The appearance of singularities in the function of interest constitutes a fundamental challenge in scientific computing. It can significantly undermine the effectiveness of numerical schemes for function approximation, numerical integration, and the solution of partial differential equations (PDEs), etc. The problem becomes more sophisticated if the location of the singularity is unknown, which is often encountered in solving PDEs. Detecting the singularity is therefore critical for developing efficient adaptive methods to reduce computational costs in various applications. In this paper, we consider singularity detection in a purely data-driven setting. Namely, the input only contains given data, such as the vertex set from a mesh. To overcome the limitation of the raw unlabeled data, we propose a self-supervised learning (SSL) framework for estimating the location of the singularity. A key component is a filtering procedure as the pretext task in SSL, where two filtering methods are presented, based on $k$ nearest neighbors and kernel density estimation, respectively. We provide numerical examples to illustrate the potential pathological or inaccurate results due to the use of raw data without filtering. Various experiments are presented to demonstrate the ability of the proposed approach to deal with input perturbation, label corruption, and different kinds of singularities such interior circle, boundary layer, concentric semicircles, etc.
The Bianchi type III (BIII) metric is a useful geometry to study cosmic anisotropies. It includes an extra exponential term multiplied by a directional scale factor and recasts the cosmological model as a dynamical system to provide various significant information regarding the evolution, stability of the system, etc. In this study, we have constructed a dynamical system for the BIII metric using $f(R,T)$ gravity theory and performed fixed point analysis in three different $f(R,T)$ models. Here, we have found that the first two models, i.e., $f(R,T) = αR + βf(T)$ and $f(R,T) = R + 2 f(T)$ are agreed with standard $Λ$CDM cosmology but the third one, i.e., $f(R,T) = (ζ+ η\, τ\, T)R$ has the issue of unbounded energy density. Thus, we can remark that some $f(R,T)$ models may not be suitable for studying the evolution of the Universe with an anisotropic background, like using BIII metric, etc. However, all three models agree with the heteroclinic path of radiation-dominated, matter-dominated, and dark energy-dominated phases of the Universe as predicted by standard cosmology.
LLM app stores have seen rapid growth, leading to the proliferation of numerous custom LLM apps. However, this expansion raises security concerns. In this study, we propose a three-layer concern framework to identify the potential security risks of LLM apps, i.e., LLM apps with abusive potential, LLM apps with malicious intent, and LLM apps with exploitable vulnerabilities. Over five months, we collected 786,036 LLM apps from six major app stores: GPT Store, FlowGPT, Poe, Coze, Cici, and Character.AI. Our research integrates static and dynamic analysis, the development of a large-scale toxic word dictionary (i.e., ToxicDict) comprising over 31,783 entries, and automated monitoring tools to identify and mitigate threats. We uncovered that 15,146 apps had misleading descriptions, 1,366 collected sensitive personal information against their privacy policies, and 15,996 generated harmful content such as hate speech, self-harm, extremism, etc. Additionally, we evaluated the potential for LLM apps to facilitate malicious activities, finding that 616 apps could be used for malware generation, phishing, etc. Our findings highlight the urgent need for robust regulatory frameworks and enhanced enforcement mechanisms.
Location fingerprint (LF) has been widely applied in indoor positioning. However, the existing studies on LF mostly focus on the fingerprint of WiFi below 6 GHz, bluetooth, ultra wideband (UWB), etc. The LF with millimeter-wave (mmWave) was rarely addressed. Since mmWave has the characteristics of narrow beam, fast signal attenuation and wide bandwidth, etc., the positioning error can be reduced. In this paper, an LF positioning method with mmWave is proposed, which is named as DoA-LF. Besides received signal strength indicator (RSSI) of access points (APs), the fingerprint database contains direction of arrival (DoA) information of APs, which is obtained via DoA estimation. Then the impact of the number of APs, the interval of reference points (RPs), the channel model of mmWave and the error of DoA estimation algorithm on positioning error is analyzed with Cramer-Rao lower bound (CRLB). Finally, the proposed DoA-LF algorithm with mmWave is verified through simulations. The simulation results have proved that mmWave can reduce the positioning error due to the fact that mmWave has larger path loss exponent and smaller variance of shadow fading compared with low frequency signals. Besides, accurate DoA estimation can reduce the positioning error.
