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FPGAs are now ubiquitous in cloud computing infrastructures and reconfigurable system-on-chip, particularly for AI acceleration. Major cloud service providers such as Amazon and Microsoft are increasingly incorporating FPGAs for specialized compute-intensive tasks within their data centers. The availability of FPGAs in cloud data centers has opened up new opportunities for users to improve application performance by implementing customizable hardware accelerators directly on the FPGA fabric. However, the virtualization and sharing of FPGA resources among multiple users open up new security risks and threats. We present a novel fault attack methodology capable of causing persistent fault injections in partial bitstreams during the process of FPGA reconfiguration. This attack leverages power-wasters and is timed to inject faults into bitstreams as they are being loaded onto the FPGA through the reconfiguration manager, without needing to remain active throughout the entire reconfiguration process. Our experiments, conducted on a Pynq FPGA setup, demonstrate the feasibility of this attack on various partial application bitstreams, such as a neural network accelerator unit and a signal processing accelerator unit., Comment: Accepted for presentation in the 2024 IEEE Asian Test Symposium (ATS)

Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. However, there has been limited work exploiting the shared structure in neural activity during similar tasks for learning latent dynamics from neural recordings. Existing approaches are designed to infer dynamics from a single dataset and cannot be readily adapted to account for statistical heterogeneities across recordings. In this work, we hypothesize that similar tasks admit a corresponding family of related solutions and propose a novel approach for meta-learning this solution space from task-related neural activity of trained animals. Specifically, we capture the variabilities across recordings on a low-dimensional manifold which concisely parametrizes this family of dynamics, thereby facilitating rapid learning of latent dynamics given new recordings. We demonstrate the efficacy of our approach on few-shot reconstruction and forecasting of synthetic dynamical systems, and neural recordings from the motor cortex during different arm reaching tasks.

Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG)., Comment: 10 pages, 7 figures

Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders., Comment: This article is accepted for publication in the 2024 European Signal Processing Conference (EUSIPCO)

A periodic surface is one that is invariant by a 2D lattice of translations. Deformation modes that stretch the lattice without stretching the surface are effective membrane modes. Deformation modes that bend the lattice without stretching the surface are effective bending modes. For periodic, piecewise smooth, simply connected surfaces, it is shown that the effective membrane modes are, in a sense, orthogonal to effective bending modes. This means that if a surface gains a membrane mode, it loses a bending mode, and conversely, in such a way that the total number of modes, membrane and bending combined, can never exceed 3. Various examples, inspired from curved-crease origami tessellations, illustrate the results., Comment: 17 pages, 4 figures

Single nitrogen vacancy (NV) centers in diamond have been used extensively for high-sensitivity nanoscale sensing, but conventional approaches use confocal microscopy to measure individual centers sequentially, limiting throughput and access to non-local physical properties. Here we design and implement a multiplexed NV sensing platform that allows us to read out many single NV centers simultaneously using a low-noise camera. Using this platform, we coherently manipulate and read out the spin states of hundreds of individual NV centers in parallel, achieving comparable magnetic field sensitivity to confocal measurements. We also implement a parallelized version of spin-to-charge-conversion readout for low NV center spin state readout noise and use it to demonstrate multiplexed covariance magnetometry, in which we measure six two-point magnetic field correlators from four NV centers simultaneously. The number of correlators we can measure is limited only by the available laser power, opening the door to massively multiplexed covariance magnetometry. Our platform significantly increases the throughput and broadens the applications of nanoscale sensing using diamond quantum sensors.

We present a comprehensive framework for predicting the effects of perturbations in single cells, designed to standardize benchmarking in this rapidly evolving field. Our framework, PerturBench, includes a user-friendly platform, diverse datasets, metrics for fair model comparison, and detailed performance analysis. Extensive evaluations of published and baseline models reveal limitations like mode or posterior collapse, and underscore the importance of rank metrics that assess the ordering of perturbations alongside traditional measures like RMSE. Our findings show that simple models can outperform more complex approaches. This benchmarking exercise sets new standards for model evaluation, supports robust model development, and advances the potential of these models to use high-throughput and high-content genetic and chemical screens for disease target discovery., Comment: 9 pages plus 19 pages supplementary material. Code is available at https://github.com/altoslabs/perturbench

This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.

