Miracle-dz/UniFAR-FTUs huggingface dataset:
lntegrating Message-Passing with Vector Architectures

Vector architecures proride excellent computational throughput, while successfully tolerating memory latency by pipelining memory accesses. In this paper, we propose a generalization of vector architectures to message-passing multicomputers, which combines the efficiency of vector computation with the scalablity of distributed-memory systems. In our proposed architecture, each node is a conventional vector processor (with chaining capability and pipelined functional units) augmented by native instructions to send and receive messages through vector registers. In this scheme, inter-node communication can be performed via vector-send/receive instructions, gaining the benefits of communication pipelining, reduced memory copies (memory-to-repter-to-register instead of memory-to-memory-to-cache), and lower communication latency (due to tight processor-communication coupling). We show that this strong integration between functional and communication units can lead to substantial performance improvement over conventional message-passing multicomputers. We model pipelined computation-communication systems both analytically and with a detailed construction-level simulation, and compare this simulation data with empirical results from an Intel Paragon. Preliminary data from a matrix multiplication example indicates our proposed vector-parallel architecture often significant scalability benefits over existing message-passing systems.
Palm-Sized Quadcopter for Three-Dimensional Chemical Plume Tracking

In this study, we designed and experimentally verified the placement of odor sensors and an algorithm using the aero-olfactory effect of a palm-sized quadcopter to solve the 3-D chemical plume tracking (3-D-CPT) problem. Solving 3-D-CPT is important in engineering as it helps perform rescue operations during disasters and identify sources of harmful substances. Moreover, the odor sensors must be properly located and a CPT algorithm be applied to improve the tracking performance of a chemical. However, studies regarding the use of quadcopters for solving the 3-D-CPT problem are scarce, and the relationship between the odor sensor location and algorithm is debatable. Hence, we utilized particle image velocimetry (PIV), an airflow visualization technology, to evaluate the arrival direction of chemicals at different heights. The results showed that odor sensors must be placed on the upper and front surfaces of a quadcopter to monitor the chemicals three-dimensionally. Additionally, we designed a 3-D surge-casting algorithm, which is an extension of the CPT strategy of a flying moth, that is, surge casting, to accommodate the proposed odor sensor placement. By conducting 3-D-CPT experiments based on different heights of odor sources using the proposed system, we discovered that even in an environment with significant changes in the wind direction the CPT performance is better than that of the conventional 3-D-CPT algorithm. Thus, 3-D-CPT should be further improved to enable its application in unknown and cluttered environments. In this study, we improved the 3-D-CPT performance of a palm-sized quadcopter by designing an appropriate sensor arrangement and algorithm balance.
Power Consumption Modeling of Wheeled Mobile Robots With Multiple Driving Modes

Wheeled mobile robots (WMRs) are being used in many real-world applications. The main challenge in the deployment of WMRs is their limited battery power. In order to complete a given task autonomously, it becomes necessary to estimating a priori the total energy consumption for the task assigned to a WMR. An accurate power consumption model allows to estimate the energy consumption of a WMR. This article presents the power consumption modeling of WMRs using a power-based graphical modeling tool called bond graph. First, the dynamic bond graph model is developed for the generalized configuration of a WMR according to number and types of wheels attached in chassis. Then, the developed bond graph model is utilized for the power consumption modeling of a WMR. The proposed power consumption model includes the various driving modes of a WMR, by considering its degree of steerability. Finally, the proposed methodology is applied to a reconfigurable outdoor WMR with a degree of steerability varying from 0 to 2. The estimated power from the model is compared with the measured power from the experiments. Further, the proposed power model is used for the energy planning in a trajectory tracking task based on changing driving modes of a reconfigurable WMR.
A2S2-GNN: Rigging GNN-Based Social Status by Adversarial Attacks in Signed Social Networks

