Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Tracking and Model Following Controller Based on Higher Order Sliding Mode Control and Observation: With an Application to MagLev System

This paper deals with the design of robust tracking and model following (RTMF) controller for linear time-invariant (LTI) systems with uncertainties. The controller is based on the second order sliding mode (SOSM) algorithm (super twisting) which is the most effective and popular in the family of higher order sliding modes (HOSM). The use of super twisting algorithm (STA) eliminates the chattering problem encountered in traditional sliding mode control while retaining its robustness properties. The proposed robust tracking controller can guarantee the asymptotic stability of tracking error in the presence of time varying uncertain parameter and exogenous disturbances. Finally, this strategy is implemented on a magnetic levitation system (MagLev) which is inherently unstable and nonlinear. While implementing this proposed RTMF controller for MagLev system, a super twisting observer (STO) is used to estimate the unknown state i.e the velocity of the ball which is not directly available for measurement. It has been observed that the RTMF controller based on STA-STO pair, is not good enough to achieve SOSM for a chosen sliding surface using continuous control. As a remedy, continuous RTMF controller based on STA is implemented with a higher order sliding mode observer (HOSMO). The simulated as well as the experimental results are provided to illustrate the effectiveness of the proposed controller-observers pair.
Data-driven geophysics: from dictionary learning to deep learning

Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of dimensionality and may inaccurately model the subsurface. "Data-driven" techniques may overcome these issues with increasingly available geophysical data. In this article, we review the basic concepts of and recent advances in data-driven approaches from dictionary learning to deep learning in a variety of geophysical scenarios. Explorational geophysics including data processing, inversion and interpretation will be mainly focused. Artificial intelligence applications on geoscience involving deep Earth, earthquake, water resource, atmospheric science, satellite remoe sensing and space sciences are also reviewed. We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. Some promising directions are provided for future research involving deep learning in geophysics, such as unsupervised learning, transfer learning, multimodal deep learning, federated learning, uncertainty estimation, and activate learning.
Is there a Golden Parachute in Sannikov's principal-agent problem?

This paper provides a complete review of the continuous-time optimal contracting problem introduced by Sannikov, in the extended context allowing for possibly different discount rates of both parties. The agent's problem is to seek for optimal effort, given the compensation scheme proposed by the principal over a random horizon. Then, given the optimal agent's response, the principal determines the best compensation scheme in terms of running payment, retirement, and lump-sum payment at retirement. A Golden Parachute is a situation where the agent ceases any effort at some positive stopping time, and receives a payment afterwards, possibly under the form of a lump-sum payment, or of a continuous stream of payments. We show that a Golden Parachute only exists in certain specific circumstances. This is in contrast with the results claimed by Sannikov, where the only requirement is a positive agent's marginal cost of effort at zero. Namely, we show that there is no Golden Parachute if this parameter is too large. Similarly, in the context of a concave marginal utility, there is no Golden Parachute if the agent's utility function has a too negative curvature at zero. In the general case, we provide a rigorous analysis of this problem, and we prove that an agent with positive reservation utility is either never retired by the principal, or retired above some given threshold (as in Sannikov's solution). In particular, different discount factors induce naturally a face-lifted utility function, which allows to reduce the whole analysis to a setting similar to the equal-discount rates one. Finally, we also confirm that an agent with small reservation utility does have an informational rent, meaning that the principal optimally offers him a contract with strictly higher utility value.
Random acceleration process under stochastic resetting

We consider the motion of a randomly accelerated particle in one dimension under stochastic resetting mechanism. Denoting the position and velocity by x and v respectively, we consider two different resetting protocols—(i) complete resetting: here both x and v reset to their initial values x 0 and v 0 at a constant rate r, (ii) partial resetting: here only x resets to x 0 while v evolves without interruption. For complete resetting, we find that the particle attains stationary state in both x and v. We compute the non-equilibrium joint stationary state of x and v from which we obtain the stationary state distribution for position by integrating over v. We also study the late time relaxation of the position distribution function. On the other hand, for partial resetting, the joint distribution is always in the transient state. At large t, the position distribution possesses a scaling behaviour ( x / t ) which we rigorously derive. Next, we study the first passage time properties with an absorbing wall at the origin. For complete resetting, we find that the mean first passage time (MFPT) is rendered finite by the resetting mechanism. We explicitly derive the expressions for the MFPT and the survival probability at large t. However, in stark contrast, for partial resetting, we find that resetting does not render finite MFPT. This is because even though x is brought to x 0, the large fluctuation in v (typically of the order ∼ t ) can take the particle substantially far from the origin. All our analytic results are corroborated by the numerical simulations.
The Hurewicz theorem in homotopy type theory

