Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of tool comparisons when estimating the likelihood of task success

People often use tools for tasks, and sometimes there is uncertainty about whether a given task can be completed with a given tool. This project explored whether, when, and how people’s optimism about successfully completing a task with a given tool is affected by the contextual salience of a better or worse tool. In six studies, participants were faced with novel tasks. For each task, they were assigned a tool but also exposed to a comparison tool that was better or worse in utility (or sometimes similar in utility). In some studies, the tool comparisons were essentially social comparisons, because the tool was assigned to another person. In other studies, the tool comparisons were merely counterfactual rather than social. The studies revealed contrast effects on optimism, and the effect worked in both directions. That is, worse comparison tools boosted optimism and better tools depressed optimism. The contrast effects were observed regardless of the general type of comparison (e.g., social, counterfactual). The comparisons also influenced discrete decisions about which task to attempt (for a prize), which is an important finding for ruling out superficial scaling explanations for the contrast effects. It appears that people fail to exclude irrelevant tool-comparison information from consideration when assessing their likelihood of success on a task, resulting in biased optimism and decisions.
Kinetic aspects of resistive wall modes

Introduction In recent studies of the interaction of low frequency resonant magnetic field perturbations it has been demonstrated that MHD theory has strong limitations in its applicability for modern Tokamak parameter range [1]. Namely, the radial scale of resonant layers in plasma is comparable to the ion Larmor radius. Therefore, it is interesting to compare MHD results for various instabilities connected with resonant magnetic flux surfaces (kink modes, resistive wall modes) with results using the kinetic approach. Code KiLCA (Kinetic Linear Cylindrical Approximation) is a wave code based on the kinetic description of the tokamak plasma in a periodic cylinder geometry [2,3]. The code has been successfully used to study kinetic effects of the interaction of resonant magnetic perturbations and the plasma in particular near resonant magnetic surfaces. The present report describes the modification of this code in such a way that also global modes and wave instabilities can be studied. The code is used to study kinetic effects including Landau damping and a proper account of particle collisions on the resistive wall mode. This is of particular interest if resonant magnetic surfaces are present in the plasma. MHD modelling In the present studies, results from ideal MHD modelling are compared to results from kinetic modelling. An ideal MHD equilibrium
Polycyclic Aromatic Hydrocarbon in Vegetables Grown on Contaminated Soils in a Sub-Saharan Tropical Environment – Lagos, Nigeria

Abstract Diet has been established as a major source of exposure to Polycyclic aromatic hydrocarbon for humans (PAHs) especially nonsmokers. Therefore their presence in plants constitute a major health concern. This research aims at determining the concentration and profile of sixteen US-EPA priority PAHs in tropical edible vegetables (Corchorus olitorius (Ewedu), Celosia argentea (Soko), Amaranthus cruentus L (Grain amaranthus/Tete), Telfairia occidentale (Ugwu), Basella alba (Amunu tutu/White Spinash), Lactuca Sativa (Lettuce), Allium ascalonicum (Spring Onions/Alubasa elewe), Talinum Triangulare (Water leaf)), grown on potted contaminated soils. Vegetable grown on potted contaminated soils and contaminated soils were extracted using n-hexane/acetone mixture in ultrasonic bath. Extracts were purified using C18 solid-phase extraction cartridge (5 mL). Resultant extracts were quantified for PAHs on an Agilent 6890 Gas chromatography/mass spectrometer (GC/MS). Total PAH concentration in contaminated soils and plants grown on them were 200 - 250,000 ng g−1 and 100-5,000 ng g−1, respectively. Two and three ringed PAHs which are the less toxic were the dominant PAH type present in most of the plant parts. Telfairia occidentale (Ugwu) consistently grew on all the soils samples which suggest it possess some potential for phyto remediation. Lactuca sativa (Lettuce) had few growth only the least contaminated soils. Its ability to grow on soils with low contamination makes it a potential bio indicators for polluted soils.
Anti-Interleukin Biologics for the treatment of the Atopic March Diseases

