical resonance

The charge density wave (CDW) state in van der Waals systems shows interesting scaling phenomena as the number of layers can significantly affect the CDW transition temperature, TCDW. However, it is often difficult to use conventional methods to study the phase transition in these systems due to their small size and sensitivity to degradation. Degradation is an important parameter, which has been shown to greatly influence the superconductivity in layered systems. Since the CDW state competes with the onset of superconductivity, it is expected that TCDW will also be affected by the degradation. Here, we probe the CDW phase transition by the mechanical resonances of suspended 2H-TaS2 and 2H-TaSe2 membranes and study the effect of disorder on the CDW state. Pristine flakes show the transition near the reported values of 75 K and 122 K, respectively. We then study the effect of degradation on 2H-TaS2, which displays an enhancement of TCDW up to 129 K after degradation in ambient air. Finally, we study a sample with local degradation and observe that multiple phase transitions occur at 87 K, 103 K, and 118 K with a hysteresis in temperature in the same membrane. The observed spatial variations in the Raman spectra suggest that variations in crystal structure cause domains with different transition temperatures, which could result in the hysteresis. This work shows the potential of using nanomechanical resonance to characterize the CDW in suspended 2D materials and demonstrates that the degradation can have a large effect on transition temperatures.
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry

This paper presents an accurate, highly efficient and learning free method for large-scale radar odometry estimation. By using a simple filtering technique that keeps the strongest returns, we produce a clean radar data representation and reconstruct surface normals for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. Drift is additionally reduced by jointly registering the latest scan to a history of keyframes. We found that our odometry pipeline generalize well to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running merely on a single laptop CPU thread at 55 Hz.
Interpreting force response patterns of a mechanically driven crystallographic phase transition

Mechanically induced crystallographic phase transformation that reflects dynamic stress responses of intrinsically stochastic nature is a pertinent yet much less well-understood phenomenon. We focus on understanding the physical significance of stochasticity and how it can enable inference of principles underlying a crystallographic phase transformation. For interpreting the mechanical responses, a statistical approach of mapping the transformation dynamics to a probabilistic escape of crystallographic states defined on a free energy landscape is shown to reliably explain the patterns of response. We demonstrate that stochastic responses associated with a structural phase transformation can offer a reliable quantitative tool for unravelling the energy profile, intrinsic kinetics, and microscopic details of solid-to-solid crystallographic transitions.
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context

Bike sharing demand is increasing in large cities worldwide. The proper functioning of bike-sharing systems is, nevertheless, dependent on a balanced geographical distribution of bicycles throughout a day. In this context, understanding the spatiotemporal distribution of check-ins and check-outs is key for station balancing and bike relocation initiatives. Still, recent contributions from deep learning and distance-based predictors show limited success on forecasting bike sharing demand. This consistent observation is hypothesized to be driven by: i) the strong dependence between demand and the meteorological and situational context of stations; and ii) the absence of spatial awareness as most predictors are unable to model the effects of high-low station load on nearby stations. This work proposes a comprehensive set of new principles to incorporate both historical and prospective sources of spatial, meteorological, situational and calendrical context in predictive models of station demand. To this end, a new recurrent neural network layering composed by serial long-short term memory (LSTM) components is proposed with two major contributions: i) the feeding of multivariate time series masks produced from historical context data at the input layer, and ii) the time-dependent regularization of the forecasted time series using prospective context data. This work further assesses the impact of incorporating different sources of context, showing the relevance of the proposed principles for the community even though not all improvements from the context-aware predictors yield statistical significance.
PreSizE: Predicting Size in E-Commerce using Transformers