Social status, the social influence of a user, plays an important role in many real-world applications, e.g., trust relations and information propagation in a social network. In this paper, we reveal the possibility of falsifying social status through adversarial attacks in graph neural networks (GNNs). Different from neural networks in the visual or speech domain, GNNs take the attributes of nodes and edges in a graph as features. To cater to the characteristics of GNNs,<inline-formula> <tex-math notation="LaTeX">$\vphantom {_{\int }}$ </tex-math></inline-formula> we design a new paradigm of adversarial example attack, named <inline-formula> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula>- GNN (<inline-formula> <tex-math notation="LaTeX">$\mathbf {GNN}$ </tex-math></inline-formula>-based <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula>dversarial <inline-formula> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula>ttacks on <inline-formula> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula>ocial <inline-formula> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula>tatus), aiming at manipulating the social status of a target node in social networks. The key idea is to establish relationships or break relationships between a set of compromised nodes and the target node. More specifically, we consider a signed directed graph representing complicated positive/negative asymmetric relationships between nodes. We design an efficient adversarial attack algorithm to determine the minimum set of signed links that should be created or deleted to reach the attack objective. We conduct extensive experiments on baseline datasets. Compared with the benchmark algorithms, <inline-formula> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula>- GNN can effectively promote or vilify the social status of the target node up to 89.36% and 192.38%, respectively, while keeping the modification to the social network to the minimum. Furthermore, the experimental results on six status evaluating algorithms verify the transferability of our proposed attack algorithm.
Entropy of meromorphic maps acting on analytic sets

Let $f : X\to X$ be a dominating meromorphic map on a compact K\"ahler manifold $X$ of dimension $k$. We extend the notion of topological entropy $h^l_{\mathrm{top}}(f)$ for the action of $f$ on (local) analytic sets of dimension $0\leq l \leq k$. For an ergodic probability measure $\nu$, we extend similarly the notion of measure-theoretic entropy $h_{\nu}^l(f)$. Under mild hypothesis, we compute $h^l_{\mathrm{top}}(f)$ in term of the dynamical degrees of $f$. In the particular case of endomorphisms of $\mathbb{P}^2$ of degree $d$, we show that $h^1_{\mathrm{top}}(f)= \log d$ for a large class of maps but we give examples where $h^1_{\mathrm{top}}(f)\neq \log d$.
NMR study of the vortex slush phase in organic superconductor κ-(BEDT-TTF) 2 Cu(NCS) 2

The vortex state in a single crystal of the layered organic superconductor $\ensuremath{\kappa}\text{\ensuremath{-}}(\mathrm{BEDT}\text{\ensuremath{-}}{\mathrm{TTF})}_{2}\mathrm{Cu}(\mathrm{NCS}{)}_{2}$, where BEDT-TTF (or ET) is bis(ethylenedithio)tetrathiafulvalene, was studied by $^{1}\mathrm{H}\text{\ensuremath{-}}\mathrm{NMR}$. Under a low field region around $0.75\phantom{\rule{0.3em}{0ex}}\mathrm{T}$, the vortex-glass-liquid transition was demonstrated by a diverging of the longitudinal nuclear spin relaxation rate and peak broadening in spectra. Under a high field region near the upper critical field ${H}_{c2}(0)\ensuremath{\simeq}7\phantom{\rule{0.3em}{0ex}}\mathrm{T}$, the curvature of nuclear spin relaxation curves showed a drastic change at the temperature where the emergence of the quantum vortex slush state was reported. The mechanism in this curvature change was discussed in terms of the fluctuating field produced by fragments of vortex glass.
On mod $p$ local-global compatibility for $\mathrm{GL}_n(\mathbb{Q}_p)$ in the ordinary case

