Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of spatio-temporal resolutions of precipitation on flood events simulation based on multi-model structures — A case study over Xiang River Basin in China

Accurate flood events simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatio-temporal resolutions of precipitation on flood events simulation in a large-scale catchment of 15 China. We use the high spatio-temporal resolutions Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcings for hydrologic simulation. Three hydrological models (HBV, SWAT, and DHSVM) and a data-driven model (Long Short-Term Memory (LSTM) network) are utilized for flood events simulation. Two calibration strategies are carried out, one of which targets at matching the flood events and the other one is the 20 conventional strategy to match continuous streamflow. The results indicate that the event-based calibration strategy improves the performance of flood events simulation, compared with conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood events simulation at finer temporal resolution, especially in flood peaks simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains 25 improvement when degrading the spatial resolution of IMERG-L. Generally, the LSTM outperforms the hydrological models in
A geometrical theory for spiral waves in excitable media

In this paper, we develop a geometrical theory for waves in excitable reacting media. Using singular perturbation arguments and dispersion of traveling plane wave trains, we derive an approximate theory of wave front propagation which has strong resemblance to the geometrical diffraction theory of high frequency waves in hyperbolic systems, governed by the eikonal equation. Using this theory, we study the effect of curvature on waves in excitable media, specifically, rotating spiral patterns in planar regions. From this theory we are able to determine the frequency and wavelength for spiral patterns in excitable, nonoscillatory media.
Optimum distribution of heat exchanger inventory for power density optimization of an endoreversible closed Brayton cycle

In this paper, the power density (defined as the ratio of the power output to the maximum specific volume in the cycle) is taken as the objective for performance optimizations of an endoreversible closed Brayton cycle coupled to constant-temperature heat reservoirs in the viewpoint of finite-time thermodynamics (FTT) or entropy generation minimization (EGM). The optimum heat conductance distribution corresponding to the optimum power density of the hot- and cold-side heat exchangers for the fixed heat exchanger inventory is analysed using numerical examples. The influence of some design parameters on the optimum heat conductance distribution and the maximum power density and the optimum pressure ratio corresponding to the maximum power density are provided. The power plant design with optimization leads to higher efficiency and smaller size. } \fnm{3}{Author to whom correspondence should be addressed.
Model reduction using the Routh stability criterion

A new method of model reduction is introduced based on the Routh stability criterion. The reduced order transfer function is determined directly from elements in the Routh stability arrays of the high-order denominator and numerator. An eighth order example illustrates the accuracy of the method, the preservation of the frequency response and computational simplicity. The method is equally applicable to unstable systems.
Effectiveness of the Coronavirus Disease 2019 (COVID-19) Bivalent Vaccine

Background. The purpose of this study was to evaluate whether a bivalent COVID-19 vaccine protects against COVID-19. Methods. Employees of Cleveland Clinic in employment on the day the bivalent COVID-19 vaccine first became available to employees, were included. The cumulative incidence of COVID-19 was examined over the following weeks. Protection provided by vaccination (analyzed as a time-dependent covariate) was evaluated using Cox proportional hazards regression. The analysis was adjusted for the pandemic phase when the last prior COVID-19 episode occurred, and the number of prior vaccine doses received. Results. Among 51011 employees, 20689 (41%) had had a previous documented episode of COVID-19, and 42064 (83%) had received at least two doses of a COVID-19 vaccine. COVID-19 occurred in 2452 (5%) during the study. Risk of COVID-19 increased with time since the most recent prior COVID-19 episode and with the number of vaccine doses previously received. In multivariable analysis, the bivalent vaccinated state was independently associated with lower risk of COVID-19 (HR, .70; 95% C.I., .61-.80), leading to an estimated vaccine effectiveness (VE) of 30% (95% CI, 20-39%). Compared to last exposure to SARS-CoV-2 within 90 days, last exposure 6-9 months previously was associated with twice the risk of COVID-19, and last exposure 9-12 months previously with 3.5 times the risk. Conclusions. The bivalent COVID-19 vaccine given to working-aged adults afforded modest protection overall against COVID-19, while the virus strains dominant in the community were those represented in the vaccine.
A comparison of two engineering models for outdoor sound propagation: Harmonoise and Nord2000