Dynamical spin-structure factor (DSF) contains fingerprint information of collective excitations in interacting quantum spin systems. In solid state experiments, DSF can be measured through neutron scatterings. However, it is in general challenging to compute the spectral properties accurately via many-body simulations. Currently, quantum simulation and computation constitute a thriving research field, which are believed to provide a very promising platform for simulating quantum many-body systems. In this work, we establish a link between the many-body dynamics and quantum simulations by studying the non-equilibrium DSF (nDSF) measured on direct product states, which are accessible in contemporary quantum simulators with Rydberg atoms, superconducting qubits, etc. Based on the many-body calculations of transverse field Ising chains, we find the nDSF can be used to sensitively probe the multi-spinon continua associated with the two-spinon creation and the spinon-antispinon process, etc. Moreover, we further demonstrate that the low-energy spinons can be confined -- forming spinon bound states -- under a finite longitudinal field. Our results pave the way of quantum simulation and manipulation of fractional excitations in highly-entangled quantum many-body systems.
Michael K. -H. Kiessling, Eric Ling, A. Shadi Tahvildar-Zadeh
In this short paper, we review the Dirac equation on the zero-gravity Kerr-Newman spacetime. Our main objective is to provide a correspondence between the classification of the bound states for the zGKN spectrum and the usual hydrogenic states $1s_{1/2}$, $2s_{1/2}$, etc. of the Hydrogen atom.
Legal predictive models are of enormous interest and value to legal community. The stakeholders, specially, the judges and attorneys can take the best advantages of these models to predict the case outcomes to further augment their future course of actions, for example speeding up the decision making, support the arguments, strengthening the defense, etc. However, accurately predicting the legal decisions and case outcomes is an arduous process, which involves several complex steps -- finding suitable bulk case documents, data extracting, cleansing and engineering, etc. Additionally, the legal complexity further adds to its intricacies. In this paper, we introduce our newly developed ML-enabled legal prediction model and its operational prototype, eLegPredict; which successfully predicts the Indian supreme court decisions. The eLegPredict is trained and tested over 3072 supreme court cases and has achieved 76% accuracy (F1-score). The eLegPredict is equipped with a mechanism to aid end users, where as soon as a document with new case description is dropped into a designated directory, the system quickly reads through its content and generates prediction. To our best understanding, eLegPredict is the first legal prediction model to predict Indian supreme court decisions.
This work presents a network-based data-driven study of the combination of factors that contribute to success in mountaineering. It simultaneously examines the effects of individual factors such as age, gender, experience etc., as well as expedition-wide factors such as number of camps, ratio of sherpas to paying climbers etc. Specifically, it combines the two perspectives into a multiscale network, i.e., a network of individual climber features within each expedition at the finer scale, and an expedition similarity network on the coarser scale. The latter is represented as a multiplex network where layers encode different factors. The analysis reveals that chances of failure to summit due to fatigue, altitude or logistical problems, drastically reduce when climbing with repeat partners, especially for experienced climbers. Additionally, node-centrality indicates that individual traits of youth and oxygen use are the strongest drivers of success. Further, the learning of network projections enables computation of correlations between intra-expedition networks and corresponding expedition success rates. Of expedition-wide factors, the expedition size and length layers are found to be strongly correlated with success rate. Lastly, community detection on the expedition-similarity network reveals distinct communities where a difference in success rates naturally emerges amongst the communities.
Arkadipta De, Venkatesh E, Kaushal Kumar Maurya
et al.
Due to the wide adoption of social media platforms like Facebook, Twitter, etc., there is an emerging need of detecting online posts that can go against the community acceptance standards. The hostility detection task has been well explored for resource-rich languages like English, but is unexplored for resource-constrained languages like Hindidue to the unavailability of large suitable data. We view this hostility detection as a multi-label multi-class classification problem. We propose an effective neural network-based technique for hostility detection in Hindi posts. We leverage pre-trained multilingual Bidirectional Encoder Representations of Transformer (mBERT) to obtain the contextual representations of Hindi posts. We have performed extensive experiments including different pre-processing techniques, pre-trained models, neural architectures, hybrid strategies, etc. Our best performing neural classifier model includes One-vs-the-Rest approach where we obtained 92.60%, 81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive, and defamation labels respectively. The proposed model outperformed the existing baseline models and emerged as the state-of-the-art model for detecting hostility in the Hindi posts.
