Miracle-dz/UniFAR-FTUs huggingface dataset:
FAE: Autoencoder-Based Failure Binning of RTL Designs for Verification and Debugging

As the Register Transfer Level (RTL) designs are more complicated, debugging becomes a major bottleneck in the design process. To make debugging more efficient, failure binning aims at grouping failure traces caused by the same error source together so that designers can focus on one bug at one time. However, as there are multiple bugs in a design, behaviors exhibited by failure traces are diverse and severely confuse designers. One error source may result in different appearances subject to different activation conditions. In addition, different error sources may also exhibit similar appearances among the limited number of failure traces. In this work, we propose an autoencoder-based failure binning engine name FAE for debugging RTL designs more efficiently. The autoencoders extract meaningful representations from the sparse and high-dimensional feature space to the latent space with good properties for clustering. Superior to prior works, FAE provides confidence ranks between bins and in a bin to clearly guide designers during debugging. Experimental results show that FAE can drive bins of higher purity under an acceptable number of bins than prior works, dropping only few less-informative failures. Evaluated by three common metrics for clustering, FAE also achieves averagely 13.1% improvement in purity, 25.0% improvement in NMI and 18.2% improvement in ARI, respectively. As a result, the proposed autoencoder-based engine, FAE, applies machine learning to extract useful information from diverse failure traces and is effective on failure binning with more focused debugging.
Trajectory Optimization for Cellular-Enabled UAV With Connectivity Outage Constraint

This paper studies the trajectory optimization problem of a single cellular-enabled unmanned aerial vehicle (UAV), taking into account the outage performance of the entire trajectory. To provide real-time control, it is critical for UAV to maintain reliable connectivity with the ground base station (GBS). We first consider the connectivity outage performance, which is defined as the sum of the time duration of the outage performance not meeting a predefined threshold during the entire UAV mission. Then we formulate a trajectory optimization problem to minimize the mission completion time, while ensuring a sum constraint of the connectivity outage performance. We show that the connectivity outage constraint can be transformed into a flying area constraint. Since the formulated problem is NP-hard, a low-complexity method is proposed to solve the problem by finding the shortest path in an undirected weighted graph with enlarged GBS coverage. Simulation results demonstrate the superiority of the proposed scheme over other state-of-the-art schemes, in terms of trajectory length and computational complexity.
Deep Learning for Flood Forecasting and Monitoring in Urban Environments

This paper describes the core computational mechanisms used by an urban flood forecasting and monitoring platform developed as part of a UK Newton Fund project in Malaysia. FLUD-FLood monitoring and forecasting platform for Urban Deployment - is a novel system aiming to deliver an effective and low cost urban flood forecasting solution, which is able to accurately forecast flood risk at street level, and deliver optimized recommendations to the relevant authorities as well as an early warning alerts to members of the public. This platform is based on a hybrid Deep Learning and Fuzzy Logic based architecture. As demonstrated by the experimental results and the analysis presented in this paper, this architecture enables the proposed system to account for factors that are not included in other modern flood forecasting systems, and simultaneously process high volumes of data originating from diverse data sources, in order to deliver accurate predictions concerning urban flood events
Security Compliance Analysis of Reconfigurable Scan Networks

Hardware security adds another dimension to the design space, and more and more attention is paid to protect a circuit against various types of attacks like sniffing, spoofing or IP theft. However, all the efforts for security taken by a designer might be sacrificed by afterwards integrating infrastructure for test, diagnosis and reliability management. Especially, access mechanisms like reconfigurable scan networks (RSNs) may open options for side-channel attacks. Using the presented approach an accurate estimation of reachability properties of all considered benchmarks is provided. The method uses a matrix-based reachability analysis of the original design and the augmented design. The reachability analysis covers complex functional dependencies, caused by configuring a single scan path as well as multiple sequentially activated scan paths through the RSN. This approach adds acceptable runtime to the security verification flow of the design, and shows the designer the introduced possible security violations.
Modeling and Simulation of Ultrafast and Highly Efficient Single Photon Detection From Infrared to Ultraviolet

