Miracle-dz/UniFAR-FTUs huggingface dataset:
Flat Band Potential of As-Grown n-MoSe2 Single Crystal Electrodes

Recently, photoelectrochemical solar cells (PEC's) are projected as alternatives to p–n junction solar cells for solar energy conversion. The behaviour of any semiconducting material as an electrode in the PEC solar cell for such a device is governed by a large number of parameters of which the flat band potential is an important one. Investigations are made about this parameter for as-grown crystals of n-MoSe2, grown by direct vapour transport technique. It is observed that these as-grown crystals exhibit a flat band potential of around 200 mV in a PEC cell using 0.025 M I2, 1 M KI, 2 M Na2SO4, and 2 cm3 concentrated H2SO4 electrolyte. The values obtained by various methods are found to be in close agreement. Vor kurzem sind photoelektrochemische Solarzellen (PEC) als Alternative zu p–n-Ubergangs Solarzellen fur die Umwandlung der Sonnenenergie vorgeschlagen worden. Das Verhalten eines Halbleitermaterials als Elektrode in PEC-Solarzellen wird fur solche Bauelemente von einer grosen Zahl von Parametern bestimmt, von denen das Flachbandpotential ein wesentlicher ist. Es wird uber Untersuchungen dieses Parameters in n-MoSe2-Kristallen unmittelbar nach der durch Dampfphasentransport erfolgten Zuchtung berichtet. Es wird beobachtet, das diese unbehandelten Kristalle ein Flachbandpotential von etwa 200 mV in einer PEC-Zelle bei Benutzung eines Elektrolyten von 0,025 M J2, 1 M KJ, 2 M Na2SO4 und 2 cm3 konzentrierter H2SO4 aufweisen. Die mit verschiedenen Methoden ermittelten Werte stimmen gut miteinander uberein.
Adapting Neural Architectures Between Domains

Neural architecture search (NAS) has demonstrated impressive performance in automatically designing high-performance neural networks. The power of deep neural networks is to be unleashed for analyzing a large volume of data (e.g. ImageNet), but the architecture search is often executed on another smaller dataset (e.g. CIFAR-10) to ﬁnish it in a feasible time. However, it is hard to guarantee that the optimal architecture derived on the proxy task could maintain its advantages on another more challenging dataset. This paper aims to improve the generalization of neural architectures via domain adaptation. We analyze the generalization bounds of the derived architecture and suggest its close relations with the validation error and the data distribution distance on both domains. These theoretical analyses lead to AdaptNAS, a novel and principled approach to adapt neural architectures between domains in NAS. Our experimental evaluation shows that only a small part of ImageNet will be sufﬁcient for AdaptNAS to extend its architecture success to the entire ImageNet and outperform state-of-the-art comparison algorithms.
A psycholexical Study of Virtues in the Dutch Language, and Relations between Virtues and Personality

Following the psycholexical approach, a list of 153 virtue descriptors was selected from a previously constructed list of trait–terms, under the assumption that virtues form a subset of traits. The virtue list was administered to 400 participants (self– and other–raters), who had to indicate the extent to which each term applied to them or to the others. Principal Component Analyses were performed yielding six factors of virtues. In addition, Big Five factors and markers of an external set of virtues were constructed. Correlation and regression analyses were performed to describe the relations between virtues, the Dutch Big Five system and other virtue systems. Compared to the other virtue systems, the present study revealed some additional domains. The overlap found with personality measures corresponds to earlier findings supporting the assumption that virtues are important traits. Copyright © 2010 John Wiley & Sons, Ltd.
Compact task representations as a normative model for higher-order brain activity

