Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.
Large Vision-Language Models (VLMs) have achieved remarkable success in understanding complex real-world scenarios and supporting data-driven decision-making processes. However, VLMs exhibit significant vulnerability against adversarial examples, either text or image, which can lead to various adversarial outcomes, e.g., jailbreaking, hijacking, and hallucination, etc. In this work, we empirically and theoretically demonstrate that VLMs are particularly susceptible to image-based adversarial examples, where imperceptible perturbations can precisely manipulate each output token. To this end, we propose a novel attack called Vision-language model Manipulation Attack (VMA), which integrates first-order and second-order momentum optimization techniques with a differentiable transformation mechanism to effectively optimize the adversarial perturbation. Notably, VMA can be a double-edged sword: it can be leveraged to implement various attacks, such as jailbreaking, hijacking, privacy breaches, Denial-of-Service, and the generation of sponge examples, etc, while simultaneously enabling the injection of watermarks for copyright protection. Extensive empirical evaluations substantiate the efficacy and generalizability of VMA across diverse scenarios and datasets. Code is available at https://github.com/Trustworthy-AI-Group/VMA.
Saravanan Krishnan, Amith Singhee, Keerthi Narayan Raghunath
et al.
Industries such as banking, telecom, and airlines - o6en have large so6ware systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a so6ware system analysis tool that allows a subject ma=er expert (SME) or system architect to analyze a large so6ware system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent Entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the Entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.
Franca Corradini, Carlo Grigioni, Alessandro Antonucci
et al.
Safe road crossing by autonomous wheelchairs can be affected by several environmental factors such as adverse weather conditions influencing the accuracy of artificial vision. Previous studies have addressed experimental evaluation of multi-sensor information fusion to support road-crossing decisions in autonomous wheelchairs. In this study, we focus on the fine-tuning of tracking performance and on its experimental evaluation against outdoor environmental factors such as fog, rain, darkness, etc. It is rather intuitive that those factors can negatively affect the tracking performance; therefore our aim is to provide an approach to quantify their effects in the reference scenario, in order to detect conditions of unacceptable accuracy. In those cases, warnings can be issued and system can be possibly reconfigured to reduce the reputation of less accurate sensors, and thus improve overall safety. Critical situations can be detected by the main sensors or by additional sensors, e.g., light sensors, rain sensors, etc. Results have been achieved by using an available laboratory dataset and by applying appropriate software filters; they show that the approach can be adopted to evaluate video tracking and event detection robustness against outdoor environmental factors in relevant operational scenarios.
In this article, we establish the Hopf-Tsuji-Sullivan dichotomy for geodesic flows on certain manifolds with no conjugate points: either the geodesic flow is conservative and ergodic, or it is completely dissipative and non-ergodic. We also show several equivalent conditions to the conservativity, such the Poincaré series diverges at the critical exponent, the conical limit set has full Patterson-Sullivan measure, etc.
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc. and provide crucial, foundational materials for a diverse set of economically relevant industries: automobiles, machinery, construction, household appliances, chemicals, etc. Reheating furnaces within the manufacturing chain of FIs are energy-intensive. Accurate and real-time prediction of underlying temperatures in reheating furnaces has the potential to reduce the overall heating time, thereby controlling the energy consumption for achieving the Net-Zero goals in FIs. In this paper, we cast this prediction as a regression task and explore neural networks due to their inherent capability of being effective and efficient, given adequate data. However, due to the infeasibility of achieving good-quality real data in scenarios like reheating furnaces, classical Hottel's zone method based computational model has been used to generate data for model training. To further enhance the Out-Of-Distribution generalization capability of the trained model, we propose a Physics-Informed Neural Network (PINN) by incorporating prior physical knowledge using a set of novel Energy-Balance regularizers.
An orthodox formulation of quantum mechanics relies on a set of postulates in Hilbert space supplemented with rules to connect it with classical mechanics such as quantisation techniques, correspondence principle, etc. Here we deduce a qubit and its dynamics straightforwardly from a discrete deterministic dynamics and conservation of the classical collision entropy. No Hilbert space is required although it can be inferred from this approach if necessary.
We consider the vector nonlinear Schrödinger equation posed on the box with periodic boundary conditions, and derive the continuous resonant (CR) equation that describes the effective dynamics for large box size and small data size over very large time-scales. Moreover, we investigate various properties of the continuous resonant equations, including the Hamiltonian structure and the well-posedness, etc..
We have theoretically investigated the molecular orientation by a asymmetric potential created by the superposition of two-color laser fields. The time-dependent Schrodinger equation is solved numerically for different field parameters. We have shown how enhancement or suppression of the molecular orientation can be manipulated by the laser field parameters, such as time between laser pulses, the different intensity of the pulses, etc.
