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There is a space of uncertainty in the modeling of vehicular dynamics of autonomous systems due to noise in sensor readings, environmental factors or modeling errors. We present Requiem, a software-only, blackbox approach that exploits this space in a stealthy manner causing target systems, e.g., unmanned aerial vehicles (UAVs), to significantly deviate from their mission parameters. Our system achieves this by modifying sensor values, all while avoiding detection by onboard anomaly detectors (hence, "stealthy"). The Requiem framework uses a combination of multiple deep learning models (that we refer to as "surrogates" and "spoofers") coupled with extensive, realistic simulations on a software-in-the-loop quadrotor UAV system. Requiem makes no assumptions about either the (types of) sensors or the onboard state estimation algorithm(s) -- it works so long as the latter is "learnable". We demonstrate the effectiveness of our system using various attacks across multiple missions as well as multiple sets of statistical analyses. We show that Requiem successfully exploits the modeling errors (i.e., causes significant deviations from planned mission parameters) while remaining stealthy (no detection even after {tens of meters of deviations}) and are generalizable (Requiem has potential to work across different attacks and sensor types).

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.

To enable the practical use of skyrmion-based devices, it is essential to achieve a balance between energy efficiency and thermal stability, while also ensuring reliable electrical detection against noise. Understanding how a skyrmion interacts with material disorder and external perturbations is thus essential. Here we investigate the electronic noise of a single skyrmion under the influence of thermal fluctuations and spin currents in a magnetic thin film. We detect the thermally induced noise with a 1/f signature in the strong pinning regime but a random telegraph noise in the intermediate pinning regime. Both the thermally dominated and current-induced telegraph-like signals are detected in the weak pinning regime. Our results provide a comprehensive electronic noise picture of a single skyrmion, demonstrating the potential of noise fluctuation as a valuable tool for characterizing the pinning condition of a skyrmion. These insights could also aid in the development of low-noise and reliable skyrmion-based devices.

Spin textures such as magnetic domain walls and skyrmions have the potential to revolutionize electronic devices by encoding information bits. Although recent advancements in ferromagnetic films have led to promising device prototypes, their widespread implementation has been hindered by the material-related drawbacks. Antiferromagnetic spin textures, however, offer a solution to many of these limitations, paving the way for faster, smaller, more energy-efficient, and more robust electronics. The functionality of synthetic antiferromagnets, comprised of two or more magnetic layers separated by spacers, may be easily manipulated by making use of different materials as well as interface engineering. In this Perspective article, we examine the challenges and opportunities presented by spin textures in synthetic antiferromagnets and propose possible directions and prospects for future research in this burgeoning field.

Stock market is often important as it represents the ownership claims on businesses. Without sufficient stocks, a company cannot perform well in finance. Predicting a stock market performance of a company is nearly hard because every time the prices of a company stock keeps changing and not constant. So, its complex to determine the stock data. But if the previous performance of a company in stock market is known, then we can track the data and provide predictions to stockholders in order to wisely take decisions on handling the stocks to a company. To handle this, many machine learning models have been invented but they didn't succeed due to many reasons like absence of advanced libraries, inaccuracy of model when made to train with real time data and much more. So, to track the patterns and the features of data, a CNN-LSTM Neural Network can be made. Recently, CNN is now used in Natural Language Processing (NLP) based applications, so by identifying the features from stock data and converting them into tensors, we can obtain the features and then send it to LSTM neural network to find the patterns and thereby predicting the stock market for given period of time. The accuracy of the CNN-LSTM NN model is found to be high even when allowed to train on real-time stock market data. This paper describes about the features of the custom CNN-LSTM model, experiments we made with the model (like training with stock market datasets, performance comparison with other models) and the end product we obtained at final stage., Comment: 8 pages, 9 figures, 5 tables

