Miracle-dz/UniFAR-FTUs huggingface dataset:
A 9 Transistor SRAM Featuring Array-level XOR Parallelism with Secure Data Toggling Operation

Security and energy-efficiency are critical for computing applications in general and for edge applications in particular. Digital in-Memory Computing (IMC) in SRAM cells have widely been studied to accelerate inference tasks to maximize both throughput and energy efficiency for intelligent computing at the edge. XOR operations have been of particular interest due to their wide applicability in numerous applications that include binary neural networks and encryption. However, existing IMC circuits for XOR acceleration are limited to two rows in a memory array and extending the XOR parallelism to multiple rows in an SRAM array has remained elusive. Further, SRAM is prone to both data imprinting and data remanence security issues, which poses limitations on security . Based on commerical Globalfoundries 22nm mode, we are proposing a novel 9T SRAM cell such that multiple rows of data (entire array) can be XORed in a massively parallel single cycle fashion. The new cell also supports data-toggling within the SRAM cell efficiently to circumvent imprinting attacks and erase the SRAM value in case of remanence attack.
Spatial Encoding of BOLD fMRI Time Series for Categorizing Static Images Across Visual Datasets: A Pilot Study on Human Vision

Functional MRI (fMRI) is widely used to examine brain functionality by detecting alteration in oxygenated blood flow that arises with brain activity. In this study, complexity-specific image categorization across different visual datasets is performed using fMRI time series (TS) to understand differences in neuronal activities related to vision. Publicly available BOLD5000 dataset is used for this purpose, containing fMRI scans while viewing 5254 images of diverse categories, drawn from three standard computer vision datasets: COCO, ImageNet and SUN. To understand vision, it is important to study how brain functions while looking at different images. To achieve this, spatial encoding of fMRI BOLD TS has been performed that uses classical Gramian Angular Field (GAF) and Markov Transition Field (MTF) to obtain 2D BOLD TS, representing images of COCO, Imagenet and SUN. For classification, individual GAF and MTF features are fed into regular CNN. Subsequently, parallel CNN model is employed that uses combined 2D features for classifying images across COCO, Imagenet and SUN. The result of 2D CNN models is also compared with 1D LSTM and Bi-LSTM that utilizes raw fMRI BOLD signal for classification. It is seen that parallel CNN model outperforms other network models with an improvement of 7% for multiclass classification.
Spectral Theory for Non-linear Superconducting Microwave Systems: Extracting Relaxation Rates and Mode Hybridization

The accurate modeling of mode hybridization and calculation of radiative relaxation rates have been crucial to the design and optimization of superconducting quantum devices. In this work, we introduce a spectral theory for the electrohydrodynamics of superconductors that enables the extraction of the relaxation rates of excitations in a general three-dimensional distribution of superconducting bodies. Our approach addresses the long-standing problem of formulating a modal description of open systems that is both efficient and allows for second quantization of the radiative hybridized fields. This is achieved through the implementation of finite but transparent boundaries through which radiation can propagate into and out of the computational domain. The resulting spectral problem is defined within a coarse-grained formulation of the electrohydrodynamical equations that is suitable for the analysis of the non-equilibrium dynamics of multiscale superconducting quantum systems.
Western white pine

Western white pme has been called "the king ofpines," because of its impressive diameter, height, and form. Blister rust and mountain pine beetle will probably prevent white pine from attaining its historically impressive dimensions in the future. Its lightcolored wood has an average specific gravity ofo.36, and it is straight grained, easy to glue and work with tools, and fairly stable. Western white pine has been a preferred species for pattern stock and, at one time, was the most popular wood for kitchen matches. Today it is used for millwork, plywood, and pulp manufacture.
Cubulated hyperbolic groups admit Anosov representations

We prove that any hyperbolic group acting properly discontinuously and cocompactly on a $\mathrm{CAT}(0)$ cube complex admits a projective Anosov representation into $\mathrm{SL}(d, \mathbb{R})$ for some $d$. More specifically, we show that if $\Gamma$ is a hyperbolic quasiconvex subgroup of a right-angled Coxeter group $C$, then a generic representation of $C$ by reflections restricts to a projective Anosov representation of $\Gamma$.
Evolution of chirality in a multiphoton pair production process

