Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Neural Network based Hardware Trojan Detection at Intermediate Representative for SoC Platforms

The rapid growth of the Internet of Things (IoT) industry has increased the demand for intellectual property (IP) cores. Increasing numbers of third-party vendors have raised security concerns for System-on-Chip (SoC) designers. With the growing complexity of SoC design, the workload is overwhelming for SoC designers to diagnose security vulnerabilities manually. Almost all existing SoC platforms are developed using SystemVerilog. However, there is a lack of reliable security static analysis tools for directly processing the SystemVerilog program. Due to its open-source, flexibility and extendability, RISC-V CPU has become an ideal platform for the IoT applications such as wearable devices, entertainment, smart thermostats, etc. As a result, assuring the trustworthiness of a given RISC-V system is highly desired. This paper proposes a graph neural network-based Trojan detection framework to protect the RISC-V SoC platform written in SystemVerilog from intruding malicious logic. The study is under-construction and planned to be validated on the Ariane RISC-V CPU with several peripheral IPs in the experimental section.
MnM: A Fast and Efficient Min/Max Searching in MRAM

In-Memory Computing (IMC) technology has been considered to be a promising approach to solve well-known memory-wall challenge for data intensive applications. In this paper, we are the first to propose MnM, a novel IMC system with innovative architecture/circuit designs for fast and efficient Min/Max searching computation in emerging Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM). Our proposed SOT-MRAM based in-memory logic circuits are specially optimized to perform parallel, one-cycle XNOR logic that are heavily used in the Min/Max searching-in-memory algorithm. Our novel in-memory XNOR circuit also has an overhead of just two transistors per row when compared to most prior methodologies which typically use multiple sense amplifiers or complex CMOS logic gates. We also design all other required peripheral circuits for implementing complete Min/Max searching-in-MRAM computation. Our cross-layer comprehensive experiments on Dijkstra's algorithm and other sorting algorithms in real word datasets show that our MnM could achieve significant performance improvement over CPUs, GPUs, and other competing IMC platforms based on RRAM/MRAM/DRAM.
RAPTA: A Hierarchical Representation Learning Solution For Real-Time Prediction of Path-Based Static Timing Analysis

This paper presents RAPTA, a customized Representation-learning Architecture for automation of feature engineering and predicting the result of Path-based Timing-Analysis early in the physical design cycle. RAPTA offers multiple advantages compared to prior work: 1) It has superior accuracy with errors std ranges 3.9ps~16.05ps in 32nm technology. 2) RAPTA's architecture does not change with feature-set size, 3) RAPTA does not require manual input feature engineering. To the best of our knowledge, this is the first work, in which Bidirectional Long Short-Term Memory (Bi-LSTM) representation learning is used to digest raw information for feature engineering, where generation of latent features and Multilayer Perceptron (MLP) based regression for timing prediction can be trained end-to-end.
Ran$Net: An Anti-Ransomware Methodology based on Cache Monitoring and Deep Learning

Ransomware has become a serious threat in the cyberspace. Existing software pattern-based malware detectors are specific for certain ransomware and may not capture new variants. Recognizing a common essential behavior of ransomware - employing local cryptographic software for malicious encryption and therefore leaving footprints on the victim machine's caches, this work proposes an anti-ransomware methodology, Ran$Net, based on hardware activities. It consists of a passive cache monitor to log suspicious cache activities, and a follow-on non-profiled deep learning analysis strategy to retrieve the secret cryptographic key from the timing traces generated by the monitor. We implement the first of its kind tool to combat an open-source ransomware and successfully recover the secret key.
Stratification of Children with Autism Spectrum Disorder Through Fusion of Temporal Information in Eye-gaze Scan-Paths