Magnetic nanoparticles (MNPs) have been extensively used as tiny heating sources in magnetic hyperthermia therapy, contrast agents in magnetic resonance imaging (MRI), tracers in magnetic particle imaging (MPI), carriers for drug/gene delivery, etc. There have emerged many magnetic nanoparticle/microbeads suppliers since the last decade, such as Ocean NanoTech, Nanoprobes, US Research Nanomaterials, Miltenyi Biotec, micromod Partikeltechnologie GmbH, and nanoComposix, etc. In this paper, we report the physical and magnetic characterizations on iron oxide nanoparticle products from Ocean NanoTech. Standard characterization tools such as Vibrating-Sample Magnetometer (VSM), X-Ray Diffraction (XRD), Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM), and Zeta Potential Analyzer are used to provide magnetic nanoparticle customers and researchers with an overview of these iron oxide nanoparticle products. In addition, the dynamic magnetic responses of these iron oxide nanoparticles in aqueous solutions are investigated under low and high frequency alternating magnetic fields, giving a standardized operating procedure for characterizing the MNPs from Ocean NanoTech, thereby yielding the best of magnetic nanoparticles for different applications.
In this paper we mainly discuss sharp lower and upper bounds for the length of longest consecutive switches in IID Bernoulli sequences. This work is an extension of results in Erdős and Révész (1975) for longest head-run and Hao et al. (2021) for longest consecutive switches in unbiased coin-tossing, and might be applied to reliability theory, biology, quality control, pattern recognition, finance, etc.
Nagendra Kumar, Gopi Ande, J. Shirish Kumar
et al.
A large amount of content is generated everyday in social media. One of the main goals of content creators is to spread their information to a large audience. There are many factors that affect information spread, such as posting time, location, type of information, number of social connections, etc. In this paper, we look at the problem of finding the best posting time(s) to get high content visibility. The posting time is derived taking other factors into account, such as location, type of information, etc. In this paper, we do our analysis over Facebook pages. We propose six posting schedules that can be used for individual pages or group of pages with similar audience reaction profile. We perform our experiment on a Facebook pages dataset containing 0.3 million posts, 10 million audience reactions. Our best posting schedule can lead to seven times more number of audience reactions compared to the average number of audience reactions that users would get without following any optimized posting schedule. We also present some interesting audience reaction patterns that we obtained through daily, weekly and monthly audience reaction analysis.
We improve further the 2015 version of abcdSAT by various heuristics such as at-least-one recently used strategy, learnt clause database approximation reduction etc. Based on the requirement of different tracks at the SAT Competition 2016, we develop three versions of abcdSAT: drup, inc and lim, which participate in the competition of main (agile), incremental library and no-limit track, respectively.
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan
et al.
Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their distance from the source task such that a correlation between the performance of tasks and their distance from the source task w.r.t. the proposed factors is observed.
Bidirectional reflectance of a surface is defined as the ratio of the scattered radiation at the detector to the incident irradiance as a function of geometry. The accurate knowledge of the bidirectional reflection function (BRF) of layers composed of discrete, randomly positioned scattering particles is very essential for many remote sensing, engineering, biophysical applications and in different areas of Astrophysics. The computations of BRF's for plane parallel particulate layers are usually reduced to solve the radiative transfer equation (RTE) by the existing techniques. In this work we present our laboratory data on bidirectional reflectance versus phase angle for two sample sizes of 0.3 and 1 $μm$ of Alumina for the He-Ne laser at 632.8 nm (red) and 543.5nm(green) wavelength. The nature of the phase curves of the asteroids depends on the parameters like- particle size, composition, porosity, roughness etc. In our present work we analyse the data which are being generated using single scattering phase function i.e. Mie theory considering particles to be compact sphere. The well known Hapke formula will be considered along with different particle phase function such as Mie and Henyey Greenstein etc to model the laboratory data obtained at the asteroid laboratory of Assam University.
We develop a new approach to study the nonlinear evolution in the large-scale structure of the Universe both in real space and in redshift space, extending the standard perturbation theory of gravitational instability. Infinite series of terms in standard Eulerian perturbation theory are resummed as a result of our starting from a Lagrangian description of perturbations. Delicate nonlinear effects on scales of the baryon acoustic oscillations are more accurately described by our method than the standard one. Our approach differs from other resummation techniques recently proposed, such as the renormalized perturbation theory, etc., in that we use simple techniques and thus resulting equations are undemanding to evaluate, and in that our approach is capable of quantifying the nonlinear effects in redshift space. The power spectrum and correlation function of our approach are in good agreement with numerical simulations in literature on scales of baryon acoustic oscillations. Especially, nonlinear effects on the baryon acoustic peak of the correlation function are accurately described both in real space and in redshift space. Our approach provides a unique opportunity to analytically investigate the nonlinear effects on baryon acoustic scales in observable redshift space, which is requisite in constraining the nature of dark energy, the curvature of the Universe, etc., by redshift surveys.