Higher-order brain areas such as the frontal cortices are considered essential for the ﬂexible solution of tasks. However, the precise computational role of these areas is still debated. Indeed, even for the simplest of tasks, we cannot really explain how the measured brain activity, which evolves over time in complicated ways, relates to the task structure. Here, we follow a normative approach, based on integrating the principle of efﬁcient coding with the framework of Markov decision processes (MDP). More speciﬁcally, we focus on MDPs whose state is based on action-observation histories, and we show how to compress the state space such that unnecessary redundancy is eliminated, while task-relevant information is preserved. We show that the efﬁciency of a state space representation depends on the (long-term) behavioural goal of the agent, and we distinguish between model-based and habitual agents. We apply our approach to simple tasks that require short-term memory, and we show that the efﬁcient state space representations reproduce the key dynamical features of recorded neural activity in frontal areas (such as ramping, sequentiality, persistence). If we additionally assume that neural systems are subject to cost-accuracy tradeoffs, we ﬁnd a surprising match to neural data on a population level.
Coated Wire Lead(II) Ion-Selective Electrodes based on Crown Ethers

Acryloylmethylbenzo-15-crown-5 was prepared from the reaction of 4'-hydroxymethylbenzo-15-crown-5 with acryloyl chloride. And, poly(acryloylmethylbenzo-15-crown-5) [poly(AMB15C5)] was synthesized by radical polymerization using AIBN as initiator in benzene. Coated wire lead(II) ion-selective electrodes (-CWISEs) using either poly(AMB15C5) or B15C5 as neutral carrier were prepared, respectively. -CWISEs gave linear responses with slopes of 28 1mV per decade within the concentration range of M, respectively. Also, the detection limits were M and response times were either 3 or 5 min. for B15C5 and poly(AMB15C5), respectively. -CWISE base on B15C5 was rather unstable than poly(AMB15C5)'s due to solubility of the B15C5 in water. The selectivity coefficients of a variety of interfering ions such as and were small (), while those of and were large (0.1∼0.01). In addition, the electrode responses depended upon the pH of test solution and the composition of the membrane. In the range pH 3∼6 of test solution, potentials of Pb2+-CWISEs were hardly changed. The optimal contents of B15C5 and poly(AMB15C5) were 7.7 wt% and 13.1 wt%, respectively.
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms

This paper develops a novel and uniﬁed framework to analyze the convergence of a large family of Q-learning algorithms from the switching system perspective. We show that the nonlinear ODE models associated with Q-learning and many of its variants can be naturally formulated as afﬁne switching systems . Building on their asymptotic stability, we obtain a number of interesting results: (i) we provide a simple ODE analysis for the convergence of asynchronous Q-learning under relatively weak assumptions; (ii) we establish the ﬁrst convergence analysis of the averaging Q-learning algorithm, and (iii) we derive a new sufﬁcient condition for the convergence of Q-learning with linear function approximation
Divisibility Networks of the Rational Numbers in the Unit Interval

Divisibility networks of natural numbers present a scale-free distribution as many other process in real life due to human interventions. This was quite unexpected since it is hard to find patterns concerning anything related with prime numbers. However, it is by now unclear if this behavior can also be found in other networks of mathematical nature. Even more, it was yet unknown if such patterns are present in other divisibility networks. We study networks of rational numbers in the unit interval where the edges are defined via the divisibility relation. Since we are dealing with infinite sets, we need to define an increasing covering of subnetworks. This requires an order of the numbers different from the canonical one. Therefore, we propose the construction of four different orders of the rational numbers in the unit interval inspired in Cantor’s diagonal argument. We motivate why these orders are chosen and we compare the topologies of the corresponding divisibility networks showing that all of them have a free-scale distribution. We also discuss which of the four networks should be more suitable for these analyses.
Learning to Adapt to Evolving Domains