Asadullah Hill Galib, Nadia Nahar, B M Mainul Hossain
Analysis of child mortality is crucial as it pertains to the policy and programs of a country. The early assessment of patterns and trends in causes of child mortality help decision-makers assess needs, prioritize interventions, and monitor progress. Post-birth factors of the child, such as real-time clinical data, health data of the child, etc. are frequently used in child mortality studies. However, in the early assessment of child mortality, pre-birth factors would be more practical and beneficial than the post-birth factors. This study aims at incorporating pre-birth factors, such as birth history, maternal history, reproduction history, socioeconomic condition, etc. for classifying child mortality. To assess the relative importance of the features, Information Gain (IG) attribute evaluator is employed. For classifying child mortality, four machine learning algorithms are evaluated. Results show that the proposed approach achieved an AUC score of 0.947 in classifying child mortality which outperformed the clinical standards. In terms of accuracy, precision, recall, and f-1 score, the results are also notable and uniform. In developing countries like Bangladesh, the early assessment of child mortality using pre-birth factors would be effective and feasible as it avoids the uncertainty of the post-birth factors.
This is a set of lecture notes for an introductory course (advanced undergaduates or the 1st graduate course) on foundations of supervised machine learning (in Portuguese). The topics include: the geometry of the Hamming cube, concentration of measure, shattering and VC dimension, Glivenko-Cantelli classes, PAC learnability, universal consistency and the k-NN classifier in metric spaces, dimensionality reduction, universal approximation, sample compression. There are appendices on metric and normed spaces, measure theory, etc., making the notes self-contained. Este é um conjunto de notas de aula para um curso introdutório (curso de graduação avançado ou o 1o curso de pós) sobre fundamentos da aprendizagem de máquina supervisionada (em Português). Os tópicos incluem: a geometria do cubo de Hamming, concentração de medida, fragmentação e dimensão de Vapnik-Chervonenkis, classes de Glivenko-Cantelli, aprendizabilidade PAC, consistência universal e o classificador k-NN em espaços métricos, redução de dimensionalidade, aproximação universal, compressão amostral. Há apêndices sobre espaços métricos e normados, teoria de medida, etc., tornando as notas autosuficientes.
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs outstanding capability to learn the input features with deep layers of neuron structures and iterative training process. However, these learned features are hard to identify and interpret from a human vision perspective, causing a lack of understanding of the CNNs internal working mechanism. To improve the CNN interpretability, the CNN visualization is well utilized as a qualitative analysis method, which translates the internal features into visually perceptible patterns. And many CNN visualization works have been proposed in the literature to interpret the CNN in perspectives of network structure, operation, and semantic concept. In this paper, we expect to provide a comprehensive survey of several representative CNN visualization methods, including Activation Maximization, Network Inversion, Deconvolutional Neural Networks (DeconvNet), and Network Dissection based visualization. These methods are presented in terms of motivations, algorithms, and experiment results. Based on these visualization methods, we also discuss their practical applications to demonstrate the significance of the CNN interpretability in areas of network design, optimization, security enhancement, etc.
Given the size of modern cities in the urbanising age, it is beyond the perceptual capacity of most people to develop a good knowledge about the beauty and ugliness of the city at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions like 'where are the worst-looking places in the city that regeneration should give first consideration', or 'in the fast urbanising cities, how is the city appearance changing', etc. To address this issue, we here present a computer vision method for the large-scale and automatic evaluation of the urban visual environment, by leveraging state-of-the-art machine learning techniques and the wide-coverage street view images. From the various factors that are at work, we choose two key features, the visual quality of street facade and the continuity of street wall, as the starting point of this line of analysis. In order to test the validity of this method, we further compare the machine ratings with ratings collected on site from 752 passers-by on fifty-six locations. We show that the machine learning model can produce a good estimation of people's real visual experience, and it holds much potential for various tasks in terms of urban design evaluation, culture identification, etc.