Recent years, multiphoton pair production has become one of the most promising approaches to investigate the Schwinger effect. However, the production and evolution of chirality, a key topic in the study of this effect, has not been thoroughly considered in the context of multiphoton pair production. In this work, as the first step of filling this gap, we used the Dirac-Heisenberg-Wigner formalism to study the production and evolution of chirality in vacuum under the excitation of the spatially homogeneous electric and magnetic fields $\mathbf{E}(t)$ and $\mathbf{B}(t)$ that satisfy $\mathbf{E}(t)\parallel\mathbf{B}(t)$ and are only nonzero in a short time span $0<t<\tau$, which serve as a simplified model of the laser beams in multiphoton pair production experiments. Based on analytical calculation, we discovered that, after the external fields vanish, an oscillation of pseudoscalar condensate occurs in the system, which leads to the suppression of the chirality of the produced fermion pairs; at the same time, it introduces a special fermion energy $\epsilon_p=\sqrt{3} m$ at which the chiral charge distribution of the fermions maximizes. This novel phenomenon could help us identify different types of products in future multiphoton pair production experiments.
T2IW: Joint Text to Image & Watermark Generation

Recent developments in text-conditioned image generative models have revolutionized the production of realistic results. Unfortunately, this has also led to an increase in privacy violations and the spread of false information, which requires the need for traceability, privacy protection, and other security measures. However, existing text-to-image paradigms lack the technical capabilities to link traceable messages with image generation. In this study, we introduce a novel task for the joint generation of text to image and watermark (T2IW). This T2IW scheme ensures minimal damage to image quality when generating a compound image by forcing the semantic feature and the watermark signal to be compatible in pixels. Additionally, by utilizing principles from Shannon information theory and non-cooperative game theory, we are able to separate the revealed image and the revealed watermark from the compound image. Furthermore, we strengthen the watermark robustness of our approach by subjecting the compound image to various post-processing attacks, with minimal pixel distortion observed in the revealed watermark. Extensive experiments have demonstrated remarkable achievements in image quality, watermark invisibility, and watermark robustness, supported by our proposed set of evaluation metrics.
Spinor-dominated magnetoresistance driven by the topological phase transition in $\beta $-Ag$_2$Se

A topological insulator is a quantum material which possesses conducting surfaces and an insulating bulk. Despite extensive researches on the properties of Dirac surface states, the characteristics of bulk states have remained largely unexplored. Here we report the observation of spinor-dominated magnetoresistance anomalies in the topological insulator $\beta $-Ag$_2$Se, induced by a magnetic-field-driven band topological phase transition. These anomalies are caused by intrinsic orthogonality in the wave-function spinors of the last Landau bands of the bulk states, in which backscattering is strictly forbidden during a band topological phase transition. This new type of longitudinal magnetoresistance, purely controlled by the wave-function spinors of the last Landau bands, highlights a unique signature of electrical transport around the band topological phase transition. With further reducing the quantum limit and gap size in $\beta $-Ag$_2$Se, our results may also suggest possible device applications based on this spinor-dominated mechanism and signify a rare case where topology enters the realm of magnetoresistance control.
Learning continuous-valued treatment effects through representation balancing

Estimating the effects of treatments with an associated dose on an instance's outcome, the"dose response", is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such effects, also known as continuous-valued treatment effects, are typically estimated from observational data, which may be subject to dose selection bias. This means that the allocation of doses depends on pre-treatment covariates. Previous studies have shown that conventional machine learning approaches fail to learn accurate individual estimates of dose responses under the presence of dose selection bias. In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data. CBRNet adopts the Neyman-Rubin potential outcome framework and extends the concept of balanced representation learning for overcoming selection bias to continuous-valued treatments. Our work is the first to apply representation balancing in a continuous-valued treatment setting. We evaluate our method on a newly proposed benchmark. Our experiments demonstrate CBRNet's ability to accurately learn treatment effects under selection bias and competitive performance with respect to other state-of-the-art methods.
XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement

The application of machine learning models in quantum information theory has surged in recent years, driven by the recognition of entanglement and quantum states, which are the essence of this field. However, most of these studies rely on existing prefabricated models, leading to inadequate accuracy. This work aims to bridge this gap by introducing a custom deep convolutional neural network (CNN) model explicitly tailored to quantum systems. Our proposed CNN model, the so-called XpookyNet, effectively overcomes the challenge of handling complex numbers data inherent to quantum systems and achieves an accuracy of 98.5%. Developing this custom model enhances our ability to analyze and understand quantum states. However, first and foremost, quantum states should be classified more precisely to examine fully and partially entangled states, which is one of the cases we are currently studying. As machine learning and quantum information theory are integrated into quantum systems analysis, various perspectives, and approaches emerge, paving the way for innovative insights and breakthroughs in this field.
An AI Agent for Fully Automated Multi-omic Analyses

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the bioinformatics analysis continues to grow. In response to this need, we introduce Automated Bioinformatics Analysis (AutoBA), an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA’s robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome/exome sequencing (WGS/WES), chromatin immunoprecipitation assays with sequencing (ChIP-seq), RNA sequencing (RNA-seq), single-cell RNA-seq, spatial transcriptomics and so on. AutoBA’s unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.
Improved theoretical guarantee for rank aggregation via spectral method

Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems, and other web applications. As it is generally NP-hard to find a global ranking that minimizes the mismatch (known as the Kemeny optimization), we focus on the Erd\"os-R\'enyi outliers (ERO) model for this ranking problem. Here, each pairwise comparison is a corrupted copy of the true score difference. We investigate spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper $\ell_{\infty}$-norm perturbation bound of the eigenvectors and also derive an error bound on the maximum displacement for each item, with only $\Omega(n\log n)$ samples. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings.
Kidney abnormality segmentation in thorax-abdomen CT scans

In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalities such as cysts, lesions, masses, metastases, and primary tumors. Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities. We began by implementing our own version of the original 3D U-Net network and incorporated four additional components: an end-to-end multi-resolution approach, a set of task-specific data augmentations, a modified loss function using top-$k$, and spatial dropout. Furthermore, we devised a tailored post-processing strategy. Ablation studies demonstrated that each of the four modifications enhanced kidney abnormality segmentation performance, while three out of four improved kidney parenchyma segmentation. Subsequently, we trained the nnUNet framework on our dataset. By ensembling the optimized 3D U-Net and the nnUNet with our specialized post-processing, we achieved marginally superior results. Our best-performing model attained Dice scores of 0.965 and 0.947 for segmenting kidney parenchyma in two test sets (20 scans without abnormalities and 30 with abnormalities), outperforming an independent human observer who scored 0.944 and 0.925, respectively. In segmenting kidney abnormalities within the 30 test scans containing them, the top-performing method achieved a Dice score of 0.585, while an independent second human observer reached a score of 0.664, suggesting potential for further improvement in computerized methods. All training data is available to the research community under a CC-BY 4.0 license on https://doi.org/10.5281/zenodo.8014289
Spatial data fusion adjusting for preferential sampling using INLA and SPDE

Spatially misaligned data can be fused by using a Bayesian melding model that assumes that underlying all observations there is a spatially continuous Gaussian random field process. This model can be used, for example, to predict air pollution levels by combining point data from monitoring stations and areal data from satellite imagery. However, if the data presents preferential sampling, that is, if the observed point locations are not independent of the underlying spatial process, the inference obtained from models that ignore such a dependence structure might not be valid. In this paper, we present a Bayesian spatial model for the fusion of point and areal data that takes into account preferential sampling. The model combines the Bayesian melding specification and a model for the stochastically dependent sampling and underlying spatial processes. Fast Bayesian inference is performed using the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches. The performance of the model is assessed using simulated data in a range of scenarios and sampling strategies that can appear in real settings. The model is also applied to predict air pollution in the USA.
Systematic Mapping of Monolithic Applications to Microservices Architecture