Let $p$ be a prime number, $n>2$ an integer, and $F$ a CM field in which $p$ splits completely. Assume that a continuous automorphic Galois representation $\overline{r}:\mathrm{Gal}(\overline{\mathbf{Q}}/F)\rightarrow\mathrm{GL}_n(\overline{\mathbf{F}}_p)$ is upper-triangular and satisfies certain genericity conditions at a place $w$ above $p$, and that every subquotient of $\overline{r}|_{\mathrm{Gal}(\overline{\mathbf{Q}}_p/F_w)}$ of dimension $>2$ is Fontaine--Laffaille generic. In this paper, we show that the isomorphism class of $\overline{r}|_{\mathrm{Gal}(\overline{\mathbf{Q}}_p/F_w)}$ is determined by $\mathrm{GL}_n(F_w)$-action on a space of mod $p$ algebraic automorphic forms cut out by the maximal ideal of a Hecke algebra associated to $\overline{r}$, assuming a weight elimination result which is a theorem of Bao V. Le Hung in his forthcoming paper~\cite{LeH}. In particular, we show that the wildly ramified part of $\overline{r}|_{\mathrm{Gal}(\overline{\mathbf{Q}}_p/F_w)}$ is determined by the action of Jacobi sum operators (seen as elements of $\mathbf{F}_p[\mathrm{GL}_n(\mathbf{F}_p)]$) on this space.
Readiness to Accept Genetic Testing for Personalized Medicine: Survey Findings on the Role of Socio-Demographic Characteristics, Health Vulnerabilities, Perceived Genetic Risk and Personality Factors

Studies from various countries have shown that majorities would accept genetic testing for personalization of treatment, but little is known about differences among population subgroups. The present study investigated whether readiness to accept a hypothetical cost-free offer of genetic testing to personalize treatment depends on socio-demographic characteristics, health-related vulnerabilities, personal dispositions, and prior awareness about personalized medicine. The study was based on a cross-sectional survey design. Out of a representative initial sample of 50–80-year-old Danish citizens (n = 15,072), n = 6807 returned a fully answered web-based questionnaire. Socio-demographic data were added from a national registry. Data were analyzed by multivariable logistic regression. A large majority of respondents (78.3%) expressed their readiness to be tested. Rates were higher in men, younger persons, and those with higher income. Additionally, ex-smokers and obese persons as well as those less satisfied with their health and respondents who perceived a personal genetic risk were more interested, as were those with higher internal health control, higher extraversion, higher emotional stability, and those who had not heard about this option before. Further research should investigate the specific concerns among population subgroups which need being addressed by systematic communication efforts in a clinical but also a broader public health context.
Titan: Security Analysis of Large-Scale Hardware Obfuscation Using Graph Neural Networks

Hardware obfuscation is a prominent design-for-trust solution that thwarts intellectual property (IP) piracy and reverse-engineering of integrated circuits (ICs). Researchers have proposed several large-scale obfuscation techniques that achieve high output corruption—thus offering resilience against seminal attacks along with acceptable power, performance, and area overheads. However, the research community has primarily evaluated hardware obfuscation on relatively small scales of obfuscation (i.e., a fixed number of obfuscated components). Moreover, prior art caters toward specific schemes based either on gate obfuscation or interconnect obfuscation, i.e., two prominent types of hardware obfuscation. The former shortcoming suggests focusing on large-scale obfuscation schemes, and the latter suggests the need for a holistic assessment framework. In this work, we propose Titan, a holistic framework considering large-scale gate and interconnect obfuscation schemes. More specifically, we propose a graph neural network (GNN)-based attack framework that is trained to exploit structural and functional properties of any secured circuit to recover its obfuscated components. We evaluate Titan on various obfuscation schemes, considering selected ITC-99 benchmarks with up to 50% obfuscation scale, i.e., up to 21,326 obfuscated components. We observe a substantial information leakage through structural and functional properties of secured designs even for large-scale obfuscation. We quantify the information leakage in two ways: first, an average reduction of Hamming distance (HD, a well-established metric for attack evaluation) by 23.27 and 16.19 percentage points over the baseline of random guessing for gate and interconnect obfuscation, respectively; second, an average recovery of 63.40% and 77.94% of obfuscated components for gate and interconnect obfuscation, respectively. Importantly, these results are superior to six state-of-the-art attacks. We will open-source our framework and associated artifacts to enable reproducibility and foster future work.
Radial Force Loads on the Impeller of a Spiral Casing Pump – Comparison of Experiment and CFD Simulation