Domain adaptation aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Current domain adaptation methods have made substantial advances in adapting discrete domains. However, this can be unrealistic in real-world applications, where target data usually come in an online and continually evolving manner, posing challenges to classic domain adaptation paradigm: (1) Mainstream domain adaptation methods are tailored to stationary target domains, and can fail in non-stationary environments. (2) Since the target data arrive online, the model should also maintain competence on previous target data, i.e. adapt without forgetting. To tackle these challenges, we propose a meta-adaptation framework which enables the learner to adapt to continually evolving target domains without forgetting. Our framework consists of two components: a meta-objective of learning representations to adapt to evolving domains, enabling meta-learning for unsupervised domain adaptation; and a meta-adapter for learning to adapt without forgetting, reserving knowledge from previous target data. Experiments validate the effectiveness our method on evolving domain adaptation benchmarks.
A convex optimization formulation for multivariate regression

Multivariate regression (or multi-task learning) concerns the task of predicting the value of multiple responses from a set of covariates. In this article, we pro-pose a convex optimization formulation for high-dimensional multivariate linear regression under a general error covariance structure. The main difﬁculty with simultaneous estimation of the regression coefﬁcients and the error covariance matrix lies in the fact that the negative log-likelihood function is not convex. To overcome this difﬁculty, a new parameterization is proposed, under which the negative log-likelihood function is proved to be convex. For faster computation, two other alternative loss functions are also considered, and proved to be convex under the proposed parameterization. This new parameterization is also useful for covariate-adjusted Gaussian graphical modeling in which the inverse of the error covariance matrix is of interest. A joint non-asymptotic analysis of the regression coefﬁcients and the error covariance matrix is carried out under the new parameterization. In particular, we show that the proposed method recovers the oracle estimator under sharp scaling conditions, and rates of convergence in terms of vector (cid:96) ∞ norm are also established. Empirically, the proposed methods outperform existing high-dimensional multivariate linear regression methods that are based on either minimizing certain non-convex criteria or certain two-step procedures.
On Efficiency in Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efﬁcient solutions to sequential decision making problems, both in terms of statistical as well as computational efﬁciency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-eﬁts of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efﬁcient HRL methods. Speciﬁcally, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efﬁcient, as established through a ﬁnite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efﬁcient.
Towards Interactive Virtual Dogs as a Pervasive Social Companion in Augmented Reality

Pets and animal-assisted intervention sessions have shown to be beneﬁcial for humans’ mental, social, and physical health. However, for speciﬁc populations, factors such as hygiene restrictions, allergies, and care and resource limitations reduce interaction opportunities. In parallel, understanding the capabilities of animals’ technological representations, such as robotic and digital forms, have received considerable attention and has fueled the utilization of many of these technological representations. Additionally, recent advances in augmented reality technology have allowed for the realization of virtual animals with ﬂexible appearances and behaviors to exist in the real world. In this demo, we present a companion virtual dog in augmented reality that aims to facilitate a range of interactions with populations, such as children and older adults. We discuss the potential beneﬁts and limitations of such a companion and propose future use cases and research directions.
Epidemiological Model With Anomalous Kinetics: Early Stages of the COVID-19 Pandemic

We generalize the phenomenological, law of mass action-like, SIR and SEIR epidemiological models to situations with anomalous kinetics. Specifically, the contagion and removal terms, normally linear in the fraction I of infected people, are taken to depend on I q u p and I q d o w n , respectively. These dependencies can be understood as highly reduced effective descriptions of contagion via anomalous diffusion of susceptible and infected people in fractal geometries and removal (i.e., recovery or death) via complex mechanisms leading to slowly decaying removal-time distributions. We obtain rather convincing fits to time series for both active cases and mortality with the same values of ( q u p , q d o w n ) for a given country, suggesting that such aspects may in fact be present in the early evolution of the COVID-19 pandemic. We also obtain approximate values for the effective population N e f f , which turns out to be a small percentage of the entire population N for each country.
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms

Methods for adversarial robustness certiﬁcation aim to provide an upper bound on the test error of a classiﬁer under adversarial manipulation of its input. Current certiﬁcation methods are computationally expensive and limited to attacks that optimize the manipulation with respect to a norm. We overcome these limitations by investigating the robustness properties of Nearest Prototype Classiﬁers (NPCs) like learning vector quantization and large margin nearest neighbor. For this purpose, we study the hypothesis margin. We prove that if NPCs use a dissimilarity measure induced by a seminorm, the hypothesis margin is a tight lower bound on the size of adversarial attacks and can be calculated in constant time—this provides the ﬁrst adversarial robustness certiﬁcate calculable in reasonable time. Finally, we show that each NPC trained by a triplet loss maximizes the hypothesis margin and is therefore optimized for adversarial robustness. In the presented evaluation, we demonstrate that NPCs optimized for adversarial robustness are competitive with state-of-the-art methods and set a new benchmark with respect to computational complexity for robustness certiﬁcation.
Online Bayesian Persuasion

In Bayesian persuasion, an informed sender has to design a signaling scheme that discloses the right amount of information so as to inﬂuence the behavior of a self-interested receiver. This kind of strategic interaction is ubiquitous in real-world economic scenarios. However, the seminal model by Kamenica and Gentzkow makes some stringent assumptions that limit its applicability in practice. One of the most limiting assumptions is, arguably, that the sender is required to know the receiver’s utility function to compute an optimal signaling scheme. We relax this assumption through an online learning framework in which the sender repeatedly faces a receiver whose type is unknown and chosen adversarially at each round from a ﬁnite set of possible types. We are interested in no-regret algorithms prescribing a signaling scheme at each round of the repeated interaction with performances close to that of a best-in-hindsight signaling scheme. First, we prove a hardness result on the per-round running time required to achieve no-α -regret for any α < 1 . Then, we provide algorithms for the full and partial feedback models with regret bounds sublinear in the number of rounds and polynomial in the size of the instance.
Supplementary for UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging

Layer input channels ks stride conv1 image C′ 3/64 3 1 conv2 conv1 64/128 3 1 conv3 conv2 128/256 3 1 conv4 conv3 256/128 3 1 conv5 conv4 128/64 3 1 conv6 conv5 64/3 3 1 sigmoid conv6 N/A N/A N/A We adopt a simplified U-Net adopted in Cycle-GAN [6]. Specifically, we remove the two most inner convolutions and upconvolutions. The detailed H architectures for the DDH and UDH are shown in Table 1 and Table 2, respectively, where the conv layer is followed by a BatchNorm layer and ReLU layer. In contrast to the final Sigmoid layer adopted in DDH, we adopt a Tanh layer multiplied by a scale factor which is set to 10/255 by referencing the engineering choice in universal adversarial perturbations [3, 4, 1]. Note that different from [3, 4, 1], Se is minimized in the loss even with this constraint. With such a scale factor, some pixel intensities in C ′ in UDH might still be out of the range [0, 1]. However, empirically we find that the
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems

This paper proposes two efﬁcient algorithms for computing approximate second-order stationary points (SOSPs) of problems with generic smooth non-convex objective functions and generic linear constraints. While ﬁnding (approximate) SOSPs for the class of smooth non-convex linearly constrained problems is computationally intractable, we show that generic problem instances in this class can be solved efﬁciently. Speciﬁcally, for a generic problem instance, we show that certain strict complementarity (SC) condition holds for all Karush-Kuhn-Tucker (KKT) solutions. Based on this condition, we design an algorithm named Successive Negative-curvature grAdient Projection (SNAP) , which performs either conventional gradient projection or some negative curvature based projection steps to ﬁnd SOSPs. SNAP is a second-order algorithm that requires e O (max { 1 /ϵ 2 G , 1 /ϵ 3 H } ) iterations to compute an ( ϵ G , ϵ H ) -SOSP, where e O hides the iteration complexity for eigenvalue-decomposition. Building on SNAP, we propose a ﬁrst-order algorithm, named SNAP + , that requires O (1 /ϵ 2 . 5 ) iterations to compute ( ϵ, √ ϵ ) -SOSP. The per-iteration computational complexities of our algorithms are polynomial in the number of constraints and problem dimension. To the best of our knowledge, this is the ﬁrst time that ﬁrst-order algorithms with polynomial per-iteration complexity and global sublinear
Autoencoders that don't overfit towards the Identity