Recent advances in the e-commerce fashion industry have led to an exploration of novel ways to enhance buyer experience via improved personalization. Predicting a proper size for an item to recommend is an important personalization challenge, and is being studied in this work. Earlier works in this field either focused on modeling explicit buyer fitment feedback or modeling of only a single aspect of the problem (e.g., specific category, brand, etc.). More recent works proposed richer models, either content-based or sequence-based, better accounting for content-based aspects of the problem or better modeling the buyer's online journey. However, both these approaches fail in certain scenarios: either when encountering unseen items (sequence-based models) or when encountering new users (content-based models). To address the aforementioned gaps, we propose PreSizE -- a novel deep learning framework which utilizes Transformers for accurate size prediction. PreSizE models the effect of both content-based attributes, such as brand and category, and the buyer's purchase history on her size preferences. Using an extensive set of experiments on a large-scale e-commerce dataset, we demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. By encoding item attributes, PreSizE better handles cold-start cases with unseen items, and cases where buyers have little past purchase data. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system yielding very effective features and significantly improving recommendations.
Quantum Systems as Lie Algebroids

Lie algebroids provide a natural medium to discuss classical systems, however, quantum systems have not been considered. In aim of this paper is to attempt to rectify this situation. Lie algebroids are reviewed and their use in classical systems is described. The geometric structure of the Schrödinger and Heisenberg representations of quantum systems is examined and their relationship to Lie algebroids is explored. Geometrically, a quantum system is seen to be a collection of bounded, linear, self-adjoint operators on a Hilbert, or more precisely, a Kähler manifold. The geometry of the Schrödinger representation is given by the Poisson structure of the co-adjoint orbits on the dual of the Lie algebra. Finally, it is shown that the Schrödinger and Heisenberg representations are equivalent. 2010 Mathematics Subject Classification: 53C15, 53D05, 53Z05, 70H05 (Primary), 70G45, 81P05, 81Q70 (Secondary) PACS. 02.40. -k, 02.40. Yy
Isolation schemes for problems on decomposable graphs

The Isolation Lemma of Mulmuley, Vazirani and Vazirani [Combinatorica’87] provides a self-reduction scheme that allows one to assume that a given instance of a problem has a unique solution, provided a solution exists at all. Since its introduction, much effort has been dedicated towards derandomization of the Isolation Lemma for specific classes of problems. So far, the focus was mainly on problems solvable in polynomial time. In this paper, we study a setting that is more typical for NP -complete problems, and obtain partial derandomizations in the form of significantly decreasing the number of required random bits. In particular, motivated by the advances in parameterized algorithms, we focus on problems on decomposable graphs. For example, for the problem of detecting a Hamiltonian cycle, we build upon the rank-based approach from [Bodlaender et al., Inf. Comput. ’15] and design isolation schemes that use O graphs. In all these schemes, the weights are bounded exponentially in the number of random bits used. As a corollary, for every fixed H we obtain an algorithm for detecting a Hamiltonian cycle in an H -minor-free graph that runs in deterministic time 2 O ( √ n ) and uses polynomial space; this is the first algorithm to achieve such complexity guarantees. For problems of more local nature, such as finding an independent set of maximum size, we obtain isolation schemes on graphs of treedepth at most d that use O ( d ) random bits and assign polynomially-bounded weights. We also complement our findings with several unconditional and conditional lower bounds, which show that many of the results cannot be significantly improved.
Technical Report for Valence-Arousal Estimation on Affwild2 Dataset

In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve information about micro-motion and macro-motion for improving video emotion recognition and achieve Concordance Correlation Coefficient (CCC) of 0.415 and 0.511 for valence and arousal on the reselected validation set.
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity. However, three fundamental challenges in designing decentralized optimization algorithms are how to reduce their sample, communication, and memory complexities. In this paper, we propose a gradient-tracking-based stochastic recursive momentum (GT-STORM) algorithm for efficiently solving nonconvex optimization problems. We show that to reach an ϵ2-stationary solution, the total number of sample evaluations of our algorithm is Õ(m1/2ϵ-3) and the number of communication rounds is Õ(m1/2ϵ-3), which improve the O(ϵ-4) costs of sample evaluations and communications for the existing decentralized stochastic gradient algorithms. We conduct extensive experiments with a variety of learning models, including non-convex logistical regression and convolutional neural networks, to verify our theoretical findings. Collectively, our results contribute to the state of the art of theories and algorithms for decentralized network optimization.