The aim of this paper to provide the solution microservices architecture as a popular alternative to monolithic architecture. It discusses the advantages of microservices and the challenges that organizations face when transitioning from a monolithic system. It presents a case study of a financial application and proposed techniques for identifying microservices on monolithic systems using domain-driven development concepts. In recent years, microservices architecture has emerged as a new architectural style in the software development industry. As legacy monolithic software becomes too large to manage, many large corporations are considering converting their traditional monolithic systems into small-scale, self-contained microservices. However, migrating from monolithic to microservices architecture is a difficult and challenging task. It presents a comparison of the two architectural styles and discusses the difficulties that led companies to switch to microservices. The study's findings suggest that the proposed technique can improve work performance and establish clear models, but it may not be useful for systems with lower levels of complexity. This research paper has practical implications for software architects and developers who are considering migrating from monolithic to microservices architecture.
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks

We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement). It even exhibits the ability to handle unseen tasks and outperforms prior methods on novel datasets. This represents a significant step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.
AnthroNet: Conditional Generation of Humans via Anthropometrics

We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses. The proposed model enables direct modeling of specific human identities through a deep generative architecture, which can produce humans in any arbitrary pose. It is the first of its kind to have been trained end-to-end using only synthetically generated data, which not only provides highly accurate human mesh representations but also allows for precise anthropometry of the body. Moreover, using a highly diverse animation library, we articulated our synthetic humans' body and hands to maximize the diversity of the learnable priors for model training. Our model was trained on a dataset of $100k$ procedurally-generated posed human meshes and their corresponding anthropometric measurements. Our synthetic data generator can be used to generate millions of unique human identities and poses for non-commercial academic research purposes.
Active shooter detection and robust tracking utilizing supplemental synthetic data

The increasing concern surrounding gun violence in the United States has led to a focus on developing systems to improve public safety. One approach to developing such a system is to detect and track shooters, which would help prevent or mitigate the impact of violent incidents. In this paper, we proposed detecting shooters as a whole, rather than just guns, which would allow for improved tracking robustness, as obscuring the gun would no longer cause the system to lose sight of the threat. However, publicly available data on shooters is much more limited and challenging to create than a gun dataset alone. Therefore, we explore the use of domain randomization and transfer learning to improve the effectiveness of training with synthetic data obtained from Unreal Engine environments. This enables the model to be trained on a wider range of data, increasing its ability to generalize to different situations. Using these techniques with YOLOv8 and Deep OC-SORT, we implemented an initial version of a shooter tracking system capable of running on edge hardware, including both a Raspberry Pi and a Jetson Nano.
Electric analog of magnons in order-disorder ferroelectrics

We analyze the ``ferron"excitations in order-disorder ferroelectrics by a microscopic pseudo-spin model. We demonstrate that analogous to magnons, the quanta of spin waves in magnetic materials, ferrons carry both static and oscillating electric dipole moments, exhibit a Stark effect, and may be parametrically excited by THz radiation. The anti-crossing gap of the ferron-photon hybrid depends strongly on propagation direction and an applied static electric field. We predict ferron diffusion lengths that can reach centimeters, which implies efficient transport of electric polarization by temperature gradients. These properties suggest that ferroelectric materials may be useful for information technology beyond data storage applications.
FLASH: a Fast joint model for Longitudinal And Survival data in High dimension

This paper introduces a prognostic method called FLASH that addresses the problem of joint modelling of longitudinal data and censored durations when a large number of both longitudinal and time-independent features are available. In the literature, standard joint models are either of the shared random effect or joint latent class type. Combining ideas from both worlds and using appropriate regularisation techniques, we define a new model with the ability to automatically identify significant prognostic longitudinal features in a high-dimensional context, which is of increasing importance in many areas such as personalised medicine or churn prediction. We develop an estimation methodology based on the EM algorithm and provide an efficient implementation. The statistical performance of the method is demonstrated both in extensive Monte Carlo simulation studies and on publicly available real-world datasets. Our method significantly outperforms the state-of-the-art joint models in predicting the latent class membership probability in terms of the C-index in a so-called ``real-time'' prediction setting, with a computational speed that is orders of magnitude faster than competing methods. In addition, our model automatically identifies significant features that are relevant from a practical perspective, making it interpretable.