Two established engineering models for predicting environmental noise levels, the European “Harmonoise” and the Scandinavian “Nord2000”, have been compared for a number of test cases involving a variety of source and receiver configurations, terrain profiles, noise barriers, properties of the ground, and meteorological conditions. The predictions are compared with reference data from the literature, and with new experimental results. In most cases the models agree very well, but in some cases the predictions deviate significantly from each other. There are many similarities between the approximations used in the two models, but some parts of the modelling methods are clearly different. The most significant differences between the two modelling methods are briefly pointed out, and an attempt is made to explain the observed differences between the predictions.
Intriguing Aspects of the Monsoon Low-Level Jet over Peninsular India Revealed by High-Resolution GPS Radiosonde Observations

AbstractThe strong cross-equatorial flow in the lower troposphere, widely known as the monsoon low-level jet (MLLJ), plays an important role in the Indian summer monsoon (ISM) rainfall during June–September. Using high-resolution GPS radiosonde observations over Gadanki (13.5°N, 79.2°E), some new aspects of MLLJ have been reported. In the present study it is found that, on average, the MLLJ exists at 710 hPa over southeastern peninsular India, rather than at 850 hPa as reported by earlier studies. It is observed that the ECMWF Re-Analysis (ERA)-Interim data provide better results on the spatial, temporal, and vertical variation of MLLJ. Further, the characteristics of the MLLJ during the active and break spells of ISM are also investigated; higher MLLJ core height and intensity are found during active phases of the Indian monsoon. This study emphasizes the use of high-resolution measurements for studying monsoon dynamics in detail.
An improved best-first branch-and-bound algorithm for constrained two-dimensional guillotine cutting problems

The constrained two-dimensional cutting problem involves maximising the sum of the profits obtained from small rectangular pieces cut from a large rectangular plate where the number of each type of cut piece cannot exceed a prescribed quantity. This paper proposes a best-first branch-and-bound algorithm to find the optimal solution to the problem. The proposed algorithm uses an efficient method to remove the duplicate patterns, and it improves the existing upper bounds. It also prevents the construction of dominated patterns and introduces a new bounding strategy that can prune more than one node at a time. Computational results are compared with a well-known exact algorithm to demonstrate the efficiency of the proposed algorithm. The proposed algorithm is as fast as or faster than the existing algorithm and reduces the average computing time by up to 99% for benchmark problems. For some problems, it can also find optimal solutions that existing algorithms are not able to find.
THE IMPACT OF MEDIA COVERAGE ON THE DYNAMICS OF INFECTIOUS DISEASE

In this paper, we give a compartment model to discuss the influence of media coverage to the spreading and controlling of infectious disease in a given region. The model exhibits two equilibria: a disease-free and a unique endemic equilibrium. Stability analysis of the models shows that the disease-free equilibrium is globally asymptotically stable if the reproduction number (ℝ0), which depends on parameters, is less than unity. But if ℝ0 > 1, it is shown that a unique endemic equilibrium appears, which is asymptotically stable. On a special case, the endemic equilibrium is globally stable. We discuss the role of media coverage on the spreading based on the theory results.
Groups whose Projective character degrees are powers of a prime

Let Gbe a finite group, and P:G → GL(n, ) be such that for all x, y ∈ G (i) P(x)P(y) = α(x, y)P(xy), and (ii)P(l) = In, where α(x, y) ∈ *; then P is a projective representation of G with cocycle α and degree n. For other basic definitions concerning projective representations see [4].
Variational bounds on the entries of the inverse of a matrix

Variational and bivariational functionals are used to derive bounds on the entries of the inverse of a real non-singular matrix. Bounds are given for both the symmetric and non-symmetric case. In the symmetric and positive definite case, the Kantorovich inequality and Weilandt inequality are available for providing such bounds. The bounds we derive can not be obtained using these established inequalities and in a number of examples our bounds are found to be tighter when simple trial vectors are used. For the non-symmetric case, the bounds we derive are of uncertain value as there is little with which to compare them
Tests of two convection theories for red giant and red supergiant envelopes

