Miracle-dz/UniFAR-FTUs huggingface dataset:
$H(\textrm{div})$-conforming Finite Element Tensors

A unified construction of $H(\textrm{div})$-conforming finite element tensors, including vector div element, symmetric div matrix element, traceless div matrix element, and in general tensors with linear constraints, is developed in this work. It is based on the geometric decomposition of Lagrange elements into bubble functions on each sub-simplex. Then the tensor at each sub-simplex is decomposed into the tangential and the normal component. The tangential component forms the bubble function space and the normal component characterizes the trace. A deep exploration on boundary degrees of freedom is presented for discovering various finite elements. The developed finite element spaces are $H(\textrm{div})$-conforming and satisfy the discrete inf-sup condition. An explicit basis of the constraint tensor space is also established.
FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping into that power from a robot is non-trivial. We present FogROS2, an open-source platform to facilitate cloud and fog robotics that is included in the Robot Operating System 2 (ROS 2) distribution. FogROS2 is distinct from its predecessor FogROS1 in 9 ways, including lower latency, overhead, and startup times; improved usability, and additional automation, such as region and computer type selection. Additionally, FogROS2 gains performance, timing, and additional improvements associated with ROS 2. In common robot applications, FogROS2 reduces SLAM latency by 50 %, reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning 45x. When compared to FogROS1, FogROS2 reduces network utilization by up to 3.8x, improves startup time by 63 %, and network round-trip latency by 97 % for images using video compression. The source code, examples, and documentation for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://index.ros. org/p/FogROS2/.
A Wide-Field Infrared Camera for the Palomar 200-inch Telescope

The availability of both large aperture telescopes and large format near-infrared (NIR) detectors are making wide-field NIR imaging a reality. We describe the Wide-field Infrared Camera (WIRC), a newly commissioned instrument that provides the Palomar 200-inch telescope with such an imaging capability. WIRC features a field-of-view (FOV) of 4.33 arcminutes on a side with its currently installed 1024-square Rockwell Hawaii-I NIR detector. A 2048-square Rockwell Hawaii-II NIR detector will be installed and commissioned later this year, in collaboration with Caltech, to give WIRC an 8.7 arcminute FOV on a side. WIRC mounts at the telescope's f/3.3 prime focus. The instrument's seeing-limited optical design, optimized for the JHK atmospheric bands, includes a 4-element refractive collimator, two 7-position filter wheels that straddle a Lyot stop, and a 5-element refractive f/3 camera. Typical seeing-limited point spread functions are slightly oversampled with a 0.25 arcsec per pixel plate scale at the detector. The entire optical train is contained within a cryogenic dewar with a 2.5 day hold-time. Entrance hatches at the top of the dewar allow access to the detector without disruption of the optics and optical alignment. The optical, mechanical, cryogenic, and electronic design of the instrument are described, a commissioning science image and performance analyses are presented.
Shifting Mortality Dynamics in the United States During the COVID-19 Pandemic as Measured by Years of Life Lost

Background: During March 2020 to September 2022, more than 1 million COVID-19–involved deaths occurred in the United States (1). As described by Shiels and colleagues (2), COVID-19 deaths during March to December 2020 and January to October 2021 were similar. However, COVID-19–involved deaths increased among younger persons and decreased among older adults in 2021 versus 2020 (2), reflecting excess premature mortality from COVID-19. Objective: We endeavored to quantify this downward age shift in COVID-19–involved deaths, which required an ageweighted metric. Unlike the mortality metric, the measure of years of life lost (YLL) (3) offers an indicator of prematuremortality based on the estimated number of years a person would have lived if they had not died prematurely. We therefore sought to estimate YLL associated with leading causes of U.S. death duringmatched 10-month intervals in 2020 and 2021. Methods and Findings: Mortality data from March to December in 2020 and 2021 were obtained from CDCWONDER (Centers for Disease Control and PreventionWide-rangingONline Data for Epidemiologic Research), an integrated system of publicuse data sets spanning public health topics (1). Age-specific standard life expectancies were obtained from the 2017 World Population Prospects and World Health Organization Global Health Estimate, providing frontier-period life expectancy projections for the year 2050 to represent lifespans thought to be achieved by a substantial number of people alive at the time of this analysis (3, 4). Absolute YLL due to cause of death c, in population of age a during interval t were defined as YLL (c, a, t) = D(c, a, t) SLE(a), where D represents the number of deaths and SLE represents standard life expectancy (3). For the 15 leading causes of death, YLL were estimated, comparing March to December 2020 with March to December 2021 to minimize the effects of seasonal variation in mortality on the comparator intervals. Data were analyzed in Python version
Predator-prey and host-parasite spatial stochastic models

