Miracle-dz/UniFAR-FTUs huggingface dataset:
On the non-linear equation of a p-adic open string for a scalar field

A survey is given of results concerning the structure of solutions of a one-dimensional non-linear pseudodifferential equation describing the dynamics (rolling) of a -adic open string for the scalar tachyon field. It is indicated that discontinuous solutions can occur for . The method of expanding the solutions in series of Hermite polynomials and the method of reduction to a non-linear boundary-value problem for the heat equation are used. Some unsolved problems are given, in particular, the existence problem for a solution of the boundary-value problem for .
Portfolio optimization and the random magnet problem

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movements of assets are mutually correlated and therefore knowledge of cross-correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is analogous to the problem of finding the magnetization of a random magnet. The interactions for this “random magnet problem” are given by the cross-correlation matrix C of stock returns. We find that random matrix theory allows us to make an estimate for C which outperforms the standard estimate in terms of constructing an investment which carries a minimum level of risk.
LocVTP: Video-Text Pre-training for Temporal Localization

Video-Text Pre-training (VTP) aims to learn transferable representations for various downstream tasks from large-scale web videos. To date, almost all existing VTP methods are limited to retrieval-based downstream tasks, e.g., video retrieval, whereas their transfer potentials on localization-based tasks, e.g., temporal grounding, are under-explored. In this paper, we experimentally analyze and demonstrate the incompatibility of current VTP methods with localization tasks, and propose a novel Localization-oriented Video-Text Pre-training framework, dubbed as LocVTP. Specifically, we perform the fine-grained contrastive alignment as a complement to the coarse-grained one by a clip-word correspondence discovery scheme. To further enhance the temporal reasoning ability of the learned feature, we propose a context projection head and a temporal aware contrastive loss to perceive the contextual relationships. Extensive experiments on four downstream tasks across six datasets demonstrate that our LocVTP achieves state-of-the-art performance on both retrieval-based and localization-based tasks. Furthermore, we conduct comprehensive ablation studies and thorough analyses to explore the optimum model designs and training strategies.
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots

The rapid development of artiﬁcial intelligence (AI) technology has enabled large-scale AI applications to land in the market and prac-tice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur frequently. Therefore, this paper proposes a semantics censorship chatbot system based on reinforcement learning, which is mainly composed of two parts: the Oﬀensive semantics censorship model and the semantics puriﬁcation model. Oﬀensive semantics review can combine the context of user input sentences to detect the rapid evolution of Oﬀensive semantics and respond to Oﬀensive semantics responses. The semantics puriﬁcation model For the case of chatting robot models, it has been contaminated by large numbers of oﬀensive semantics, by strengthening the oﬀensive reply learned by the learning algorithm, rather than rolling back to the early versions. In addition, by integrating a once-through learning approach, the speed of semantics puriﬁcation is accelerated while reducing the impact on the quality of replies. The experimental results show that our proposed approach reduces the probability of the chat model generating oﬀensive replies and that the integration of the few-shot learning algorithm improves the training speed rapidly while eﬀectively slowing down the decline in BLEU values.
Inference and prediction in a multiple structural break model of economic time series

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and is treated as a random variable in our model. Second, our model adopts a hierarchical prior for regime coefficients, which allows for the regime coefficients of one regime to contain information about regime coefficients of other regimes. However, the regime coefficients can be analytically integrated out of the posterior distribution and therefore we only need to deal with one level of the hierarchy. Third, the implementation of our model is simple and the computational cost is low. Our model is applied to two different time series: S&P 500 monthly returns and U.S. real GDP quarterly growth rates. We linked breaks detected by our model to certain historical events. Abstract Approved: Thesis SupervisorApproved: Thesis Supervisor Title and Department
Jointly Predicting Emotion, Age, and Country Using Pre-Trained Acoustic Embedding

