Miracle-dz/UniFAR-FTUs huggingface dataset:
Soliton formation and the core-halo mass relation: An eigenstate perspective

UltraLight Dark Matter (ULDM) is an axion-like dark matter candidate with an extremely small particle mass. ULDM halos consist of a spherically symmetric solitonic core and an NFW-like skirt. We simulate halo creation via soliton mergers and use these results to explore the core-halo mass relation. We calculate the eigenstates of the merged halos and use these to isolate the solitonic core and calculate its relative contribution to the halo mass. We compare this approach to using a fitting function to isolate the core and find a difference in masses up to 30%. We analyze three families of simulations: equal-mass mergers, unequal-mass mergers, and halos with a two-step merger history. Setting the halo mass to the initial mass in the simulation does not yield a consistent core-halo relationship. Excluding material"ejected"by the collision yields a core-halo relationship with a slope of 1/3 for simultaneous mergers and roughly 0.4 for two-step mergers. Our findings suggest there is no universal core-halo mass relationship for ULDM and shed light on the differing results for the core-halo relationship previously reported in the literature.
Evolution of the radius of analyticity for the generalized Benjamin equation

In this work we consider the initial value problem for the generalized Benjamin equation \begin{equation}\label{Benj-IVP} \begin{cases} \partial_t u-l\mathcal{H} \partial_x^2u-\partial_x^3u+u^p\partial_xu = 0, \quad x,\; t\in \mathbb{R};\;\;,\; p\geq 1, \\ u(x,0) = u_0(x), \end{cases} \end{equation} where $u=u(x,t)$ is a real valued function, $0<l<1$ and $\mathcal{H}$ is the Hilbert transform. This model was introduced by T. B. Benjamin (J. Fluid Mech. 245 (1992) 401--411) and describes unidirectional propagation of long waves in a two-fluid system where the lower fluid with greater density is infinitely deep and the interface is subject to capillarity. We prove that the local solution to the IVP associated with the generalized Benjamin equation for given data in the spaces of functions analytic on a strip around the real axis continue to be analytic without shrinking the width of the strip in time. We also study the evolution in time of the radius of spatial analyticity and show that it can decrease as the time advances. Finally, we present an algebraic lower bound on the possible rate of decrease in time of the uniform radius of spatial analyticity.
Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data

Flooding is one of the most disastrous natural hazards responsible for substantial economic losses. A predictive model for ﬂood-induced ﬁnancial damages is helpful for many applications, such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. A Burr distribution with a bias correction scheme to increase the regressor’s predictive capability eﬀectively enables inference for claim amount distributions. Aiming to study the impact of physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study of the coastal counties in the eight US South-West states resulted in an R 2 = 0 . 807. Further analysis of 11 counties with a signiﬁcant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction signiﬁcantly improves the similarity with the reference distribution, and that adding the rainfall predictor strengthens the regressor performance.
Toward BCI-enabled Metaverse: A Joint Radio and Computing Resource Allocation Approach

—Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the way for the creation of intelligent human-like avatars. Our approach takes a ﬁrst step toward the Metaverse systems in which the digital avatars are envisioned to be more intelligent by collecting and analyzing brain signals through cellular networks. In our proposed system, Metaverse users experience Metaverse applications while sending their brain signals via uplink wireless channels in order to create intelligent human-like avatars at the base station. As such, the digital avatars can not only give useful recommendations for the users but also enable the system to create user-driven applications. Our proposed framework involves a mixed decision-making and classiﬁcation problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efﬁcient manner. To this end, we propose a hybrid training algorithm that utilizes recent advances in deep reinforcement learning to address the problem. Speciﬁcally, our hybrid training algorithm contains three deep neural networks cooperating with each other to enable better realization of the mixed decision-making and classiﬁcation problem. Simulation results show that our proposed framework can jointly address resource allocation for the system and classify brain signals of the users with highly accurate predictions.
Thermodynamic origin of the contact

As can be inferred from present experiments in ultracold gases, the scattering length is a quantity that determines the thermodynamic state of the gas. As such, a conjugate thermodynamic variable to it exists. Here, we show that the recently introduced contact is the conjugate of the inverse of the scattering length. We find that this identification, in addition to well-known approximations, allows for a derivation of three of the most relevant results concerning the contact, namely its relation to the adiabatic change of the energy, the general form of the energy and the generalized virial theorem. We find that the current identification of the contact variable depends on the use of the contact approximation for the intermolecular potential but it is independent of whether the gas is made out of fermions or bosons and of the strength of the interaction.
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series