Dialectal Arabic is the primary spoken language used by native Arabic speakers in daily communication. The rise of social media platforms has notably expanded its use as a written language. However, Arabic dialects do not have standard orthographies. This, combined with the inherent noise in user-generated content on social media, presents a major challenge to NLP applications dealing with Dialectal Arabic. In this paper, we explore and report on the task of CODAfication, which aims to normalize Dialectal Arabic into the Conventional Orthography for Dialectal Arabic (CODA). We work with a unique parallel corpus of multiple Arabic dialects focusing on five major city dialects. We benchmark newly developed pretrained sequence-to-sequence models on the task of CODAfication. We further show that using dialect identification information improves the performance across all dialects. We make our code, data, and pretrained models publicly available., Comment: Accepted to ArabicNLP 2024, ACL

This paper addresses compressed sensing of linear time-varying (LTV) wireless propagation links under the assumption of double sparsity i.e., sparsity in both the delay and Doppler domains, using Affine Frequency Division Multiplexing (AFDM) measurements. By rigorously linking the double sparsity model to the hierarchical sparsity paradigm, a compressed sensing algorithm with recovery guarantees is proposed for extracting delay-Doppler profiles of LTV channels using AFDM. Through mathematical analysis and numerical results, the superiority of AFDM over other waveforms in terms of channel estimation overhead and minimal sampling rate requirements in sub-Nyquist radar applications is demonstrated., Comment: Accepted in SPAWC 2024

Probability density functions form a specific class of functional data objects with intrinsic properties of scale invariance and relative scale characterized by the unit integral constraint. The Bayes spaces methodology respects their specific nature, and the centred log-ratio transformation enables processing such functional data in the standard Lebesgue space of square-integrable functions. As the data representing densities are frequently observed in their discrete form, the focus has been on their spline representation. Therefore, the crucial step in the approximation is to construct a proper spline basis reflecting their specific properties. Since the centred log-ratio transformation forms a subspace of functions with a zero integral constraint, the standard $B$-spline basis is no longer suitable. Recently, a new spline basis incorporating this zero integral property, called $Z\!B$-splines, was developed. However, this basis does not possess the orthogonal property which is beneficial from computational and application point of view. As a result of this paper, we describe an efficient method for constructing an orthogonal $Z\!B$-splines basis, called $Z\!B$-splinets. The advantages of the $Z\!B$-splinet approach are foremost a computational efficiency and locality of basis supports that is desirable for data interpretability, e.g. in the context of functional principal component analysis. The proposed approach is demonstrated on an empirical demographic dataset.

In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attracted substantial interest from both industry and academy, highlighting its significance in the current technological landscape. Most datasets in this area are primarily focused on Key Information Extraction (KIE), where the extraction process revolves around extracting information using a specific, predefined set of keys. Unlike most existing datasets and benchmarks, our focus is on discovering key-value pairs (KVPs) without relying on predefined keys, navigating through an array of diverse templates and complex layouts. This task presents unique challenges, primarily due to the absence of comprehensive datasets and benchmarks tailored for non-predetermined KVP extraction. To address this gap, we introduce KVP10k , a new dataset and benchmark specifically designed for KVP extraction. The dataset contains 10707 richly annotated images. In our benchmark, we also introduce a new challenging task that combines elements of KIE as well as KVP in a single task. KVP10k sets itself apart with its extensive diversity in data and richly detailed annotations, paving the way for advancements in the field of information extraction from complex business documents., Comment: accepted ICDAR2024

With the continuous evolution of computational devices, more and more applications are being executed remotely. The applications operate on a wide spectrum of devices, ranging from IoT nodes with low computational capabilities to large cloud providers with high capabilities. Remote execution often deals with sensitive data or executes proprietary software. Hence, the challenge of ensuring that the code execution will not be compromised rises. Remote Attestation deals with this challenge. It ensures the code is executed in a non-compromised environment by calculating a potentially large sequence of cryptographic hash values. Each hash calculation is computationally intensive and over a large sequence the overhead becomes extremely high. In this work, we propose LightFAt: a Lightweight Control Flow Attestation scheme. Instead of relying on the expensive cryptographic hash calculation, LightFAt leverages the readings from the processor's Performance Monitor Unit (PMU) in conjunction with a lightweight unsupervised machine learning (ML) classifier to detect whether a target application's control flow is compromised, hence improving the system's security. On the verifier's side, LightFAt reaches a detection accuracy of over 95%, with low false-negative and false-positive rates., Comment: This official version of this paper will appear in the 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