We consider two interacting particle systems on Z d to model predator ] prey and host ] parasite interactions . In both models we have two types of (cid:14) . d particles 1 and 2 and each site in Z can be in one of four states: empty, occupied by a type 1 particle, occupied by a type 2 particle or occupied by (cid:14) . two particles one of each type . Each type gives birth to particles of the same type on nearest neighbor sites . The interaction between the two types of particles occurs only when a site is occupied by one particle of each type . For both models we show that coexistence and noncoexistence are possible in any dimension .
Rational points on twists of X0(63)

Let $\varrho\colon G_\mathbb{Q}\longrightarrow PGL_2(\mathbb{F}_p)$ be a Galois representation with cyclotomic determinant, and let $N>1$ be an integer that is square mod $p$. There exist two twisted modular curves $X^+(N,p)_\varrho$ and $X^+(N,p)'_\varrho$\, defined over~$\mathbb{Q}$ whose rational points classify the quadratic $\mathbb{Q}$-curves of degree $N$ realizing $\varrho$. The paper focuses on the only genus-three instance: the case $N\! =7,\,p=3$. From an explicit description of the automorphism group of the modular curve $X_0(63)$, it follows that the twisted curves are isomorphic over $\mathbb{Q}$ in this case. We also obtain a plane quartic equation for the twists and then produce the desired $\mathbb{Q}$-curves, provided that the set of rational points on this quartic can be determined. The existence of elliptic quotients and of an unramified double cover $X(7,3)_\varrho$ having a genus-two quotient permits a variety of combinations of covers and Prym-Chabauty methods to determine these rational points. We include two examples where these methods apply.
Detecting DGA Botnet based on Malware Behavior Analysis

DGA botnet uses the Domain Generation Algorithm to generate domains that are used to establish the connection between malware bots and malicious actors. It has become a serious threat to internet-connected systems. Detection of DGA botnets is a challenging task due to its complexity and performance issues when processing a great amount of data from real-time large-scale networks. In this paper, we propose and develop a DGA botnet detection method using the combination of the Long Short-Term Memory network (LSTM) and network traffic analysis. We also propose a set of rules that can be used for detecting various DGA malware behaviors. Our method recognizes even hard-to-detect dictionary DGAs such as suppobox and matsnu, while providing an F1-score of 0.9888.
An Empirical Study of MPQUIC Schedulers in Mobile Wireless Networks

Multipath QUIC (MPQUIC), an emerging multipath transport protocol (MTP) that inherits the advantages of the canonical multipath TCP (MPTCP) and the widespread QUIC, potentially plays a vital role in 5G and beyond. MPQUIC can exploit multiple networks (e.g., Wi-Fi, LTE, 5G) on a mobile device to boost the quality of services while efficiently utilizing network resources. In MPQUIC, the scheduler, which is in charge of concurrently scheduling data transmission in several paths, largely impacts the protocols’ performance, especially in dynamic environments. In fact, in the literature, a considerable number of MTP schedulers for MPQUIC have been proposed. Unfortunately, their performances have been primarily evaluated in static networks without (or simply) considering mobile ones. Hence, this work attempts to investigate the performance of MPQUIC schedulers in the mobile context, aiming to fill the literature gap. Specifically, we implement and assess the performance of five MPQUIC schedulers in various mobility patterns using the Mininet-WiFi emulator. More importantly, we introduce q-ReLeS, an extension of an MPTCP scheduler called ReLeS for MPQUIC. The experimental results show that q-ReLeS reduces the download time from to compared to the others. Besides, the empirical investigation demonstrates that mobility and velocity patterns substantially impact the performance of MPQUIC schedulers.
Exploring Participants’ Representations and Shifting Sensitivities in a Hackathon for Dementia

Recent HCI research has addressed emerging approaches for public engagement. One such public-facing method which has gained popularity over the previous decade has been open design events, or hackathons. In this article, we report on DemVR, a hackathon event that invited designers, technologists, and students of these disciplines to design Virtual Reality (VR) environments for people with dementia and their care partners. While our event gained reasonable attraction from designers and developers, this article unpacks the challenges in representing and involving people with dementia in these events, which had multiple knock-on effects on participant's outputs. Our analysis presents insights into participants’ motivations, challenges participants faced when constructing their “absent user”, and the design features teams developed to address the social context of the user. We conclude the article by proposing a set of commitments for collaborative design events, community building through design, and reification in design.
Implementation of a RISC-V SMT Core in an AI processor