Background: Looking pattern differences are shown to separate individuals with Autism Spectrum Disorder (ASD) and Typically Developing (TD) controls. Recent studies have shown that, in children with ASD, these patterns change with intellectual and social impairments, suggesting that patterns of social attention provide indices of clinically meaningful variation in ASD. Method: We conducted a naturalistic study of children with ASD (n = 55) and typical development (TD, n = 32). A battery of eye-tracking video stimuli was used in the study, including Activity Monitoring (AM), Social Referencing (SR), Theory of Mind (ToM), and Dyadic Bid (DB) tasks. This work reports on the feasibility of spatial and spatiotemporal scanpaths generated from eye-gaze patterns of these paradigms in stratifying ASD and TD groups. Algorithm: This article presents an approach for automatically identifying clinically meaningful information contained within the raw eye-tracking data of children with ASD and TD. The proposed mechanism utilizes combinations of eye-gaze scan-paths (spatial information), fused with temporal information and pupil velocity data and Convolutional Neural Network (CNN) for stratification of diagnosis (ASD or TD). Results: Spatial eye-gaze representations in the form of scanpaths in stratifying ASD and TD (ASD vs. TD: DNN: 74.4%) are feasible. These spatial eye-gaze features, e.g., scan-paths, are shown to be sensitive to factors mediating heterogeneity in ASD: age (ASD: 2–4 y/old vs. 10–17 y/old CNN: 80.5%), gender (Male vs. Female ASD: DNN: 78.0%) and the mixture of age and gender (5–9 y/old Male vs. 5–9 y/old Female ASD: DNN:98.8%). Limiting scan-path representations temporally increased variance in stratification performance, attesting to the importance of the temporal dimension of eye-gaze data. Spatio-Temporal scan-paths that incorporate velocity of eye movement in their images of eye-gaze are shown to outperform other feature representation methods achieving classification accuracy of 80.25%. Conclusion: The results indicate the feasibility of scan-path images to stratify ASD and TD diagnosis in children of varying ages and gender. Infusion of temporal information and velocity data improves the classification performance of our deep learning models. Such novel velocity fused spatio-temporal scan-path features are shown to be able to capture eye gaze patterns that reflect age, gender, and the mixed effect of age and gender, factors that are associated with heterogeneity in ASD and difficulty in identifying robust biomarkers for ASD.
Genotypic Correlations among First Lactation Profitability, Linear Type Traits and Production Characteristics of Holstein Friesian Cows in Turkey

Objective: The aim of this study is to determine genetic correlations between linear type traits, yield characteristics and first lactation profitability for use in selection programs. Material and Methods: The research was carried out in Hatay province between 2012- 2017. A total of 810 cows in the first lactation were used in the study. Analysis of variance and covariances of genetic parameters was carried out with Multivariate Limited Maximum Likelihood test. Results: The average first lactation net profit, 305-d milk yield, first calving age, conception rate at first service, and calving ease rate were determined as 540 US$, 6008 kg, 793 d, 61.6%, and 91.7%, respectively. Heritabilities of linear type and production traits ranged from 0.10 to 0.42 and 0.04 to 0.39, respectively. The genetic correlations between first lactation profitability and type traits ranged from -0.08 and 0.42, while between first lactation profitability and production traits ranged from 0.06 and 0.34. The highest genetic correlation was between first lactation profitability and central ligament (0.42), while the lowest genetic correlation was between first lactation profitability and udder depth (-0.08). The highest genetic correlation was between first lactation profitability and lactation protein yield (0.34), while the lowest genetic correlation was between first lactation profitability and conception rate at first service (0.09). Genetic correlations between type and production traits varied from -0.42 (FA and CFS) to 0.49 (DC and 305-d MY). Conclusion: These results showed that primiparous cows having more angular, strong fore udder attachments, strong foot and legs structure were more profitable and opportunities of selection programs by using genetic correlations between linear type traits and production characteristics. It also concluded that the first lactation profitability could be used successfully in Turkish dairy cattle breeding programs.
Colloquium
: Quantum anomalous Hall effect

The quantum Hall (QH) effect, quantized Hall resistance combined with zero longitudinal resistance, is the characteristic experimental fingerprint of Chern insulators - topologically non-trivial states of two-dimensional matter with broken time-reversal symmetry. In Chern insulators, non-trivial bulk band topology is expressed by chiral states that carry current along sample edges without dissipation. The quantum anomalous Hall (QAH) effect refers to QH effects that occur in the absence of external magnetic fields due to spontaneously broken time-reversal symmetry. The QAH effect has now been realized in four different classes of two-dimensional materials: (i) thin films of magnetically (Cr- and/or V-) doped topological insulators in the (Bi,Sb)2Te3 family, (ii) thin films of the intrinsic magnetic topological insulator MnBi2Te4, (iii) moir\'e materials formed from graphene, and (iv ) moir\'e materials formed from transition metal dichalcogenides. In this Article, we review the physical mechanisms responsible for each class of QAH insulator, highlighting both differences and commonalities, and comment on potential applications of the QAH effect.
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks

Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural activity. Many properties of single RNNs are well characterized theoretically, but experimental neuroscience has moved in the direction of studying multiple interacting areas, and RNN theory needs to be likewise extended. We take a constructive approach towards this problem, leveraging tools from nonlinear control theory and machine learning to characterize when combinations of stable RNNs will themselves be stable. Importantly, we derive conditions which allow for massive feedback connections between interacting RNNs. We parameterize these conditions for easy optimization using gradient-based techniques, and show that stability-constrained"networks of networks"can perform well on challenging sequential-processing benchmark tasks. Altogether, our results provide a principled approach towards understanding distributed, modular function in the brain.
Topological extraordinary optical transmission