We prove the Hurewicz theorem in homotopy type theory, i.e., that for $X$ a pointed, $(n-1)$-connected type $(n \geq 1)$ and $A$ an abelian group, there is a natural isomorphism $\pi_n(X)^{ab} \otimes A \cong \tilde{H}_n(X; A)$ relating the abelianization of the homotopy groups with the homology. We also compute the connectivity of a smash product of types and express the lowest non-trivial homotopy group as a tensor product. Along the way, we study magmas, loop spaces, connected covers and prespectra, and we use $1$-coherent categories to express naturality and for the Yoneda lemma. As homotopy type theory has models in all $\infty$-toposes, our results can be viewed as extending known results about spaces to all other $\infty$-toposes.
PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing

Facial attribute editing aims to manipulate attributes on the human face, e.g., adding a mustache or changing the hair color. Existing approaches suffer from a serious compromise between correct attribute generation and preservation of the other information such as identity and background, because they edit the attributes in the imprecise area. To resolve this dilemma, we propose a progressive attention GAN (PA-GAN) for facial attribute editing. In our approach, the editing is progressively conducted from high to low feature level while being constrained inside a proper attribute area by an attention mask at each level. This manner prevents undesired modifications to the irrelevant regions from the beginning, and then the network can focus more on correctly generating the attributes within a proper boundary at each level. As a result, our approach achieves correct attribute editing with irrelevant details much better preserved compared with the state-of-the-arts. Codes are released at this https URL.
Climate & BCG: Effects on COVID-19 DeathGrowth Rates

Multiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors using the death growth rates of 40 regions worldwide with both machine learning and Bayesian methods. We find weak, non-significant (< 3 sigma) evidence for temperature and relative humidity as factors in the spread of COVID-19 but little or no evidence for BCG vaccination prevalence or PM2.5 pollution. The only variable detected at a statistically significant level (> 3 sigma) is the rate of positive COVID-19 tests, with higher positive rates correlating with higher daily growth of deaths.
On sex-specific foraging behaviour in the Willow Warbler, Phylloscopus trochilus

The foraging behaviour of Willow Warblers (Phylloscopus trochilus) in relation to the availability of their insect prey was studied at the Tovetorp Field Station and at the Royal Park of Djurgarden, Stockholm, Sweden, from 1985 through 1989. The numbers and types of arthropods available to Willow Warblers fluctuated throughout the breeding season. In general, most arthropods were found on the underside of the leaves. Males spent more time gleaning and less time hovering and fly catching when preying chiefly on aphid nymphs then when foraging on other arthropods on birch before budbreak. They performed more fly-catching behaviour and tended to forage in taller trees than did females. Dissimilarities in foraging behaviour between the sexes were attributed to differences in wing length and wing shape. Because females are smaller and have less pointed wings they are better adapted for hovering than males. Consequently, females were better at capturing prey on the underside of birch and willow leaves, which wa.
MOLECULAR GENETIC ANALYSIS OF THE V~Ser GROUP ASSOCIATED WITH TWO MOUSE LIGHT CHAIN GENETIC MARKERS