In this paper, we demonstrated the benefit of using a pre-trained model to extract acoustic embedding to jointly predict (multitask learning) three tasks: emotion, age, and native country. The pre-trained model was trained with wav2vec 2.0 large and robust model on the speech emotion corpus. The emotion and age tasks were regression problems, while country prediction was a classification task. A single harmonic mean from three metrics was used to evaluate the performance of multitask learning. The classifier was a linear network with two independent layers and shared layers connected to the output layers. This study explores multitask learning on different acoustic features (including the acoustic embedding extracted from a model trained on an affective speech dataset), seed numbers, batch sizes, and waveform normalizations for predicting paralinguistic information from speech.
Design and simulation of a novel E-mode GaN MIS-HEMT based on a cascode connection for suppression of electric field under gate and improvement of reliability

We proposed a novel AlGaN/GaN enhancement-mode (E-mode) high electron mobility transistor (HEMT) with a dual-gate structure and carried out the detailed numerical simulation of device operation using Silvaco Atlas. The dual-gate device is based on a cascode connection of an E-mode and a D-mode gate. The simulation results show that electric field under the gate is decreased by more than 70% compared to that of the conventional E-mode MIS-HEMTs (from 2.83 MV/cm decreased to 0.83 MV/cm). Thus, with the discussion of ionized trap density, the proposed dual-gate structure can highly improve electric field-related reliability, such as, threshold voltage stability. In addition, compared with HEMT with field plate structure, the proposed structure exhibits a simplified fabrication process and a more effective suppression of high electric field.
Dissipative Systems Analysis and Control. Theory and Applications

Two of the four very knowledgeable authors of this new monograph from the Springer series `Communications and Control Engineering' are already known as (co)authors of two other volumes in the same series and hence stand for high-quality publications in the field. This new book presents a very good advanced introduction to dissipative systems with focus on feedback control. The guiding principle of dissipation (or passivity) leads to a profound understanding of various control schemes. As an example, one of the benefits from this approach is that the search for a suitable Lyapunov function as the criterion for stability is closer to being constructive starting from generalized energies. In the first three chapters, properties of linear and nonlinear dissipative systems are analysed, whence the next chapters concentrate on the design of different feedback controllers. This book excellently combines the theoretical outline including the mathematical foundations (partly with proofs) with lots of elaborate examples. There is almost no concept or consideration which is not illustrated by some calculation for a given system. This is the real strength of this monograph: not only is the reader enabled to verify his/her own understanding and to appraise the relevance of a given concept by these inclusions, but these reflect also the richness of the set of very different systems for which these concepts hold. Almost every standard problem can be found at some point in this book. The last chapter is devoted to the analysis and control of two electromechanical laboratory experiments. Here, imperfect model equations and tracking errors challenge the theoretical concepts. The solutions found by the authors are very instructive for readers interested in applications. A helpful appendix clarifies some of the mathematical concepts on which the main part of the book relies. In summary, this book is really to be recommended to those who seek a sound, well written and far-reaching introduction. Holger Kantz
Learning Underspecified Models

This paper examines whether one can learn to play an optimal action while only knowing part of true specification of the environment. We choose the optimal pricing problem as our laboratory, where the monopolist is endowed with an underspecified model of the market demand, but can observe market outcomes. In contrast to conventional learning models where the model specification is complete and exogenously fixed, the monopolist has to learn the specification and the parameters of the demand curve from the data. We formulate the learning dynamics as an algorithm that forecast the optimal price based on the data, following the machine learning literature (Shalev-Shwartz and Ben-David (2014)). Inspired by PAC learnability, we develop a new notion of learnability by requiring that the algorithm must produce an accurate forecast with a reasonable amount of data uniformly over the class of models consistent with the part of the true specification. In addition, we assume that the monopolist has a lexicographic preference over the payoff and the complexity cost of the algorithm, seeking an algorithm with a minimum number of parameters subject to PAC-guaranteeing the optimal solution (Rubinstein (1986)). We show that for the set of demand curves with strictly decreasing uniformly Lipschitz continuous marginal revenue curve, the optimal algorithm recursively estimates the slope and the intercept of the linear demand curve, even if the actual demand curve is not linear. The monopolist chooses a misspecified model to save computational cost, while learning the true optimal decision uniformly over the set of underspecified demand curves.
Inside oscillatons

Non-singular self-gravitating objects can be found by solving the coupled Einstein–Klein–Gordon (EKG) equations for a real scalar field. Such objects are generically known as oscillatons, in which the scalar field and the metric are fully time-dependent. In this paper, we describe a numerical procedure to minimize the nonlinearities present in the EKG equations, in the case of spherical symmetry, which permits us to find accurate numerical solutions. In order to gain physical insight of relativistic oscillatons, we study oscillatons in flat space, in the weak field limit, the so-called Newtonian oscillatons, using a fixed Schwarzschild background. This last case may be related to the ejected scalar field during a gravitational collapse of scalar field configurations.
Can Locational Disparity of Prosumer Energy Optimization Due to Inverter Rules Be Limited?