: Τ he incumbent technology for bringing light to the nanoscale, the near-field scanning optical microscope, has notoriously small throughput efficiencies – of the order of 10 -4 - 10 -5 , or less. We report on a broadband , topological, unidirectionally-guiding structure, not requiring adiabatic tapering and in principle enabling near- p erfect ( ~ 100%) o ptical t ransmission through an u nstructured s ingle (POTUS) arbitrarily-subdiffraction slit at its end. Specifically, for a slit width of just λ eff /72 ( λ 0 /138) the attained normalized transmission coefficient reaches a value of 1.52 , while for a unidirectional-only (non-topological) device the normalized transmission through a λ eff /21 ( ~ λ 0 /107) slit reaches 1.14 – both, limited only by inherent material losses, and with zero reflection from the slit. The associated, under ideal (ultralow-loss) conditions, near- p erfect o ptical e xtraordinary t ransmission (POET)
Localization by particle–hole symmetry breaking: a loop expansion

Localization by a broken particle–hole (PH) symmetry in a random system of non-interacting quantum particles is studied on a d-dimensional lattice. Our approach is based on a chiral symmetry argument and the corresponding invariant measure, where the latter is described by a Grassmann functional integral. Within a loop expansion we find for small loops diffusion in the case of PH symmetry. Breaking the PH symmetry results in the creation of random dimers, which suppress diffusion and lead to localization on the scale D/|μ| , where D is the effective diffusion coefficient at PH symmetry and μ is the parameter related to PH symmetry breaking.
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning

Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian or Lipschitz, and demands a posterior estimate whose representational complexity grows unbounded with time. In this work, we develop a novel MBRL method (i) which relaxes the assumptions on the target transition model to belong to a generic family of mixture models; (ii) is applicable to large-scale training by incorporating a compression step such that the posterior estimate consists of a Bayesian coreset of only statistically significant past state-action pairs; and (iii) exhibits a sublinear Bayesian regret. To achieve these results, we adopt an approach based upon Stein's method, which, under a smoothness condition on the constructed posterior and target, allows distributional distance to be evaluated in closed form as the kernelized Stein discrepancy (KSD). The aforementioned compression step is then computed in terms of greedily retaining only those samples which are more than a certain KSD away from the previous model estimate. Experimentally, we observe that this approach is competitive with several state-of-the-art RL methodologies, and can achieve up-to 50 percent reduction in wall clock time in some continuous control environments.
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks

Efficient deep neural network (DNN) models equipped with compact operators (e.g., depthwise convolutions) have shown great potential in reducing DNNs' theoretical complexity (e.g., the total number of weights/operations) while maintaining a decent model accuracy. However, existing efficient DNNs are still limited in fulfilling their promise in boosting real-hardware efficiency, due to their commonly adopted compact operators' low hardware utilization. In this work, we open up a new compression paradigm for developing real-hardware efficient DNNs, leading to boosted hardware efficiency while maintaining model accuracy. Interestingly, we observe that while some DNN layers' activation functions help DNNs' training optimization and achievable accuracy, they can be properly removed after training without compromising the model accuracy. Inspired by this observation, we propose a framework dubbed DepthShrinker, which develops hardware-friendly compact networks via shrinking the basic building blocks of existing efficient DNNs that feature irregular computation patterns into dense ones with much improved hardware utilization and thus real-hardware efficiency. Excitingly, our DepthShrinker framework delivers hardware-friendly compact networks that outperform both state-of-the-art efficient DNNs and compression techniques, e.g., a 3.06% higher accuracy and 1.53$\times$ throughput on Tesla V100 over SOTA channel-wise pruning method MetaPruning. Our codes are available at: https://github.com/facebookresearch/DepthShrinker.
A simple method to reprogram the binding specificity of DNA-coated colloids that crystallize

DNA-coated colloids can crystallize into a multi-tude of lattices, ranging from face-centered cubic to diamond and thereby contribute to our understanding of crystallization and open avenues to producing structures with useful photonic properties. Despite the broad potential design space of DNA-coated colloids, the design cycle for syn-thesizing DNA-coated particles is slow: prepar-ing a particle with a new type of DNA sequence takes more than one day and requires custom-made and chemically modiﬁed DNA that typi-cally takes the supplier over a month to synthesize. Here, we introduce a method to generate particles with custom sequences from a single feed stock in under an hour at ambient condi-tions. Our method appends new DNA domains onto the DNA grafted to colloidal particles based on a template that takes the supplier less than a week to produce. The resultant particles crystallize as readily and at the same temperature as those produced via direct chemical synthesis. Moreover, we show that particles coated with a single sequence can be converted into a variety of building blocks with diﬀering speciﬁcities by appending diﬀerent DNA sequences to them. This approach to DNA-coated particle prepara-tion will make it practical to identify optimal and complex particle sequence designs and to expand the use of DNA-coated colloids to a much broader range of investigators and commercial entities.
xView3-SAR: Detecting Dark Fishing Activity Using Satellite Radar Imagery