The extensive amino acid sequence diversity of Ig variable (V) j regions is known to be derived from both evolutionary and somatic processes (I-3). Comparison of amino acid sequences of mouse myeloma and hybridoma K light (L) chains, for example, indicates that these sequences can be arranged into groups such that members of a group are more similar to each other than to members of other groups (4-6). In the case of mouse ~ L chains, multiple V region genes have been observed (7, 8) for most groups of V regions tested. Thus, the multiple germ line V~ genes provide a portion of the V region diversity observed at the protein level. Additional diversity is introduced by permitting each V~ gene to join with any one of four different active joining (J) segments, by introducing junctional diversity during the joining event, and by somatic mutation (for review, see refs. 3 and 9). Several mouse V~ genetic markers have been described which involve characteristic V~ regions that are expressed by some inbred strains of mice, but not by others. These genetic markers have been detected by peptide mapping (10, 11) and isoelectric focusing (12, 13) of L chains from normal mouse serum Ig, by analysis of expression of various idiotypes (14, 15), and by isoelectric focusing of L chains of antiphosphorylcholine antibodies that bear the H8 idiotype (16). All of these genetic polymorphisms are determined by genes closely linked to the Lyt-2 and Lyt-3 loci on chromosome 6 (17, 18), the same chromosome that bears the K L chain structural genes (19). A system for nomenclature of genes that determine these V~ polymorphisms has been proposed (20). One of these V~ polymorphisms, called the I,-peptide marker, was detected by tryptic peptide mapping of reduced and [14C]iodoacetic acid-alkylated L chains from normal serum Ig of unimmunized mice (10, 11). This analysis detected a heterogeneous group of VK regions that accounts for <5% of the total L chains expressed by IB-positive strains. To obtain myelomas producing homogeneous This work was supported in part by grant CA-30147 from the National Cancer Institute, grant CD218 from the American Cancer Society, and grant F-884 from the Robert A. Welch Foundation, awarded to P. Gottlieb. i Abbreviations used in this paper: C, constant region of Ig; cDNA, complementary DNA; CDR, complementarity-determining region; Fr, framework; J, joining region of Ig; L, light chain of Ig; NP, (4-hydroxyl-3-nitrophenyl)acetyl; 5'UT, 5' untranslated region; V, variable region of Ig.
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning

General-purpose trajectory planning algorithms for automated driving utilize complex reward functions to perform a combined optimization of strategic, behavioral, and kinematic features. The specification and tuning of a single reward function is a tedious task and does not generalize over a large set of traffic situations. Deep learning approaches based on path integral inverse reinforcement learning have been successfully applied to predict local situation-dependent reward functions using features of a set of sampled driving policies. Sample-based trajectory planning algorithms are able to approximate a spatio-temporal subspace of feasible driving policies that can be used to encode the context of a situation. However, the interaction with dynamic objects requires an extended planning horizon, which depends on sequential context modeling. In this work, we are concerned with the sequential reward prediction over an extended time horizon. We present a neural network architecture that uses a policy attention mechanism to generate a low-dimensional context vector by concentrating on trajectories with a human-like driving style. Apart from this, we propose a temporal attention mechanism to identify context switches and allow for stable adaptation of rewards. We evaluate our results on complex simulated driving situations, including other moving vehicles. Our evaluation shows that our policy attention mechanism learns to focus on collision-free policies in the configuration space. Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.
An Adversarial Approach to Structural Estimation

We propose a new simulation‐based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence.
Feature Selection on Noisy Twitter Short Text Messages for Language Identification

The task of written language identification involves typically the detection of the languages present in a sample of text. Moreover, a sequence of text may not belong to a single inherent language but also may be mixture of text written in multiple languages. This kind of text is generated in large volumes from social media platforms due to its flexible and user friendly environment. Such text contains very large number of features which are essential for development of statistical, probabilistic as well as other kinds of language models. The large number of features have rich as well as irrelevant and redundant features which have diverse effect over the performance of the learning model. Therefore, feature selection methods are significant in choosing feature that are most relevant for an efficient model. In this article, we basically consider the Hindi-English language identification task as Hindi and English are often two most widely spoken languages of India. We apply different feature selection algorithms across various learning algorithms in order to analyze the effect of the algorithm as well as the number of features on the performance of the task. The methodology focuses on the word level language identification using a novel dataset of 6903 tweets extracted from Twitter. Various n-gram profiles are examined with different feature selection algorithms over many classifiers. Finally, an exhaustive comparative analysis is put forward with respect to the overall experiments conducted for the task.
Joint Access Point Selection and Interference Cancellation for Cell-Free Massive MIMO