In orthogonal time frequency space (OTFS) modulation, Zak transform approach is a natural approach for converting information symbols multiplexed in the DD domain directly to time domain for transmission, and vice versa at the receiver. Past research on OTFS has primarily considered a two-step approach where DD domain symbols are first converted to time-frequency domain which are then converted to time domain for transmission, and vice versa at the receiver. The Zak transform approach can offer performance and complexity benefits compared to the two-step approach. This paper presents an early investigation on the bit error performance of OTFS realized using discrete Zak transform (DZT). We develop a compact DD domain input-output relation for DZT-OTFS using matrix decomposition that is valid for both integer and fractional delay-Dopplers. We analyze the bit error performance of DZT-OTFS using pairwise error probability analysis and simulations. Simulation results show that 1) both DZT-OTFS and two-step OTFS perform better than OFDM, and 2) DZT-OTFS achieves better performance compared to two-step OTFS over a wide range of Doppler spreads., Comment: ICC'2023. Copyright IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Although the development of spintronic devices has advanced significantly over the past decade with the use of ferromagnetic materials, the extensive implementation of such devices has been limited by the notable drawbacks of these materials. Antiferromagnets claim to resolve many of these shortcomings leading to faster, smaller, more energy-efficient, and more robust electronics. Antiferromagnets exhibit many desirable properties including zero net magnetization, imperviousness to external magnetic fields, intrinsic high-frequency dynamics with a characteristic precession frequency on the order of terahertz (THz), and the ability to serve as passive exchange-bias materials in multiple magnetoresistance (MR)- based devices. In this Perspective article, we will discuss the fundamental physics of magnetic structures in antiferromagnets and their interactions with external stimuli such as spin current, voltage, and magnons. A discussion on the challenges lying ahead is also provided along with an outlook of future research directions of these systems., Comment: Insufficient discussions on electrical manipulation and detection of antiferromagnetic order

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4x improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments., Comment: 17 pages, 15 figures, Accepted at AAAI 2021

The association process of Feshbach molecules is well described by a Landau-Zener (LZ) transition above the Fermi temperature, such that two-body physics dominates the dynamics. However, using $^6$Li atoms and the associated Feshbach resonance at $B_r=834.1$ G, we observe an enhancement of the atom-molecule coupling as the fermionic atoms reach degeneracy, demonstrating the importance of many-body coherence not captured by the conventional LZ model. In the experiment, we apply a linear association ramp ranging from adiabatic to non-equilibrium molecule association for various temperatures. We develop a theoretical model that explains the temperature dependence of the atom-molecule coupling. Furthermore, we characterize this dependence experimentally and extract the atom-molecule coupling coefficient as a function of temperature, finding qualitative agreement between our model and experimental results. In addition, we simulate the dynamics of molecular association during a nonlinear field ramp. We find that, in the non-equilibrium regime, molecular association efficiency can be enhanced by sweeping the magnetic field cubically with time. Accurate measurement of the atom-molecule coupling coefficient is important for both theoretical and experimental studies of molecular association and many-body collective dynamics., Comment: 18 pages, 6 figures

Ferroelectricity, the electrostatic counterpart to ferromagnetism, has long been thought to be incompatible with metallicity due to screening of electric dipoles and external electric fields by itinerant charges. Recent measurements, however, demonstrated signatures of ferroelectric switching in the electrical conductance of bilayers and trilayers of WTe$_2$, a semimetallic transition metal dichalcogenide with broken inversion symmetry. An especially promising aspect of this system is that the density of electrons and holes can be continuously tuned by an external gate voltage. This degree of freedom enables investigation of the interplay between ferroelectricity and free carriers, a previously unexplored regime. Here, we employ capacitive sensing in dual-gated mesoscopic devices of bilayer WTe$_2$ to directly measure the spontaneous polarization in the metallic state and quantify the effect of free carriers on the polarization in the conduction and valence bands, separately. We compare our results to a low-energy model for the electronic bands and identify the layer-polarized states that contribute to transport and polarization simultaneously. Bilayer WTe$_2$ is thus shown to be a canonical example of a ferroelectric metal and an ideal platform for exploring polar ordering, ferroelectric transitions, and applications in the presence of free carriers.

This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SINR) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus (TSWV) disease.

In this article, we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis-Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. The use of the package is demonstrated through examples involving both simulated and real data., Comment: 15 pages, 10 figures. This paper will be submitted to the R journal

Due to the globalization in the semiconductor supply chain, counterfeit dynamic random-access memory (DRAM) chips/modules have been spreading worldwide at an alarming rate. Deploying counterfeit DRAM modules into an electronic system can have severe consequences on security and reliability domains because of their sub-standard quality, poor performance, and shorter life span. Besides, studies suggest that a counterfeit DRAM can be more vulnerable to sophisticated attacks. However, detecting counterfeit DRAMs is very challenging because of their nature and ability to pass the initial testing. In this paper, we propose a technique to identify the DRAM origin (i.e., the origin of the manufacturer and the specification of individual DRAM) to detect and prevent counterfeit DRAM modules. A silicon evaluation shows that the proposed method reliably identifies off-the-shelf DRAM modules from three major manufacturers.