In this paper we have proposed the design of a single-photon thermoelectric detector’s detection pixel consisting of heat sink (W), thermoelectric sensor (CeB6), absorber (W), and antireflection layer (SiO2). It is justified that the detector with such a detection pixel can provide for the system efficiency above 90% for photons with energy 0.8–4 eV. Modeling and simulation are used to study the processes of heat propagation in four-layer detection pixel of a thermoelectric single-photon detector after photon absorption. Calculations were carried out on the basis of the equation of heat distribution from a limited volume using the three-dimensional matrix method for differential equations. The temporal dependence of the amplitude of the signal appearing on the detection pixel was calculated for different thicknesses of detection pixel layers. The signal time delay, the maximum signal value, the maximum signal reaching time, the signal decay time to the background, and the detector’s count rate were determined. It is shown that the maximum of the signal is many times higher than the background level, does not depend on the area of photon thermalization in the absorber and has a linear dependence on the photon energy. The construction of the detection pixel also provides for terahertz count rates. The signal time delay is less than a femtosecond. Detectors with such characteristics are in demand in various fields of science and may have a wide range of practical applications in future technologies.
Planning Maximum-Manipulability Cutting Paths

This letter presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point cloud. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g., demolition of legacy nuclear plant, where the cut path needs not be precise as long as it achieves dismantling. We show how detours in the path can be leveraged to increase the manipulability of the robot at all points along the path. This helps to avoid singularities, while maximising the robot's capability to make small deviations during task execution. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion, while ensuring significantly greater manipulability when compared against a conventional RRT* planner.
Anomaly Detection with Conditional Variational Autoencoders

Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question. Previous works argued that training VAE models only with inliers is insufficient and the framework should be significantly modified in order to discriminate the anomalous instances. In this work, we exploit the deep conditional variational autoencoder (CVAE) and we define an original loss function together with a metric that targets hierarchically structured data AD. Our motivating application is a real world problem: monitoring the trigger system which is a basic component of many particle physics experiments at the CERN Large Hadron Collider (LHC). In the experiments we show the superior performance of this method for classical machine learning (ML) benchmarks and for our application.
Onion: An Efficient Heuristic for Designing Routerless Network-on-Chip

In routerless network-on-chip (NoC), any pair of cores are directly connected via at least one isolated ring, such that high-cost routers can be entirely abandoned. In this paper, we focus on the problem of designing a set of rings that guarantee the connectivity and minimize the average hop count for all core pairs in an n×n chip multiprocessor. Following an outside-in layered approach, a novel algorithm called Onion is proposed. In Onion, a set of square rings are recursively generated to realize routerless NoC. We prove that Onion guarantees the core-to-core connectivity with the minimum number of links between adjacent cores. To further improve its performance and at the cost of an extra link between adjacent cores, an extended algorithm called Onion+ is also proposed. Extensive numerical results show that both Onion and Onion+ perform increasingly better than RLrec, the current state-of-the-art algorithm, as n increases.
Dynamic Spectrum Anti-Jamming Communications: Challenges and Opportunities

Due to the openness of the transmission medium, it is necessary for radio systems to have anti-jamming abilities. Traditional anti-jamming methods such as sequence-based frequency hopping and direct sequence spread spectrum have shortcomings of low spectral efficiency and fixed communication patterns. With the development of software-defined radio, jamming devices are increasingly advanced and efficient. In this article, we propose a new paradigm for anti-jamming called DSAJ. With the help of cognitive radio and machine learning, the aim of DSAJ is to learn the dynamic and complex spectrum environment and obtain an optimal communication strategy. We first introduce the basic concept of anti-jamming communications and provide a brief summary of anti-jamming methods. Then, through a case study, mathematical modeling and applications of DSAJ are discussed for both single-user and multi-user systems. A real-life DSAJ testbed is described, and some potential research directions are discussed.
Towards Complete Fault Coverage by Test Point Insertion using Optimization-SAT Techniques