To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output does not cause voltage violations. However, such actions, as observed in this work, restrict the range available for local energy management, with more adverse losses on arbitrage profits for prosumers located farther away from the substation. The goal of the paper is three-fold: (a) to develop an optimal local energy optimization algorithm for activation of load flexibility and inverter-interfaced solar PV and energy storage under time-varying electricity prices; (b) to quantify the locational impact on prosumer arbitrage gains due to inverter injection rules prevalent in different energy markets; (c) to propose a computationally efficient hybrid inverter control policy which provides voltage regulation while substantially reducing locational disparity. Using numerical simulations on three identical prosumers located at different parts of a radial feeder, we show that our control policy is able to minimize locational disparity in arbitrage gains between customers at the beginning and end of the feeder to 1.4%, while PV curtailment is reduced by 91.7% compared to the base case with restrictive volt-Var and volt-watt policy.
Garment function module to reduce motion artifacts in heart-activity-sensing clothing based on a magnetic-induced conductivity sensing method

Bio-signal-sensing clothing has been actively developed worldwide. However, motion artifacts caused by a wearer’s motion degrade the bio-signal quality. A significant amount of work has been aimed at resolving this problem, but such work mainly focused on the application, rather than on the comfort, fashion, or function of the clothing. This study investigated the feasibility of reducing motion artifacts in the garment design by improving the garment function modules. To achieve this, the study comprised two parts: observation of the effect of the bodice module and that of the sleeve module. We found that the bodice module reduces motion artifacts and obtains stable signals of heart activity. Further, we found that the sleeve module reduces motion artifacts but not as significantly as the bodice module. These results suggest fundamental guidelines for applying apparel design techniques to heart-activity-sensing clothing based on the magnetic-induced sensing method.
HOLISMOKES. X. Comparison between neural network and semi-automated traditional modeling of strong lenses

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper (submitted) a neural network predicting the parameter values with corresponding uncertainties of a Singular Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we present a newly-developed pipeline glee_auto. py to model consistently any galaxy-scale lensing system. In contrast to previous automated modeling pipelines that require high-resolution images, glee_auto. py is optimized for ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee_tools. py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We apply the network to 31 real galaxy-scale lenses of HSC and compare the results to the traditional models. In the direct comparison, we find a very good match for the Einstein radius especially for systems with $\theta_E \gtrsim 2$". The lens mass center and ellipticity show reasonable agreement. The main discrepancies are on the external shear as expected from our tests on mock systems. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with $\sim10^5$ lenses expected.
Domain Decomposition Learning Methods for Solving Elliptic Problems

With recent advancements in computer hardware and software platforms, there has been a surge of interest in solving partial differential equations with deep learning-based methods, and the integration with domain decomposition strategies has attracted considerable attention owing to its enhanced representation and parallelization capacities of the network solution. While there are already several works that substitute the subproblem solver with neural networks for overlapping Schwarz methods, the non-overlapping counterpart has not been extensively explored because of the inaccurate flux estimation at interface that would propagate errors to neighbouring subdomains and eventually hinder the convergence of outer iterations. In this study, a novel learning approach for solving elliptic boundary value problems, i.e., the compensated deep Ritz method using neural network extension operators, is proposed to enable reliable flux transmission across subdomain interfaces, thereby allowing us to construct effective learning algorithms for realizing non-overlapping domain decomposition methods (DDMs) in the presence of erroneous interface conditions. Numerical experiments on a variety of elliptic problems, including regular and irregular interfaces, low and high dimensions, two and four subdomains, and smooth and high-contrast coefficients are carried out to validate the effectiveness of our proposed algorithms.
Secure Lightweight Authentication for Multi User IoT Environment