Two theories of stellar envelope convection are considered here in the context of red giants and red supergiants of intermediate to high mass: Boehm-Vitense's standard mixing-length theory (MLT) and Canuto & Mazzitelli's new theory incorporating the full spectrum of turbulence (FST). Both theories assume incompressible convection. Two formulations of the convective mixing length are also evaluated: l proportional to the local pressure scale height (H(sub P)) and l proportional to the distance from the upper boundary of the convection zone (z). Applications to test both theories are made by calculating stellar evolutionary sequences into the red zone (z). Applications to test both theories are made by calculating stellar evolutionary sequences into the red phase of core helium burning. Since the theoretically predicted effective temperatures for cool stars are known to be sensitive to the assigned value of the mixing length, this quantity has been individually calibrated for each evolutionary sequence. The calibration is done in a composite Hertzsprung-Russell diagram for the red giant and red supergiant members of well-observed Galactic open clusters. The MLT model requires the constant of proportionality for the convective mixing length to vary by a small but statistically significant amount with stellar mass, whereas the FST model succeeds in all cases with the mixing lenghth simply set equal to z. The structure of the deep stellar interior, however, remains very nearly unaffected by the choices of convection theory and mixing lenghth. Inside the convective envelope itself, a density inversion always occurs, but is somewhat smaller for the convectively more efficient MLT model. On physical grounds the FST model is preferable, and seems to alleviate the problem of finding the proper mixing length.
Applied Soft

much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We
Some topological properties of residually Černikov groups

In this paper we shall indicate how to generalise the concept of a cofinite group (see [7]). We recall that any residually finite group can be made into a topological group by taking as a basis of neighbourhoods of the identity precisely the normal subgroups of finite index. The class of compact cofinite groups is then easily seen to be the class of profinite groups, where a group is profinite if and only if it is an inverse limit of finite groups. It turns out that every cofinite group can be embedded as a dense subgroup of a profinite group. This has important consequences for the class of countable locally finite-soluble groups with finite Sylow p-subgroups for all primes p, as shown in [7] and [14].
Approximate solution methods for the capacitated multi-facility Weber problem

This work considers the capacitated multi-facility Weber problem, which is concerned with locating m facilities and allocating their limited capacities to n customers in order to satisfy their demand at minimum total transportation cost. This is a non-convex optimization problem and difficult to solve. Therefore, approximate solution methods are proposed in this article. Some of them are based on the relaxation of the capacity constraints and apply the subgradient algorithm. The resulting Lagrangian subproblem is a variant of the well-known multi-facility Weber problem and can be solved using column generation and branch-and-price approach on a variant of the set covering formulation. Others are based on the approximating mixed-integer linear programming formulations obtained by exploiting norm properties and the alternate solution of the discrete location and transportation problems. The results of a detailed computational analysis are also reported.
New magnetic phenomena in TbNi2

Structural investigations at room temperature revealed that TbNi2 does not crystallize in the Laves phase structure, but shows a superstructure of the Laves phase with the space group F-43m. Susceptibility, specific heat, magnetostriction and magnetoresistance measurements on polycrystalline specimens showed an additional magnetic phase transition at TR = 14 K below the Curie temperature of TC = 36±0.2 K. In order to clarify the nature of this magnetic phase transition at 14 K, elastic neutron diffraction below and above TR and TC was performed. The analysis of these data showed that this transition at TR is due to the rotation of the Tb moments on three of the total of eight non-equivalent Tb sites in the rhombohedrally distorted unit cell in the magnetic ordered state. This rotation of these Tb moments is out of the [111] direction into a plane perpendicular to the space diagonal. The cause for this magnetic instability is due to an interplay of the regularly arranged vacancies in the superstructure and the crystal field level position which has been studied by inelastic neutron scattering.
A nonlinear diocotron mode

A perturbation procedure for the construction of a nonlinear diocotron mode of an infinite length cylindrical pure electron plasma column is presented. The plasma is modeled as a cold fluid executing E×B drift in a strong axial magnetic field. The mode has no axial variation and depends on θ (azimuthal angle) and t (time) through the combination θ−Ωt. Thus the potential and the density are stationary in the frame rotating with angular frequency Ω. In this frame, the potential satisfies the relation ∇2φ=F(φ) where F is a function of φ only. This equation is solved perturbatively to determine a nonlinear mode supported by a cylindrically symmetric equilibrium density profile. The properties of the mode depend only on the offset of the plasma column from the axis of the bounding conducting cylinder and the frequency of the mode is larger than the value predicted by linear theory by an amount roughly proportional to the square of the offset.
A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness

Speech-centric machine learning systems have revolutionized many leading domains ranging from transportation and healthcare to education and defense, profoundly changing how people live, work, and interact with each other. However, recent studies have demonstrated that many speech-centric ML systems may need to be considered more trustworthy for broader deployment. Specifically, concerns over privacy breaches, discriminating performance, and vulnerability to adversarial attacks have all been discovered in ML research fields. In order to address the above challenges and risks, a significant number of efforts have been made to ensure these ML systems are trustworthy, especially private, safe, and fair. In this paper, we conduct the first comprehensive survey on speech-centric trustworthy ML topics related to privacy, safety, and fairness. In addition to serving as a summary report for the research community, we point out several promising future research directions to inspire the researchers who wish to explore further in this area.
MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL

Conversational text-to-SQL is designed to translate multi-turn natural language questions into their corresponding SQL queries. Most advanced conversational text-to-SQL methods are incompatible with generative pre-trained language models (PLMs), such as T5. In this paper, we present a two-stage unified MultI-task Generation frAmework (MIGA) that leverages PLMs’ ability to tackle conversational text-to-SQL. In the pre-training stage, MIGA first decomposes the main task into several related sub-tasks and then unifies them into the same sequence-to-sequence (Seq2Seq) paradigm with task-specific natural language prompts to boost the main task from multi-task training. Later in the fine-tuning stage, we propose four SQL perturbations to alleviate the error propagation problem. MIGA tends to achieve state-of-the-art performance on two benchmarks (SparC and CoSQL). We also provide extensive analyses and discussions to shed light on some new perspectives for conversational text-to-SQL.
Norm of word embedding encodes information gain

Distributed representations of words encode lexical semantic information, but what type of information is encoded and how? Focusing on the skip-gram with negative-sampling method, we found that the squared norm of static word embedding encodes the information gain conveyed by the word; the information gain is defined by the Kullback-Leibler divergence of the co-occurrence distribution of the word to the unigram distribution. Our findings are explained by the theoretical framework of the exponential family of probability distributions and confirmed through precise experiments that remove spurious correlations arising from word frequency. This theory also extends to contextualized word embeddings in language models or any neural networks with the softmax output layer. We also demonstrate that both the KL divergence and the squared norm of embedding provide a useful metric of the informativeness of a word in tasks such as keyword extraction, proper-noun discrimination, and hypernym discrimination.
TopoImb: Toward Topology-level Imbalance in Learning from Graphs

Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes. Despite developments in learning from graph-structured data over the years, one obstacle persists: graph imbalance. Although several attempts have been made to target this problem, they are limited to considering only class-level imbalance. In this work, we argue that for graphs, the imbalance is likely to exist at the sub-class topology group level. Due to the flexibility of topology structures, graphs could be highly diverse, and learning a generalizable classification boundary would be difficult. Therefore, several majority topology groups may dominate the learning process, rendering others under-represented. To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation. {\method} can be used as a key component in GNN models to improve their performances under the data imbalance setting. Analyses on both topology-level imbalance and the proposed {\method} are provided theoretically, and we empirically verify its effectiveness with both node-level and graph-level classification as the target tasks.
Modeling and Performance Analysis of Single-Server Database Over Quasi-Static Rayleigh Fading Channel

Cloud database is the key technology in cloud computing. The effective and efficient service quality of the cloud database is inseparable from communication technology, just as improving communication quality will reduce the concurrency phenomenon in the ticketing system. In order to visually observe the impact of communication on the cloud database, we propose a Communication-Database (C-D) Model with a single-server database over the quasi-static Rayleigh fading channel, which consists of three parts: CLIENTS SOURCE, COMMUNICATION SYSTEM and DATABASE SYSTEM. This paper uses the queuing model, M/G/1//K, to model the whole system. The C-D Model is analyzed in two cases: nonlinearity and linearity, which correspond to some instances of SISO and MIMO. The program simulation results of average staying time, average number of transactions and other performance characteristics are basically consistent with theoretical results, which verify the validity of the C-D Model. The comparison of these experimental results also proves that poor communication quality does lead to the reduction in the quality of database service.
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust representation via diversifying the appearances of in-domain observations for better generalization. Limited by the specific observations of the environment, these methods ignore the possibility of exploring diverse real-world image datasets. In this paper, we investigate how a visual RL agent would benefit from the off-the-shelf visual representations. Surprisingly, we find that the early layers in an ImageNet pre-trained ResNet model could provide rather generalizable representations for visual RL. Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner. Extensive experiments are conducted on DMControl Generalization Benchmark, DMControl Manipulation Tasks, Drawer World, and CARLA to verify the effectiveness of PIE-G. Empirical evidence suggests PIE-G improves sample efficiency and significantly outperforms previous state-of-the-art methods in terms of generalization performance. In particular, PIE-G boasts a 55% generalization performance gain on average in the challenging video background setting. Project Page: https://sites.google. com/view/pie-g/home.