Computational fluid dynamics (CFD) is becoming a widely used tool in the industrial fields. Experimental methods are expensive and time-consuming and CFD is a suitable substitute. In order to investigate the accuracy between the two methods, the radial force on a 6-bladed impeller was measured. The results reveal that the steady-steady analysis method fails, and higher accuracy is achieved using transient simulation. Fairly good accuracy is achieved when simulating the power parameters, where it is sufficient to use the steady-state method as well to save time. On the other hand, for force load detection, where the magnitude of the radial force varies with the position of the impeller, the transient method should be used. The distribution of the non-stationary radial force vectors is symmetric around the origin and lies mainly in 6 regions, which are the same as the number of impeller blades.
Phenotypic trend, breeding value and heritability of biometric traits in Marwari horses

Analysis of phenotypic trend and estimation of inbreeding coefficient, breeding value and heritability of biometric traits was carried out for a total of 226 Marwari horses. The pedigree construct divided the entire population of 226 horses into 6 tiers spanning over a period of 30 years. The inbreeding coefficient of the herd was estimated to be 0.0017. The average height at withers, body length, heart girth and body weight of Marwari horses was 150.15±0.04 cm, 151.44±0.06 cm; 170.02±0.19 cm and 371.34±0.52 kg, respectively with non-significant effect of sex, tier and interaction of sex and tier. The Best Linear Unbiased Prediction (BLUP) estimate of breeding value for the height at withers (150.45 cm), body length (151.97 cm), heart girth (169.73 cm) and body weight (366.3 kg) for Marwari horses was -0.059 cm, -0.079 cm, 0.096 cm and 3.526 kg, respectively. The effect of sex and interaction of sex and tiers was non-significant but that of tier on the estimated breeding value for body weight was significant indicating that body weight received favour in breeding programme. The heritability of height at withers, body length, heart girth and body weight has been estimated to be 0.396±0.586, 0.370±0.777, 0.507±1.95 and 0.597±0.612, respectively. Negligible inbreeding and constant phenotype over decades indicated that the Marwari herd has been bred to maintain enough genetic variation and hence it can be the most reliable source of the stallions and mares for propagation and conservation of this legendry breed of horse which is getting diluted and declining at a faster rate.
Eigenvalue distribution of large weighted bipartite random graphs

We study eigenvalue distribution of the adjacency matrix $A^{(N,p, \alpha)}$ of weighted random bipartite graphs $\Gamma= \Gamma_{N,p}$. We assume that the graphs have $N$ vertices, the ratio of parts is $\frac{\alpha}{1-\alpha}$ and the average number of edges attached to one vertex is $\alpha\cdot p$ or $(1-\alpha)\cdot p$. To each edge of the graph $e_{ij}$ we assign a weight given by a random variable $a_{ij}$ with all moments finite. We consider the moments of normalized eigenvalue counting measure $\sigma_{N,p, \alpha}$ of $A^{(N,p, \alpha)}$. The weak convergence in probability of normalized eigenvalue counting measures is proved.
An Inductorless 20-Gbaud Overshoot Suppression Modulator Driver Using Interleave Frequency Variant Feedback in 90-nm CMOS

An inductorless modulator driver with effective overshoot suppression and a large swing for high-speed optical communications is presented. A novel interleave frequency variant feedback (IFVF) technique is proposed, which employs active feedback loops with the RC degeneration. The $RC$ degeneration allows place the pole in the overall transfer function to shape the waveform for overshoot and ringing suppression. Using a 90-nm complementary metal oxide semiconductor (CMOS) technology, the driver achieves a maximum 3.4 Vpp differential output voltage swing. Clear eye diagrams for non-return to zero (NRZ) and pulse amplitude modulation 4-level (PAM4) signal formats can be obtained up to 32 Gb/s and 20 Gbaud, respectively. The circuit consumes a total power of 560 mW and occupies a core area of only 0.092 mm2.
Scale–Relation Joint Decoupling Network for Remote Sensing Image Semantic Segmentation