M. T Gopalakrishna, M. Ravishankar, D. R Rameshbabu
Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.
Graph partition is a fundamental problem of parallel computing for big graph data. Many graph partition algorithms have been proposed to solve the problem in various applications, such as matrix computations and PageRank, etc., but none has pay attention to random walks. Random walks is a widely used method to explore graph structure in lots of fields. The challenges of graph partition for random walks include the large number of times of communication between partitions, lots of replications of the vertices, unbalanced partition, etc. In this paper, we propose a feasible graph partition framework for random walks implemented by parallel computing in big graph. The framework is based on two optimization functions to reduce the bandwidth, memory and storage cost in the condition that the load balance is guaranteed. In this framework, several greedy graph partition algorithms are proposed. We also propose five metrics from different perspectives to evaluate the performance of these algorithms. By running the algorithms on the big graph data set of real world, the experimental results show that these algorithms in the framework are capable of solving the problem of graph partition for random walks for different needs, e.g. the best result is improved more than 70 times in reducing the times of communication.
The arrangement of things in n-dimensional space is specified as Spatial. Spatial data consists of values that denote the location and shape of objects and areas on the earths surface. Spatial information includes facts such as location of features, the relationship of geographic features and measurements of geographic features. The spatial cognition is a primal area of study in various other fields such as Robotics, Psychology, Geosciences, Geography, Political Sciences, Geographic Economy, Environmental, Mining and Petroleum Engineering, Natural Resources, Epidemiology, Demography etc., Any text document which contains physical location specifications such as place names, geographic coordinates, landmarks, country names etc., are supposed to contain the spatial information. The spatial information may also be represented using vague or fuzzy descriptions involving linguistic terms such as near to, far from, to the east of, very close. Given a query involving events, the aim of this ongoing research work is to extract the relevant information from multiple text documents, resolve the uncertainty and vagueness and translate them in to locations in a map. The input to the system would be a text Corpus and a Spatial Query event. The output of the system is a map showing the most possible, disambiguated location of the event queried. The author proposes Fuzzy Logic Techniques for resolving the uncertainty in the spatial expressions.
This volume contains the proceedings of the First International Workshop of Formal Techniques for Safety-Critical Systems (FTSCS 2012), held in Kyoto on November 12, 2012, as a satellite event of the ICFEM conference. The aim of this workshop is to bring together researchers and engineers interested in the application of (semi-)formal methods to improve the quality of safety-critical computer systems. FTSCS is particularly interested in industrial applications of formal methods. Topics include: - the use of formal methods for safety-critical and QoS-critical systems, including avionics, automotive, and medical systems; - methods, techniques and tools to support automated analysis, certification, debugging, etc.; - analysis methods that address the limitations of formal methods in industry; - formal analysis support for modeling languages used in industry, such as AADL, Ptolemy, SysML, SCADE, Modelica, etc.; and - code generation from validated models. The workshop received 25 submissions; 21 of these were regular papers and 4 were tool/work-in-progress/position papers. Each submission was reviewed by three referees; based on the reviews and extensive discussions, the program committee selected nine regular papers, which are included in this volume. Our program also included an invited talk by Ralf Huuck.
Interpretations of violence in Christopher Marlowe's plays have emphasized biographical, literary, and philosophical roots over social and historical conditions. If, however, we glance out Marlowe's window at contemporary rituals, we can enlarge these views of staged violence. His numerous references to official methods of persecution—from boiling to pressing, from branding to beheading—are not only projections of dramatic character but also revisions of corresponding Tudor social practices: public executions and their kin, torture. Although these entertainments provide Marlowe with ready-made elements for dramatizing tragedies of will, he uses those elements to turn theatricality against itself and to expose the fraudulent core of such exhibitions, even as he acknowledges their thematic power. Exaggerating the profound ambiguity of artifice, Marlowe undermines the moralizing that accompanies spectacles of punishment and transforms a theater of pain into a drama of subversion.