The atopic march refers to the natural history of allergic disorders as they develop from infancy to childhood. Classically, the atopic march begins with atopic dermatitis (AD), followed by food allergy, advancing to asthma, allergic rhinitis (AR), and finally to the fifth member eosinophilic esophagitis. The pathogenesis of the atopic march is complex, and involves genetic, immunological, and environmental factors. T helper type 2 (Th2) lymphocytes, and epithelial cells play a key role in the pathogenesis of the diseases in the atopic march. Th2 cells secrete cytokines, such as interleukin5 (IL-5), IL-4, and IL-13, whereas, epithelial cell injury release alarmin cytokines, including IL-25, IL-33, and thymic stromal lymphopoietin (TSLP). Th2 cells and alarmin cytokines play an important role in the development of eczematous skin lesions, airway inflammation and remodeling, and oesophageal mucosal inflammation. Treatment of eosinophilic asthma and associated comorbid disorders is challenging, and requires a precision targeted approach with biologics. Dupilumab is a fully humanized IgG4 monoclonal antibody to the IL-4Rα, which mediates signaling to both IL-4 and IL-13, and blocks their immunopathological effects. Dupilumab is the only biologic that has been approved for the treatment of eosinophilic asthma, AD, and eosinophilic esophagitis. In patients with eosinophilic asthma treatment with dupilumab has been shown to improve asthma control, reduce exacerbations, and improve lung function. In patients with atopic dermatitis dupilumab has been demonstrated to improve the Eczema Area Severity Index (EASI) score, Investigator’s Global Assessment (IGA) response, SCORing Atopic Dermatitis (SCORAD) score, and the Peak Pruritus Numerical Rating (PNR) scale. Lebrikizumab and Tralokinumab (anti-IL-13) failed to show the expected results for the treatment of asthma, astoundingly, in several clinical trials they have been shown to significantly improvement EASI score, IGA response, SCORAD score, PNR scale, sleep architecture, and Dermatology Life Quality Index (DLQI). They have been granted First Tract Designation, and European Commission regulatory approval, respectively. Tezepelumab (antiTSLP) is approved for the treatment of eosinophilic asthma, and has been shown to significantly reduce exacerbations, and improve asthma control, lung function, and HLQoL. However, tezepelumab did not meet the endpoints in phase II for the treatment of AD.
The Fluorescent Probe Method in Investigation of the State of Erythrocyte Membranes in White Rats at Exposure to Chemical Environmental Factors

The purpose of the presented work was to study the possibility of using the fluorescent probes method to diagnose the harmful effects of chemical factors on the example of polyethylene glycol on the organism of white rats by evaluating the state of erythrocyte membranes. Material and methods. We used the following fluorescent probes in the studies: ortho-hydroxy derivatives of 2.5-diaryl-1.3-oxazole. Results and discussion. In the case of erythrocytes of rats, which are toxic to PEG-400, there is a marked decrease in the fluorescence intensity of all the probes used. The discussed decrease in the fluorescence intensity of the probes indicates a decrease in the number of molecules of each of the probes associated with erythrocyte membranes per one hour of incubation, which indicates a decrease in the rate of binding of probes to membranes. Such a decrease in the binding rate can be explained by the formation of an additional protective membrane around each lipid membrane. Conclusion. The established fact of formation of an additional coat of polyethylene glycol molecules on the surface of erythrocyte membranes can be treated as a stable standardized indicator for the method of fluorescent probes, which may indicate the absence of damaging effect on the membranes at the object of study, which requires confirmation in further studies.
Kohonen ' s algorithm for the parametr & ation of manifolds numerical

T. Kohonen has described an algorithm for fitting a k-dimensional grid of points to a set of points taken from a k-manifold in ~n, for k<~n. The algorithm is inspired by a neural model and bears some of the marks of its ancestry. In this paper we show that if the process converges, it converges to a locally 1-1 mapping of the grid onto the manifold. Hitherto this result has only been proved for the case where k= 1. The analytic process of parametrising manifolds is well known to Mathematicians and Physicists, [1] and is becoming increasingly familiar to Engineers concerned with, for example, robot manip-ulator trajectories [2]. The numerical equivalent does not appear to have been much studied, except in special cases. Suppose we have a finite set of points in En and we wish to fit a manifold to them. There are two special cases of note, the first is the case of fitting a 1-manifold, a curve, to a set of points in R 2. We may be concerned with choosing a curve that passes through all the points, as is commonly done with low order splines, or we may suppose there is some random variation and we wish to satisfy some goodness of fit criterion such as least squares, together with some simplicity criterion such as a bound on the degree of a polynomial. The second special case is where we have a set of points in R n and we wish to find the hyperplane of dimension k which contains most of the variation of the data. The well-known Karhunen-Lo~ve expansion accomplishes this last [3], and there are algorithms for the one-dimensional curve-fitting case, and also for the two-dimensional case. In all cases, the manifold is constrained globally by all the data points. The problem of finding the 'best fit manifold', and assessing the goodness of fit, is then a generali-sation (to the non-linear and to higher dimensions) of a standard problem. It is reasonable to want to (a) smooth data, (b) interpolate data and (c) reduce the effective dimension of data, without the constraints on dimension on the one hand or linearity on the other. It is necessary to tackle this problem locally, unlike the case of least squares best fitting of graphs of functions, since the global topology is unknown. We cannot therefore expect to emerge with one function, but must finish with …
Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe Dataset