Increasing the fault and test coverage is an important goal in the DFT strategies of semiconductor companies. In particular, the growing distribution in the automotive market leads to strict test requirements and high fault coverage demands. A wide-spread method to increase the fault coverage is to insert controllability and observability test points in order to ease fault excitation and detection. Since test points cause hardware overhead, it is important to restrict their number to a minimum to prevent high costs. The paper targets the identification of effective test points in order to make untestable faults testable. Optimization-SAT-based ATPG techniques are used to calculate a minimum set of effective test points, which is able to make all previously undetectable faults of a fault set $F$ detectable. Additionally, untestable fault ordering heuristics are introduced to reduce the number of needed test points. Experimental results on benchmark circuits show that the proposed method is able to generate a small set of test points, which is able to provide 100% fault coverage for stuck-at faults.
Quantitative Monitoring of the Response of Tumor Spheroids Cultured in 3D Environment by Optical Coherence Tomography

In order to have greater clinical prediction during the early stage of drug development process, tumor spheroid culture model is generally used to investigate the drug response. Since the tumor spheroids are cultured in three-dimensional environment, quantification under optical microscope may not be objective and the process is labor intensive. As such, we developed an inverted swept-source optical coherence tomography (OCT) system to quantitatively monitor the response of tumor spheroids under tested condition. The tumor spheroids were cultured in standard 96-well microplates mounted on a special-designed platform of the system. A $3\times 3\times 3$ mm3 OCT reconstructed image could be captured within 5 seconds to enable high throughput assay. The tumor spheroids exposed to anti-cancer drug and modified by gene silencing were respectively monitored by the OCT system. Dynamic response of the tumor spheroids could be quantitatively analyzed to study the efficiency of anti-cancer drug and gene therapy during tumor development. The proposed OCT system has the compatibility of conventional cell culture consumables and practicability of drug development process. Non-invasive and quantitative assessment could be realized for the routine analysis in biological laboratories.
Abnormal Gait Detection by Classifying Inertial Sensor Data using Transfer Learning

Gait analysis is a method used to assess the way we walk or run to highlight possible biomechanical abnormalities. In this paper, we aim at performing this analysis utilizing 4 wireless body adhering modules, each equipped with an accelerometer and gyroscope pair (MPU6050). The data transmitted from these modules are converted to images to facilitate further processing. The sensor data images are then used as an input for transfer learning to the Deep Convolutional Neural Network (CNN) model, Inception v3, wherein the last two layers of the model are trained. Information that flows through the network shapes the structure of the model utilizing the labeled training dataset. The network can differentiate/classify 4 walking styles, namely: 'Normal walk,' 'Hemiplegic gait,' 'Diplegic gait,' and 'Parkinsonian gait. ' By identifying which biomechanical trait a person has, one can further assess the neurological disorder that he/she may be suffering from cost-effectively compared to the traditional rigid methods currently available.
Efficient Analog Defect Simulation

Simulation of defects in industrial analog and mixed-signal (A/MS) circuits has long been regarded as impractical: how to simulate the tens of thousands of potential defects for each test and significant process corner of a PLL or SerDes, for example, when simulating each defect could require hours or days of simulation? Would the results for a subset be useful? This paper shows an efficient flow, consistent with the proposed IEEE P2427 Standard for Analog Defect Modeling and Coverage. The flow describes development of realistic models for hard/catastrophic and soft/parametric defects, introduces analog defect collapsing, discusses coverage results for an ITC'17 A/MS benchmark bandgap voltage reference plus LDO voltage regulator, and a programmable PLL, and shows why the results are applicable to large circuits. The results also show that some strategies are more useful than others.
Urinary suPAR: a non-invasive biomarker of infection and tissue inflammation for use in studies of large free-ranging mammals

Studies of large free-ranging mammals incorporating physiological measurements typically require the collection of urine or faecal samples, due to ethical and practical concerns over trapping or darting animals. However, there is a dearth of validated biomarkers of immune activation and inflammation that can be measured non-invasively. We here evaluate the utility of urinary measurements of the soluble form of the urokinase plasminogen activator receptor (suPAR), for use as a health marker in studies of wild large mammals. We investigate how urinary suPAR concentrations change in response to viral infection and surgical trauma (inflammation), comparing it to the measurement of a marker of cellular immune activation, urinary neopterin (uNEO), in captive rhesus macaques. We then test the field utility of urinary suPAR, assessing the effects of soil and faecal contamination, sunlight, storage at different temperatures, freeze–thaw cycles, and lyophilization. We find that suPAR concentrations rise markedly in response to both infection and surgery-associated inflammation, unlike uNEO concentrations, which only rise in response to the former. Our field validation demonstrates that urinary suPAR is reasonably robust to many of the issues associated with field collection, sample processing, and storage, as long as samples can be stored in a freezer. Urinary suPAR is thus a promising biomarker applicable for monitoring various aspects of health in wild primates and potentially also other large mammals.
Commodity-specific punishment for experimentally induced defection in cooperatively breeding fish