In recent decades, mobile applications (apps) have gained enormous popularity. Smart services for smart cities increasingly gain attention. The main goal of this proposed research was to present a new artificial intelligence (AI)powered mobile app on Istanbul’s traffic congestion forecast using traffic density data. It addresses the research question using time series approaches (long short-term memory (LSTM), Transformer, and eXtreme Gradient Boosting (XGBoost)) based on past data over the traffic load dataset combined with meteorological conditions. While previous studies were limited to direct Istanbul traffic forecasting, in this study, we focused on district-based traffic forecasting that can be queried with a mobile app. The proposed pipeline was tested on the summarized Istanbul traffic dataset for 6 main distinct districts (Fatih, Buyukcekmece, Atasehir, Kagithane, Tuzla, and Bagcilar). Analysis of the simulation results on predicted models will be discussed according to performance indicators such as mean absolute percentage error (MAPE), mean average error (MAE), and root mean squared error (RMSE). And then, it was observed that the Transformer model made the most accurate traffic prediction with a minimum MAE score for each district. The developed traffic forecasting prototype is expected to be a starting point for future products for a mobile app suitable for citizens’ daily use.
Multistep Multiappliance Load Prediction

A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
Probing vector hair of black holes with extreme-mass-ratio inspirals

The bumblebee gravity model, with a vector field nonminimally coupled to gravity, is a natural extension of the Einstein-Maxwell theory. In this theory, a black hole can carry a vector hair, making the metric deviate from the Schwarzschild metric. To investigate the detectability of the vector hair, we consider an Extreme Mass Ratio Inspiral (EMRI) system, where a stellar-mass black hole inspiraling into a supermassive black hole. We find that, with a one-year observation of an EMRI by a space-based gravitational-wave detector, we can probe the vector charge as small as $Q\sim 10^{-3}$ in the bumblebee gravity model, which is about three orders of magnitude tighter comparing to current EHT observations.
Frequentist properties of Bayesian sequential tests

SUMMARY Let X1, X2, . denote independent random variables which are normally distributed with unknown mean 0 and unit variance. We consider sequential tests of the hypothesis Ho: 0 6. The tests which we consider were shown by Schwarz (1962) to approximate the optimal Bayesian tests with respect to a general loss structure and any prior density which is everywhere positive. Their continuation regions are bounded subsets of the (n, Sn) plane, where Sn is the cumulative sum. We give both inequalities and asymptotic expressions for the power function and the expected sample size. We also give comparisons of the properties of the approximate Bayesian test, the sequential probability ratio test, and the fixed sample size test.
Reasoning with Language Model Prompting: A Survey

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).
Resolving Open-textured Rules with Templated Interpretive Arguments

Open-textured terms in written rules are typically settled through interpretive argumentation. Ongoing work has attempted to catalogue the schemes used in such interpretive argumentation. But how can the use of these schemes affect the way in which people actually use and reason over the proper interpretations of open-textured terms? Using the interpretive argument-eliciting game Aporia as our framework, we carried out an empirical study to answer this question. Differing from previous work, we did not allow participants to argue for interpretations arbitrarily, but to only use arguments that fit with a given set of interpretive argument templates. Finally, we analyze the results captured by this new dataset, specifically focusing on practical implications for the development of interpretation-capable artificial reasoners.
A Survey on Password Guessing

Text password has served as the most popular method for user authentication so far, and is not likely to be totally replaced in foreseeable future. Password authentication offers several desirable properties (e.g., low-cost, highly available, easy-to-implement, reusable). However, it suffers from a critical security issue mainly caused by the inability to memorize complicated strings of humans. Users tend to choose easy-to-remember passwords which are not uniformly distributed in the key space. Thus, user-selected passwords are susceptible to guessing attacks. In order to encourage and support users to use strong passwords, it is necessary to simulate automated password guessing methods to determine the passwords’ strength and identify weak passwords. A large number of password guessing models have been proposed in the literature. However, little attention was paid to the task of providing a systematic survey which is necessary to review the state-of-the-art approaches, identify gaps, and avoid duplicate studies. Motivated by that, we conduct a comprehensive survey on all password guessing studies presented in the literature from 1979 to 2022. We propose a generic methodology map to present an overview of existing methods. Then, we explain each representative approach in detail. The experimental procedures and available datasets used to evaluate password guessing models are summarized, and the reported performances of representative studies are compared. Finally, the current limitations and the open problems as future research directions are discussed. We believe that this survey is helpful to both experts and newcomers who are interested in password security.
Review of security techniques for memristor computing systems