The RISC-V core, which supports simultaneous multithreading (SMT) on a heterogeneous virtual engine architecture, has been implemented. In the architecture, multiple types of engines specialized for different purposes are included. The RISC-V SMT core has the advantage of allowing multiple threads to execute simultaneously with a lower cost than that of simply using multiple cores. The RISC-V core supports operations of other engines under the SMT mechanism to be installed in “Chichibu,” which has been developed by ArchiTek Co., LTD as a multicore chip for edge artificial intelligence. In this implementation, we have reduced the hardware resource usage to less than half that of the previous implementation and exceeded the operating frequency of 100 MHz. In addition, the instructions per clock cycle (IPC) has been improved by approximately 66% using SMT.
A Method of Mutating Windows Malwares using Reinforcement Learning with Functionality Preservation

Recently, the development in both the quantity and complication of malware has raised a need for powerful malware detection solutions. The outstanding characteristics of machine learning (ML) and deep learning (DL) techniques have been leveraged in the fight against malware. However, they are proven to be vulnerable to adversarial attacks, where intended modifications in malware can flip the detection result and then evade the detector’s eyes. This research area is being focused on and deeply interested in many publications due to its significance in the evaluation of malware detection approaches. In such works, using Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can help malware authors craft metamorphic malware against antivirus. Unfortunately, the functionality of created malware is not mentioned and verified during the mutation phase, which can result in evasive but useless malware mutants. In this paper, we focus on Windows Portable Executable malware and propose an RL-based malware mutant creation approach to fool black-box static ML/DL-based detectors. Specifically, we introduce a validator to confirm functionality preservation, which is one of our requirements for a successfully created malware. The experiment results prove the effectiveness of our solution in crafting elusive and executable Windows malware mutants.
Prototype of a parking system with path recommendation

With the growth of population and vehicles, parking is a significant problem people face in modern life, especially in developed countries. It is often time-consuming and inconvenient for the driver to find a parking space in the parking lot, especially during rush hour. Therefore, this paper suggested a parking prototype that uses the YOLOv5s algorithm to detect vehicles and empty slots, and the Deep SORT algorithm will track the detected cars in moving frames. In addition, this prototype may use the BFS algorithm to recommend the path to the nearest available parking space for a user through a developed user interface. Our experimental results with different video situations showed that the proposed prototype achieved 0.906 mAP for average accuracy and an operating speed of 18 FPS for ten vehicles using Nvidia GTX 1080 GPU in the demo application.
Leveraging AI-Driven Realtime Intrusion Detection by Using WGAN and XGBoost

Currently, pattern-based detection is difficult to detect new network attacks with signatures. Thus, using machine learning is an approach proposed by many researchers for intrusion detection systems to deal with this issue. This paper presents a hybrid method combining a rule-based inspector with an AI-driven model, namely WGID, to improve intrusion detection performance. In this method, traffic flows that are not triggered by any rule of the rule-based inspector will be deeply analyzed by the WGID-based inspector. WGID comprises the TWGAN algorithm to generate more coherent samples based on the WGAN to tackle the imbalanced dataset. Based on the training dataset augmented by TWGAN, WGID adopts the XGBoost method to perform the deep analysis. To demonstrate the WGID performance, we conduct different rigorous experiments to evaluate WGID using three well-known datasets. The results indicate that the WGID achieves an excellent accuracy of , , and with the CSE-CIC-IDS2018, NSL-KDD, and UGR datasets, respectively. It also performs better than related models using the same datasets. Moreover, the deep inspection time for each traffic flow is also small enough to detect intrusions in the inline mode (i.e., average 1.892μs/flow).
A robust and high-performance neural network for classifying landmarks in upper gastrointestinal endoscopy images