— The Internet of Things (IoT) is giving a boost to a plethora of new opportunities for the robust and sustainable deployment of cyber-physical systems. The cornerstone of any IoT system is the sensing devices. These sensing devices have considerable resource constraints, including insufﬁcient battery capacity, CPU capability, and physical security. Because of such resource constraints, designing lightweight cryptographic proto- cols is an opportunity. Remote User Authentication ensures that two parties establish a secure and durable session key. This study presents a lightweight and safe authentication strategy for the user-gateway (U-GW) IoT network model. The proposed system is designed leveraging Elliptic Curve Cryptography (ECC). We undertake a formal security analysis with both the Automated Validation of Internet Security Protocols (AVISPA) and Bur-rows–Abadi–Needham (BAN) logic tools and an informal security assessment with the Delev-Yao channel. We use publish/subscribe based Message Queuing Telemetry Transport (MQTT) protocol for communication. Additionally, the performance analysis and comparison of security features show that the proposed scheme is resilient to well-known cryptographic threats.
Temporal Saliency Query Network for Efficient Video Recognition

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient frames without awareness of the class-specific saliency scores, which neglect the implicit association between the saliency of frames and its belonging category. To alleviate this issue, we devise a novel Temporal Saliency Query (TSQ) mechanism, which introduces class-specific information to provide fine-grained cues for saliency measurement. Specifically, we model the class-specific saliency measuring process as a query-response task. For each category, the common pattern of it is employed as a query and the most salient frames are responded to it. Then, the calculated similarities are adopted as the frame saliency scores. To achieve it, we propose a Temporal Saliency Query Network (TSQNet) that includes two instantiations of the TSQ mechanism based on visual appearance similarities and textual event-object relations. Afterward, cross-modality interactions are imposed to promote the information exchange between them. Finally, we use the class-specific saliencies of the most confident categories generated by two modalities to perform the selection of salient frames. Extensive experiments demonstrate the effectiveness of our method by achieving state-of-the-art results on ActivityNet, FCVID and Mini-Kinetics datasets. Our project page is at https://lawrencexia2008.github. io/projects/tsqnet .
Epicutaneous sensitization. what do we know? сенсибилизация. Все ли мы знаем?

Background. According to the currently existing hypothesis, epicutaneous sensitization is one of the leading mechanisms in the development of food allergy. The aim of this review was to analyze immune mechanisms in epicutaneous sensitization and the role of skin barrier impairment. We performed a literature search using PubMed, UpToDate, Web of Science, and Scopus databases by the key words: epicutaneous sensitization, atopic dermatitis, skin barrier impairment, food allergy. Articles were to be in open access and present the most relevant information on the topic. Studies were selected by the largest sample size and the highest citation index. Once publications were identified, they were reviewed by all the authors to select the studies that specifically addressed the theme of the review. A total of 101 publications from 1998–2000 were included in the study. This review article discusses the data of experimental studies, sets out modern ideas about the hypothesis of a double exposure to an allergen, and presents research data proving the clinical significance of epicutaneous sensi tization in relation to food allergy. Knowledge about the mechanisms of epicutaneous sensitization development is necessary to elaborate strategies for prevention of food allergy. One of the modern trends in prevention is the use of emollients, which are supposed to restore the skin response. However, studies on preventive intake of emollients do not present a similar viewpoint. There is not enough evidence for or against the mechanism of epicutaneous sensitization as an indispensable condition for the formation of food allergies. Further research in this area is required.
Renormalization of twist-two operators in QCD and its application to singlet splitting functions

Splitting functions govern the scale evolution of parton distribution functions. Through a Mellin transformation, they are related to anomalous dimensions of twist-two operators in the operator product expansion. We study oﬀ-shell operator matrix element, where the physical operators mix under renormalization with other gauge-variant operators of the same quantum numbers. We devise a new method to systematically extract the Feynman rules resulting from those operators without knowing the operators themselves. As a ﬁrst application of the new approach, we independently reproduce the well-known three-loop singlet splitting functions obtained from computations of on-shell quantities. dimensions for 𝑛 = 6 and of the four-loop anomalous dimensions for 𝑛 up to 4.