Neural network (NN) algorithms have become the dominant tool in visual object recognition, natural language processing, and robotics. To enhance the computational efficiency of these algorithms, in comparison to the traditional von Neuman computing architectures, researchers have been focusing on memristor computing systems. A major drawback when using memristor computing systems today is that, in the artificial intelligence (AI) era, well-trained NN models are intellectual property and, when loaded in the memristor computing systems, face theft threats, especially when running in edge devices. An adversary may steal the well-trained NN models through advanced attacks such as learning attacks and side-channel analysis. In this paper, we review different security techniques for protecting memristor computing systems. Two threat models are described based on their assumptions regarding the adversary’s capabilities: a black-box (BB) model and a white-box (WB) model. We categorize the existing security techniques into five classes in the context of these threat models: thwarting learning attacks (BB), thwarting side-channel attacks (BB), NN model encryption (WB), NN weight transformation (WB), and fingerprint embedding (WB). We also present a cross-comparison of the limitations of the security techniques. This paper could serve as an aid when designing secure memristor computing systems.
Risk-Sensitive Reinforcement Learning with Exponential Criteria

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variance in the total reward amongst different episodes in slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments.
HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation

Language models with the Transformers structure have shown great performance in natural language processing.However, there still poses problems when fine-tuning pre-trained language models on downstream tasks, such as over-fitting or representation collapse.In this work, we propose HyPe, a simple yet effective fine-tuning technique to alleviate such problems by perturbing hidden representations of Transformers layers. Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information. Therefore, making the Transformers layers more robust to hidden representation perturbations can further benefit the fine-tuning of PLMs en bloc.We conduct extensive experiments and analyses on GLUE and other natural language inference datasets. Results demonstrate that HyPe outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. In addition, HyPe acquires negligible computational overheads, and is better than and compatible with previous state-of-the-art fine-tuning techniques.
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems

Despite tremendous advancements in dialogue systems, stable evaluation still requires human judgments producing notoriously high-variance metrics due to their inherent subjectivity.Moreover, methods and labels in dialogue evaluation are not fully standardized, especially for open-domain chats, with a lack of work to compare and assess the validity of those approaches.The use of inconsistent evaluation can misinform the performance of a dialogue system, which becomes a major hurdle to enhance it.Thus, a dimensional evaluation of chat-oriented open-domain dialogue systems that reliably measures several aspects of dialogue capabilities is desired.This paper presents a novel human evaluation method to estimate the rates of many{pasted macro ‘LN’} dialogue system behaviors.Our method is used to evaluate four state-of-the-art open-domain dialogue systems and compared with existing approaches.The analysis demonstrates that our behavior method is more suitable than alternative Likert-style or comparative approaches for dimensional evaluation of these systems.
Fast FullSubNet: Accelerate Full-band and Sub-band Fusion Model for Single-channel Speech Enhancement

FullSubNet is our recently proposed real-time single-channel speech enhancement network that achieves outstanding performance on the Deep Noise Suppression (DNS) Challenge dataset. A number of variants of FullSubNet have been proposed, but they all focus on the structure design towards better performance and are rarely concerned with computational efficiency. For many speech enhancement applications, a key feature is that system runs on a real-time, latency-sensitive, battery-powered platform, which strictly limits the algorithm latency and computational complexity. In this work, we propose a new architecture named Fast FullSubNet dedicated to accelerating the computation of FullSubNet. Specifically, Fast FullSubNet processes sub-band speech spectra in the mel-frequency domain by using cascaded linear-to-mel full-band, sub-band, and mel-to-linear full-band models such that frequencies involved in the sub-band computation are vastly reduced. After that, a down-sampling operation is proposed for the sub-band input sequence to further reduce the computational complexity along the time axis. Experimental results show that, compared to FullSubNet, Fast FullSubNet has only 13\% computational complexity and 16\% processing time, and achieves comparable or even better performance. Code and audio samples are available at https://github.com/Audio-WestlakeU/FullSubNet.
Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning

—Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.
Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.
Breaking the 4 barrier for the bound of a generating set of the class group

Under the assumption of the validity of the Generalized Riemann Hypothesis, we prove that the class group of every ﬁeld of degree n and discriminant with absolute value ∆ can be generated using prime ideals with norm ≤ (4 − 1 / (2 n )) log 2 ∆, except for a ﬁnite number of ﬁelds of degree n ≤ 8. For those ﬁelds, the conclusion holds with the slightly larger limit (4 − 1 / (3 n )) log 2 ∆.