Noncentrosymmetric triangular magnets offer a unique platform for realizing strong quantum fluctuations. However, designing these quantum materials remains an open challenge attributable to a knowledge gap in the tunability of competing exchange interactions at the atomic level. Here, we create a new noncentrosymmetric triangular S = 3/2 magnet CaMnTeO$_6$ based on careful chemical and physical considerations. The model material displays competing magnetic interactions and features nonlinear optical responses with the capability of generating coherent photons. The incommensurate magnetic ground state of CaMnTeO$_6$ with an unusually large spin rotation angle of 127 deg.(1) indicates that the anisotropic interlayer exchange is strong and competing with the isotropic interlayer Heisenberg interaction. The moment of 1.39(1) $\mu$B, extracted from low-temperature heat capacity and neutron diffraction measurements, is only 46% of the expected value of the static moment 3 $\mu$B. This reduction indicates the presence of strong quantum fluctuations in the half-integer spin S = 3/2 CaMnTeO$_6$ magnet, which is rare. By comparing the spin-polarized band structure, chemical bonding, and physical properties of AMnTeO$_6$ (A = Ca, Sr, Pb), we demonstrate how quantum-chemical interpretation can illuminate insights into the fundamentals of magnetic exchange interactions, providing a powerful tool for modulating spin dynamics with atomically precise control.

Cluster states are key resources for measurement-based quantum information processing. Photonic cluster and graph states, in particular, play indispensable roles in quantum network and quantum metrology. We demonstrate a semiconductor quantum dot based device in which the confined hole spin acts as a needle in a quantum knitting machine producing continuously and deterministically at sub-Gigahertz repetition rate single indistinguishable photons which are all polarization entangled to each other and to the spin in a one dimensional cluster state. By projecting two nonadjacent photons onto circular polarization bases we disentangle the spin from the photons emitted in between, thus continuously and deterministically preparing all-photonic cluster states for the first time. We use polarization tomography on four sequentially detected photons to demonstrate and to directly quantify the robustness of the cluster's entanglement and the determinism in its photon generation., Comment: 8 pages, 4 figures, 1 table

The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configurations. Fault injection attacks can result in denial of service, disrupting functionality or leaking secret information. This threat is further amplified in multi-tenancy scenarios. Detecting such threats before loading onto the FPGA is crucial, but existing methods face difficulty identifying sophisticated attacks. We present MaliGNNoma, a machine learning-based solution that accurately identifies malicious FPGA configurations. Serving as a netlist scanning mechanism, it can be employed by cloud service providers as an initial security layer within a necessary multi-tiered security system. By leveraging the inherent graph representation of FPGA netlists, MaliGNNoma employs a graph neural network (GNN) to learn distinctive malicious features, surpassing current approaches. To enhance transparency, MaliGNNoma utilizes a parameterized explainer for the GNN, labeling the FPGA configuration and pinpointing the sub-circuit responsible for the malicious classification. Through extensive experimentation on the ZCU102 board with a Xilinx UltraScale+ FPGA, we validate the effectiveness of MaliGNNoma in detecting malicious configurations, including sophisticated attacks, such as those based on benign modules, like cryptography accelerators. MaliGNNoma achieves a classification accuracy and precision of 98.24% and 97.88%, respectively, surpassing state-of-the-art. We compare MaliGNNoma with five state-of-the-art scanning methods, revealing that not all attack vectors detected by MaliGNNoma are recognized by existing solutions, further emphasizing its effectiveness. Additionally, we make MaliGNNoma and its associated dataset publicly available., Comment: Will appear in the 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

Integrated sensing and communications (ISAC) is regarded as a key technology in next-generation (6G) mobile communication systems. Affine frequency division multiplexing (AFDM) is a recently proposed waveform that achieves optimal diversity gain in high mobility scenarios and has appealing properties in high-frequency communication. In this letter, we present an AFDM-based ISAC system. We first show that in order to identify all delay and Doppler components associated with the propagation medium, either the full AFDM signal or only its pilot part consisting of one discrete affine Fourier transform (DAFT) domain symbol and its guard interval can be used. Our results show that using one pilot symbol achieves almost the same sensing performance as using the entire AFDM frame. Furthermore, due to the chirp nature of AFDM, sensing with one pilot provides a unique feature allowing for simple self-interference cancellation, thus avoiding the need for expensive full duplex methods., Comment: Published in IEEE Wireless Communications Letters, 2024

Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed previously. This paper attempts to define the space of such challenges, and leverage a recently proposed morphological framework to build a comprehensive and extensible model for MSA nominals. Our model design addresses the nominals' intricate morphotactics, as well as their paradigmatic irregularities. Our implementation showcases enhanced accuracy and consistency compared to a commonly used MSA morphological analyzer and generator. We make our models publicly available., Comment: Findings of the Association for Computational Linguistics: EACL 2024