Cell-free Massive MIMO is a highly promising approach to enhance network capacity by moving a large number of distributed access points (AP) closer to mobile users while utilizing simple matched filtering and conjugate beamforming. Recent work using minimum mean-squared-error (MMSE) receiver that suppress multi-user interference (MUI) shows significant capacity increase, but at the cost of high computational complexity and residual MUI enhancement. We propose a significantly lower complexity adaptive approach where central processing unit (CPU) removes MUI iteratively without enhancing residual term. It does so dynamically by using available knowledge of channel estimates to perform joint process of combining selected strongest AP signals for each user and subtracting the sum of interference estimates from all other users at the same time. We provide signal-to-interference plus noise-ratio (SINR) and complexity analyses backed by numerical results to show the superiority of this approach compared with the state-of-the-art techniques.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.
Acceleration noise constraints on gravity-induced entanglement

It has been proposed that quantum features of the gravitational field can be exposed experimentally by employing gravity as a mediator of entanglement. We show that in order to witness this type of entanglement experimentally, strong limits on acceleration noise, which has been neglected in previous work, must be overcome. In the case of two particles of similar mass, Casimir-Polder forces lead to a fundamental limit of tenths of femtometers per second squared per root Hertz. Limits are between three and six orders of magnitude less strict for two particles of unequal mass, depending on collisional decoherence.
Motivic decompositions of moduli spaces of vector bundles on curves

Let $r \geq 2, d$ be two integers which are coprime to each other. Let $C$ be a smooth projective curve of genus $g \geq 2$ and $M(r,L)$ be the moduli space of rank $r$ stable vector bundles on $C$ whose determinants are isomorphic to a fixed line bundle $L$ of degree $d$ on $C. $ In this paper, we study motivic decomposition of $M(r,L)$ for $r=2, 3$ cases. We give a new proof of a version of the main result of arXiv:1806.11101. We also found a new motivic decomposition of $M(3,L). $
Antioxidant activities of grape skin and grape seed polyphenolics and potential use of antioxidants in foods as a functional food ingredient

ion can oxidiz AN An radical spe 2 H O2 [O] 15 her Radicals chemical processes in live plant cells and processes used to convert plant nto foods can cause the formation of numerous free radicals. Radicals can be m the oxidation of tyrosine and tryptophan, which in turn can oxidize s and ascorbic acid in foods. Sulfur-containing aminoacids and peptides can ate free radicals by the addition of an electron to cystine or by proton from cysteine. Oxidation of sulfite or bisulfite can form SO3radicals, which e ascorbate and tocopherol (Huie and Neta, 1984). TIOXIDANTS tioxidants are substances that can either directly or indirectly scavenge free cies. They can be natural or synthetic. Antioxidants can be divided into two
Industrial Experience of Finding Cryptographic Vulnerabilities in Large-scale Codebases

Enterprise environment often screens large-scale (millions of lines of code) codebases with static analysis tools to find bugs and vulnerabilities. Parfait is a static code analysis tool used in Oracle to find security vulnerabilities in industrial codebases. Recently, many studies show that there are complicated cryptographic vulnerabilities caused by misusing cryptographic APIs in JavaTM1 In this paper, we describe how we realize a precise and scalable detection of these complicated cryptographic vulnerabilities based on Parfait framework. The key challenge in the detection of cryptographic vulnerabilities is the high false alarm rate caused by pseudo-influences. Pseudo-influences happen if security-irrelevant constants are used in constructing security-critical values. Static analysis is usually unable to distinguish them from hard-coded constants that expose sensitive information. We tackle this problem by specializing the backward dataflow analysis used in Parfait with refinement insights, an idea from the tool CryptoGuard [20]. We evaluate our analyzer on a comprehensive Java cryptographic vulnerability benchmark and eleven large real-world applications. The results show that the Parfait-based cryptographic vulnerability detector can find real-world cryptographic vulnerabilities in large-scale codebases with high true-positive rates and low runtime cost.
Free‐water DTI estimates from single b‐value data might seem plausible but must be interpreted with care

Free‐water elimination DTI (FWE‐DTI) has been used widely to distinguish increases of free‐water partial‐volume effects from tissue’s diffusion in healthy aging and degenerative diseases. Because the FWE‐DTI fitting is only well‐posed for multishell acquisitions, a regularized gradient descent (RGD) method was proposed to enable application to single‐shell data, more common in the clinic. However, the validity of the RGD method has been poorly assessed. This study aims to quantify the specificity of FWE‐DTI procedures on single‐shell and multishell data.