Coercion is an important but underrated component in the evolution of cooperative behaviour. According to the pay-to-stay hypothesis of cooperative breeding, subordinates trade alloparental care for the concession to stay in the group. Punishment of idle subordinates is a key prediction of this hypothesis, which has received some experimental scrutiny. However, previous studies neither allowed separating between punishment and effects of disruption of social dynamics, nor did they differentiate between different helping behaviours that may reflect either mutualistic or reciprocal interaction dynamics. In the cooperative breeder Neolamprologus pulcher, we experimentally engineered the ability of subordinates to contribute to alloparental care by manipulating two different helping behaviours independently from one another in a full factorial design. We recorded the treatment effects on breeder aggression, subordinate helping efforts and submissive displays. We found two divergent regulatory mechanisms of cooperation, dependent on behavioural function. Experimental impediment of territory maintenance of subordinates triggered punishment by dominants, whereas prevented defence against egg predators released a compensatory response of subordinates without any enforcement, suggesting pre-emptive appeasement. These effects occurred independently of one another. Apparently, in the complex negotiation process among members of cooperative groups, behaviours fulfilling different functions may be regulated by divergent interaction mechanisms.
Early warning of some notifiable infectious diseases in China by the artificial neural network

In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.
In situ analysis of shear bands and boundary layer formation in metals

Shear banding is a plastic instability in large deformation of solids where the flow becomes concentrated in narrow layers, with broad implications in materials processing applications and dynamic failure of metals. Given the extremely small length and time scales involved, several challenges persist in studying the development of shear bands. Here, we present a new approach to study shear bands at low speeds using low melting point alloys. We use in situ imaging to directly capture the essential features of shear banding, including transition from homogeneous to shear banded flow, band nucleation and propagation dynamics, and temporal evolution of the flow around a developing band. High-resolution, time-resolved measurements of the local displacement and velocity profiles during shear band growth are presented. The experiments are complemented by an analysis of the shear band growth as a Bingham fluid flow. It is shown that shear banding occurs only beyond a critical shear stress and is accompanied by a sharp drop in the viscosity by several orders of magnitude, analogous to the yielding transition in yield-stress fluids. Likewise, the displacement field around a nucleated band evolves in a manner that resembles boundary layer formation, with the band thickness scaling with time as a power law.
The NIa-Protease Protein Encoded by the Pepper Mottle Virus Is a Pathogenicity Determinant and Releases DNA Methylation of Nicotiana benthamiana

It is well documented that the canonical function of NIa-protease (NIa-Pro) of the potyviruses is responsible for cleaving the viral polyprotein into functional proteins. Although NIa-Pro is vital for the infection cycle of potyviruses, the function of NIa-Pro in the interaction of the potyvirus host is not clear. In this study, NIa-Pro is ectopically expressed from a potato virus X (PVX) vector and infiltrates Nicotiana benthamiana wild type and 16-TGS. The pathogenicity and inhibition of host transcriptional gene silencing (TGS) are characterized. Ectopic expression of NIa-Pro from a PVX vector resulted in severe mosaic symptoms followed by a hypersensitive-like response in N. benthamiana. Furthermore, PepMoV NIa-Pro was able to reverse established TGS of a green fluorescent protein transgene by reducing methylation of promoter sequences in N. benthamiana and possessed the capacity to interfere with the global methylation of N. benthamiana. Taken together, the results of this study likely suggest that PepMoV NIa-Pro is a pathogenicity determinant and a potent suppressor of host TGS and suggest that NIa-Pro may employ novel mechanisms to suppress host antiviral defenses. To the best of our knowledge, this is the first report of a plant RNA virus modulating host TGS in a novel manner by interfering with the establishment of the methylation step of the plant DNA methylation pathway.