Sound event detection (SED) in machine listening entails identifying the different sounds in an audio file and identifying the start and end time of a particular sound event in the audio. SED finds use in various applications such as audio surveillance, speech recognition, and context-based indexing and retrieval of data in a multimedia database. However, in real-life scenarios, the audios from various sources are seldom devoid of any interfering noise or disturbance. In this paper, we test the performance of the You Only Hear Once (YOHO) algorithm on noisy audio data. Inspired by the You Only Look Once (YOLO) algorithm in computer vision, the YOHO algorithm can match the performance of the various state-of-the-art algorithms on datasets such as Music Speech Detection Dataset, TUT Sound Event, and Urban-SED datasets but at lower inference times. In this paper, we explore the performance of the YOHO algorithm on the VOICe dataset containing audio files with noise at different sound-to-noise ratios (SNR). YOHO could outperform or at least match the best performing SED algorithms reported in the VOICe dataset paper and make inferences in less time.
Satellite mass functions and the faint end of the galaxy mass-halo mass relation in LCDM

The abundance of the faintest galaxies provides insight into the nature of dark matter and the process of dwarf galaxy formation. In the LCDM scenario, low mass haloes are so numerous that the efficiency of dwarf formation must decline sharply with decreasing halo mass in order to accommodate the relative scarcity of observed dwarfs and satellites in the Local Group. The nature of this decline contains important clues to the mechanisms regulating the onset of galaxy formation in the faintest systems. We explore here two possible models for the stellar mass (M*)-halo mass (M200) relation at the faint end, motivated by some of the latest LCDM cosmological hydrodynamical simulations. One model includes a sharp mass threshold below which no luminous galaxies form, as expected if galaxy formation proceeds only in systems above the Hydrogen-cooling limit. In the second model, M* scales as a steep power-law of M200 with no explicit cutoff, as suggested by recent semianalytic work. Although both models predict satellite numbers around Milky Way-like galaxies consistent with current observations, they predict vastly different numbers of ultra-faint dwarfs and of satellites around isolated dwarf galaxies. Our results illustrate how the satellite mass function around dwarfs may be used to probe the M*-M200 relation at the faint end and to elucidate the mechanisms that determine which low-mass haloes ‘light up’ or remain dark in the LCDM scenario.
Cohomology of Lie algebroid over algebraic spaces

We consider Lie algebroid over an algebraic space as a quasicoherent sheaf of Lie-Rinehart algebras. We compute algebraic (analytic) de Rham cohomologies for some free divisors and the associated logarithmic de Rham cohomologies as well. We express hypercohomology for a locally free Lie alge- broid (of ﬁnite or inﬁnte rank) as a derived functor and simplify it via ˇCech cohomology. Furthermore, we deﬁne the Hochschild hypercohomology of a sheaf of generalized bialgebras and study the special cases, namely Hochschild hypercohomology of universal enveloping algebroid and jet algebroid of a Lie algebroid. We present a version of Hochschild-Kostant-Rosenberg (HKR) the- orem for a locally free Lie algebroid as well as its dual version.
Truly Low-Space Element Distinctness and Subset Sum via Pseudorandom Hash Functions

We consider low-space algorithms for the classic Element Distinctness problem: given an array of $n$ input integers with $O(\log n)$ bit-length, decide whether or not all elements are pairwise distinct. Beame, Clifford, and Machmouchi [FOCS 2013] gave an $\tilde O(n^{1.5})$-time randomized algorithm for Element Distinctness using only $O(\log n)$ bits of working space. However, their algorithm assumes a random oracle (in particular, read-only random access to polynomially many random bits), and it was asked as an open question whether this assumption can be removed. In this paper, we positively answer this question by giving an $\tilde O(n^{1.5})$-time randomized algorithm using $O(\log ^3 n\log \log n)$ bits of space, with one-way access to random bits. As a corollary, we also obtain a $\operatorname{\mathrm{poly}}(n)$-space $O^*(2^{0.86n})$-time randomized algorithm for the Subset Sum problem, removing the random oracles required in the algorithm of Bansal, Garg, Nederlof, and Vyas [STOC 2017]. The main technique underlying our results is a pseudorandom hash family based on iterative restrictions, which can fool the cycle-finding procedure in the algorithms of Beame et al. and Bansal et al.
Implicit model specialization through dag-based decentralized federated learning

Federated learning allows a group of distributed clients to train a common machine learning model on private data. The exchange of model updates is managed either by a central entity or in a decentralized way, e.g. by a blockchain. However, the strong generalization across all clients makes these approaches unsuited for non-independent and identically distributed (non-IID) data. We propose a unified approach to decentralization and personalization in federated learning that is based on a directed acyclic graph (DAG) of model updates. Instead of training a single global model, clients specialize on their local data while using the model updates from other clients dependent on the similarity of their respective data. This specialization implicitly emerges from the DAG-based communication and selection of model updates. Thus, we enable the evolution of specialized models, which focus on a subset of the data and therefore cover non-IID data better than federated learning in a centralized or blockchain-based setup. To the best of our knowledge, the proposed solution is the first to unite personalization and poisoning robustness in fully decentralized federated learning. Our evaluation shows that the specialization of models emerges directly from the DAG-based communication of model updates on three different datasets. Furthermore, we show stable model accuracy and less variance across clients when compared to federated averaging.
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification

We study the problem of the identification of m arms with largest means under a fixed error rate $\delta$ (fixed-confidence Top-m identification), for misspecified linear bandit models. This problem is motivated by practical applications, especially in medicine and recommendation systems, where linear models are popular due to their simplicity and the existence of efficient algorithms, but in which data inevitably deviates from linearity. In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem. We show that knowing the scale of the deviation from linearity is necessary to exploit the structure of the problem. We then describe the first algorithm for this setting, which is both practical and adapts to the amount of misspecification. We derive an upper bound to its sample complexity which confirms this adaptivity and that matches the lower bound when $\delta$ $\rightarrow$ 0. Finally, we evaluate our algorithm on both synthetic and real-world data, showing competitive performance with respect to existing baselines.
Boundary value problems on non-Lipschitz uniform domains: stability, compactness and the existence of optimal shapes

We study boundary value problems for bounded uniform domains in R n , n ⩾ 2, with non-Lipschitz, and possibly fractal, boundaries. We prove Poincaré inequalities with uniform constants and trace terms for ( ε , ∞ )-domains contained in a fixed bounded Lipschitz domain. We introduce generalized Dirichlet, Neumann, and Robin problems for Poisson-type equations and prove the Mosco convergence of the associated energy functionals along sequences of suitably converging domains. This implies a stability result for weak solutions, the norm convergence of the associated resolvents, and the convergence of the corresponding eigenvalues and eigenfunctions. We provide compactness results for parametrized classes of admissible domains, energy functionals, and weak solutions. Using these results, we can then prove the existence of optimal shapes in these classes in the sense that they minimize the initially given energy functionals. For the Robin boundary problems, this result is new.
LogAvgExp Provides a Principled and Performant Global Pooling Operator

We seek to improve the pooling operation in neural networks, by applying a more theoretically justified operator. We demonstrate that LogSumExp provides a natural OR operator for logits. When one corrects for the number of elements inside the pooling operator, this becomes $\text{LogAvgExp} := \log(\text{mean}(\exp(x)))$. By introducing a single temperature parameter, LogAvgExp smoothly transitions from the max of its operands to the mean (found at the limiting cases $t \to 0^+$ and $t \to +\infty$). We experimentally tested LogAvgExp, both with and without a learnable temperature parameter, in a variety of deep neural network architectures for computer vision.
A gravitational and dynamical model of star formation in Orion

The Orion Nebula Cluster (ONC) is the most massive region of active star formation within a kpc of the Sun. Using Gaia EDR3 parallaxes and proper motions, we examine the bulk motions of stars radially and tangentially relative to the cluster center. We find an age gradient with distance to the stars in the ONC, from 385 pc for the oldest stars, to 395 pc for the younger stars, indicating that the star forming front is propagating into the cloud. We find an organized signature of rotation of the central cluster, but it is present only in stars younger than 2 Myr. We also observe a net infall of young stars into the center of the ONC’s deep gravitational potential well. The infalling sources lie preferentially along the filament, on the other hand, outflowing sources are distributed spherically around the cluster, and they have larger velocity dispersion. We further propose a solution to a longstanding question of why the ONC shows a weak signature of expansion even though the cluster is likely bound: much of this expansion is likely driven by unstable N-body interactions among stars, resulting in low-velocity ejections. Finally we observe a significant infall of stars in various low-mass star-forming regions towards the Orion Complex at distances as far away as 200 pc, presumably due to a strong gravitational potential of Orion. Though analyzing signatures imprinted on stellar dynamics across different spatial scales, these observation shed new light on the signatures of formation and evolution of young clusters.
Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion

Whispered speech is a special way of pronunciation without using vocal cord vibration. A whispered speech does not contain a fundamental frequency, and its energy is about 20dB lower than that of a normal speech. Converting a whispered speech into a normal speech can improve speech quality and intelligibility. In this paper, a novel attention-guided generative adversarial network model incorporating an autoencoder, a Siamese neural network, and an identity mapping loss function for whisper to normal speech conversion (AGAN-W2SC) is proposed. The proposed method avoids the challenge of estimating the fundamental frequency of the normal voiced speech converted from a whispered speech. Specifically, the proposed model is more amendable to practical applications because it does not need to align speech features for training. Experimental results demonstrate that the proposed AGAN-W2SC can obtain improved speech quality and intelligibility compared with dynamic-time-warping-based methods.