Autoencoders (AE) aim to reproduce the output from the input. They may hence tend to overﬁt towards learning the identity-function between the input and output, i.e., they may predict each feature in the output from itself in the input. This is not useful, however, when AEs are used for prediction tasks in the presence of noise in the data. It may seem intuitively evident that this kind of overﬁtting is prevented by training a denoising AE [36], as the dropped-out features have to be predicted from the other features. In this paper, we consider linear autoencoders, as they facilitate analytic solutions, and ﬁrst show that denoising / dropout actually prevents the overﬁtting towards the identity-function only to the degree that it is penalized by the induced L2-norm regularization. In the main theorem of this paper, we show that the emphasized denoising AE [37] is indeed capable of completely eliminating the overﬁtting towards the identity-function. Our derivations reveal several new insights, including the closed-form solution of the full-rank model, as well as a new (near-)orthogonality constraint in the low-rank model. While this constraint is conceptually very different from the regularizers recently proposed in [11, 42, 14], their resulting effects on the learned embeddings are empirically similar. Our experiments on three well-known data-sets corroborate the various theoretical insights derived in this paper.
Stochastic Deep Gaussian Processes over Graphs

In this paper we propose Stochastic D eep G aussian P rocesses over G raphs (DGPG), which are deep Gaussian models that learn the mappings between input and output signals in graph domains. The approximate posterior distributions of the latent variables are derived with variational inference, and the evidence lower bound is evaluated and optimized by the proposed recursive sampling scheme. The Bayesian non-parametric natural of our model allows it to resist overﬁtting, while the expressive deep structure grants it the potential to learn complex relations. Extensive experiments demonstrate that our method achieves superior performances in both small size (< 50) and large size (> 35,000) datasets. We show that DGPG outperforms another Gaussian-based approach, and is competitive to a state-of-the-art method in the challenging task of trafﬁc ﬂow prediction. Our model is also capable of capturing uncertainties in a mathematical principled way and automatically discovering which vertices and features are relevant to the prediction.
Falcon: Fast Spectral Inference on Encrypted Data

Homomorphic Encryption (HE) based secure Neural Networks(NNs) inference is one of the most promising security solutions to emerging Machine Learning as a Service (MLaaS). In the HE-based MLaaS setting, a client encrypts the sensitive data, and uploads the encrypted data to the server that directly processes the encrypted data without decryption, and returns the encrypted result to the client. The client’S data privacy is preserved since only the client has the private key. Existing HE-enabled Neural Networks (HENNs), however, suffer from heavy computational overheads. The state-of-the-art HENNs adopt ciphertext packing techniques to reduce homomorphic multiplications by packing multiple messages into one single ciphertext. Nevertheless, rotations are required in these HENNs to implement the sum of the elements within the same ciphertext. We observed that HENNs have to pay signiﬁcant computing overhead on rotations, and each of rotations is ∼ 10 × more expensive than homomorphic multiplications between ciphertext and plaintext. So the massive rotations have become a primary obstacle of efﬁcient HENNs. In this paper, we propose a fast, frequency-domain deep neural network called Falcon, for fast inferences on encrypted data. Falcon includes a fast Homomor-phic Discrete Fourier Transform (HDFT) using block-circulant matrices to ho-momorphically support spectral operations. We also propose several efﬁcient methods to reduce inference latency, including Homomorphic Spectral Convolu-tion and Homomorphic Spectral Fully Connected operations by combining the batched HE and block-circulant matrices. Our experimental results show Falcon achieves the state-of-the-art inference accuracy and reduces the inference latency by 45 . 45% ∼ 85 . 34% over