Unsustainable ﬁshing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that evade monitoring systems—known as “dark vessels”—is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require domain-speciﬁc treatment and is not widely accessible to the ML community. Moreover, the objects (vessels) are small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels from SAR. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We provide an overview of the results from the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (https://iuu.xview.us/) and code (https://github.com/DIUx-xView) to support ongoing development and evaluation of ML approaches for this important application.
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they generally need hundreds or thousands of sequential function evaluations (steps) of large neural networks to draw a sample. Sampling from DPMs can be viewed alternatively as solving the corresponding diffusion ordinary differential equations (ODEs). In this work, we propose an exact formulation of the solution of diffusion ODEs. The formulation analytically computes the linear part of the solution, rather than leaving all terms to black-box ODE solvers as adopted in previous works. By applying change-of-variable, the solution can be equivalently simplified to an exponentially weighted integral of the neural network. Based on our formulation, we propose DPM-Solver, a fast dedicated high-order solver for diffusion ODEs with the convergence order guarantee. DPM-Solver is suitable for both discrete-time and continuous-time DPMs without any further training. Experimental results show that DPM-Solver can generate high-quality samples in only 10 to 20 function evaluations on various datasets. We achieve 4.70 FID in 10 function evaluations and 2.87 FID in 20 function evaluations on the CIFAR10 dataset, and a $4\sim 16\times$ speedup compared with previous state-of-the-art training-free samplers on various datasets.
Detector optimization to reduce the cosmogenic neutron backgrounds in the TAO experiment

Short-baseline reactor antineutrino experiments with shallow overburden usually have large cosmogenic neutron backgrounds. The Taishan Antineutrino Observatory (TAO) is a ton-level liquid scintillator detector located at about 30 m from a core of the Taishan Nuclear Power Plant. It will measure the reactor antineutrino spectrum with high precision and high energy resolution to provide a reference spectrum for JUNO and other reactor antineutrino experiments, and provide a benchmark measurement to test nuclear databases. Background is one of the critical concerns of TAO since the overburden is just 10 meter-water-equivalent. The cosmogenic neutron background was estimated to be ∼10% of signals [1]. With detailed Monte Carlo simulations, we propose several measures in this work to reduce the neutron backgrounds, including doping Gadolinium in the buffer liquid, adding a polyethylene layer above the bottom lead shield, and optimization of the veto strategy. With these improvements, the neutron background-to-signal ratio can be reduced to ∼2%, and might be further suppressed with pulse shape discrimination.
MERIT concordance table: IPC - ISIC (rev.2)

This document contains the MERIT concordance table between the International Patent Classification (IPC) used by the European Patent Office (EPO) and the International Standard Industrial Classification of All Economic Activities (ISIC - rev. 2) of the United Nations. ISICs are grouped into 22 different aggregate sectors. Only the manufacturing sectors are taken into account. The percentages in the last column of the table give the share of the patents in each IPC-class assigned to the accessory ISIC-category. A percentage of 100%, for example, therefore means that all patents in the IPC-class are assigned to the corresponding ISIC-category. The table makes it possible to assign the patent data by field of technology of EPO to a classification by economic sector. While constructing this table, we have used a similar concordance table (between IPC and the Finnish version of ISIC) prepared by Statistics Finland (supplied by M. Akerblom, whose help is gratefully acknowledged).
The Twisted Magnetic Field of the Protobinary L483

We present H-band (1.65 μm) and SOFIA HAWC+ 154 μm polarization observations of the low-mass core L483. Our H-band observations reveal a magnetic field that is overwhelmingly in the E–W direction, which is approximately parallel to the bipolar outflow that is observed in scattered IR light and in single-dish 12CO observations. From our 154 μm data, we infer a ∼45° twist in the magnetic field within the inner 5″ (1000 au) of L483. We compare these new observations with published single-dish 350 μm polarimetry and find that the 10,000 au scale H-band data match the smaller-scale 350 μm data, indicating that the collapse of L483 is magnetically regulated on these larger scales. We also present high-resolution 1.3 mm Atacama Large Millimeter/submillimeter Array data of L483 that reveals it is a close binary star with a separation of 34 au. The plane of the binary of L483 is observed to be approximately parallel to the twisted field in the inner 1000 au. Comparing this result to the ∼1000 au protostellar envelope, we find that the envelope is roughly perpendicular to the 1000 au HAWC+ field. Using the data presented, we speculate that L483 initially formed as a wide binary and the companion star migrated to its current position, causing an extreme shift in angular momentum thereby producing the twisted magnetic field morphology observed. More observations are needed to further test this scenario.