As we all know, remote sensing (RS) images contain multiscale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, proximity relationships of same-scale objects, and inclusion relationships of cross-scale objects. However, the existing semantic segmentation methods have never explored the cross-scale relationships, which are especially important when it comes to the situation that the RS objects cannot be accurately identified, they could be supplemented by the surrounding contents. To address the above concern, we propose a scale–relation joint decoupling network (SRJDN) for the semantic segmentation of RS images by simultaneously considering decoupling scales and decoupling relationships to excavate more complete relationships of multiscale RS objects. The SRJDN is performed by following three steps, namely, scale decoupling (SD), relation decoupling (RD), and fine-granularity guided fusion (FGF). The SD module uses dilated convolution with different rates to decouple RS objects into different scale feature groups, from small to large scales. Afterward, the RD considers all the spatial topological relationships and decouples these relationships according to the scale, which is divided into two parts, including same-scale relation extraction (SSRE) and cross-scale relation extraction (CSRE). The SSRE establishes the graph structures at each scale independently to mine the relationships of same-scale RS objects and the CSRE constructs the graph in a unified pattern between cross-scales to explore cross-scale target relationships. Third, the FGF module regards small-scale features as fine-granularity representation and applies its attention map to guide the learning of other scale features, which could mine more reliable and comprehensive saliency information and improve the feature consistency. Numerical experiments conducted on two large-scale fine-resolution RS image datasets empirically demonstrate the robustness of the proposed joint decoupling strategy and the effectiveness of FGF in RS image semantic segmentation tasks.
Loss Function Design for Data-Driven Predictors to Enhance the Energy Efficiency of Connected and Automated Vehicles

In car-following scenarios, accurate previews of the preceding vehicle’s trajectory are essential for minimizing the energy consumption of a following automated vehicle. This work presents a novel design strategy for data-driven vehicle speed predictors to increase the energy efficiency of the following connected and automated vehicle. The loss function is formulated as a weighted-mean-squared error, where the weights are tuned based on the influence of uncertainty at an individual prediction step on the energy consumption of the automated following vehicle. The efficacy of the proposed loss function is validated by applying it to training representative predictors and testing the predictors in the energy-optimal control of the following vehicle. The energy saving of the optimal controller is evaluated by comparing the electricity consumption of a battery electric vehicle with the human driver following, emulated by an intelligent driver model. Simulation results show that the energy consumption of an electric vehicle is reduced by an average of 12% compared to a human driver following demonstrated by an intelligent driver model, whereas using a conventional loss function, a mean-squared error loss function, reduces by 10%.
Finding the maximum eigenvalue of a class of tensors with applications in copositivity test and hypergraphs

Finding the maximum eigenvalue of a symmetric tensor is an important topic in tensor computation and numerical multilinear algebra. This paper is devoted to a semi-definite program algorithm for computing the maximum $H$-eigenvalue of a class of tensors with sign structure called $W$-tensors. The class of $W$-tensors extends the well-studied nonnegative tensors and essentially nonnegative tensors, and covers some important tensors arising naturally from spectral hypergraph theory. Our algorithm is based on a new structured sums-of-squares (SOS) decomposition result for a nonnegative homogeneous polynomial induced by a $W$-tensor. This SOS decomposition enables us to show that computing the maximum $H$-eigenvalue of an even order symmetric $W$-tensor is equivalent to solving a semi-definite program, and hence can be accomplished in polynomial time. Numerical examples are given to illustrate that the proposed algorithm can be used to find maximum $H$-eigenvalue of an even order symmetric $W$-tensor with dimension up to $10,000$. We present two applications for our proposed algorithm: we first provide a polynomial time algorithm for computing the maximum $H$-eigenvalues of large size Laplacian tensors of hyper-stars and hyper-trees; second, we show that the proposed SOS algorithm can be used to test the copositivity of a multivariate form associated with symmetric extended $Z$-tensors, whose order may be even or odd. Numerical experiments illustrate that our structured semi-definite program algorithm is effective and promising.
Dynamics and Control of a Spherical Robot with an Axisymmetric Pendulum Actuator

This paper investigates the possibility of the motion control of a ball with a pendulum mechanism with non-holonomic constraints using gaits - the simplest motions such as acceleration and deceleration during the motion in a straight line, rotation through a given angle and their combination. Also, the controlled motion of the system along a straight line with a constant acceleration is considered. For this problem the algorithm for calculating the control torques is given and it is shown that the resulting reduced system has the first integral of motion.