Gastrointestinal Endoscopy in real-time needs to be done quickly and accurately. During an endoscopic examination, the technical doctor must spend a lot of time classifying anatomical landmarks before proceeding with further work such as lesion detection or abnormal region segmentation. The endoscopist also can ensure the completion of the examination with the help of the anatomical landmarks. In this study, we develop a robust and high-performance neural network classifying ten anatomical landmarks in the Upper Gastrointestinal tract. To this end, the proposed method consists of a Siamese Neural Network (SNN) to learn an efficient (dis)similarity metric of a pair of landmarks. We then propose to use the weight that is learned by SNN to a CNN-based structure for the feature extraction. The proposed CNN-based architecture is a lightweight model of Resnet-18 that is designed to achieve the best performance as well as a low cost of computational time. Finally, we utilize an SVM classifier to automatically classify ten anatomical landmarks from the Upper Gastrointestinal endoscopic images. The dataset used in this study is collected in four lighting modes such as (WLI) White-light Imaging, FICE (Flexible spectral imaging color enhancement), BLI (Blue Light Imaging), and LCI (Linked color imaging). The dataset is labeled by endoscopists with more than five years of experience. To enrich the dataset, we also research and evaluate augmentation techniques as effectively as possible. The augmented data includes a combination of the geometrical transforms and the data generated using CycleGAN deep learning networks. The proposed method works well with four lighting modes without depending on a certain lighting mode. It achieved 96.3% sensitivity, 99.6% specificity, and 99.3% accuracy. As a result of the computational time, we achieved approximately 60 FPS (Frame Per Second) using 20W average power with the deep learning framework Pytorch on the Jetson Xavier AGX.
Angular distribution and polarization of photoelectrons in the region of resonances

General expressions for the angular distribution and polarization of photoelectrons in the region of autoionizing states have been obtained. It is shown that when an autoionizing state has two or more channels of decay a resonance structure can occur in both the angular distribution and the polarization. For the autoionizing states nsnp6mp of Ne and Ar, possible shapes for the energy dependence of the angular distribution have been discussed. This dependence is sensitive to the relation between the matrix elements which characterize excitation and decay of the autoionizing state through the different channels.
Methods of Calculating Usefulness Ratings of Behavioral Facilitation Tweets in Disaster Situations

When a severe disaster strikes, people transmit various information about the disaster. This kind of information is circulated especially on Twitter, an extremely popular social networking service. Especially, information that encourages people to take some kind of action or not to take any action is called “behavioral facilitation information”, which is known to influence the behaviors of many people. However, the volume of such information is enormous. Information transmitted by numerous unspecified people is not always useful for various victims. As described herein, we propose methods for calculating the usefulness ratings of tweets that include behavioral facilitation information, and which are posted during earthquake disasters. Specifically, we present several methods for calculating the usefulness ratings of tweets as real numbers from 0 to 3 according to a four-step scale (not useful at all, not very useful, somewhat useful, very useful). Then we compare the methods in terms of accuracy. Based on that comparison, the most suitable method for earthquake disasters can be determined. According to an accuracy evaluation experiment using behavioral facilitation tweets actually posted when a certain earthquake occurred, the method using Bidirectional Encoder Representations from Transformers (BERT) achieved the best accuracy (average RMSE or Root-Mean-Square Error calculated using five-fold cross-validation) of 0.39, which is sufficiently high even for unknown data. This result implies that the method can achieve sufficiently high accuracy, even for unknown data.
Fear of COVID-19 and job insecurity among hospitality industry employees: The mediating role of happiness

Worldwide, COVID-19 had an unprecedented impact on the lives of people. This study explored the association between the fear of COVID-19 and job insecurity of Turkish hospitality employees, as well as the role of work happiness in that relationship. Survey data were collected from 417 employees (female = 53.24%; managers = 23%; age range = 19 to 63 years). SPSS-23 and AMOS-18 were used to test the research hypotheses using structural equation modelling and regression analysis. The findings indicated that fear of COVID-19 was associated with high job insecurity among employees from all levels. Work happiness played a mediating role in this relationship, lowering levels of fear and insecurity. These findings suggest that happiness is protective of fear of COVID-19 and job insecurity of employees. Management may consider boosting employee happiness to create a resilient workforce in the face of COVID-19.
Driving quality improvement with nudges: True interventions in cardiology

Key Points Quality improvement research brings evidence‐based interventions to clinical practice. This single‐center study demonstrated that simple clinical guidelines, computer prompts, and education were able to reduce the duration of triple therapy after percutaneous coronary intervention. Behavioral interventions are an important tool to drive clinical improvement but must be tailored to respect physician autonomy and professionalism.
Direct mask-free patterning of molecular organic semiconductors using organic vapor jet printing

We demonstrate the solvent-free, high-resolution direct printing of molecular organic semiconductors for use in low cost optoelectronic applications. In this method, called organic vapor jet printing, hot inert carrier gas picks up the molecular organic vapor and expands it through a microscopic nozzle, resulting in physisorption of the molecules onto a cooled substrate. Pattern resolution and printing speed are determined by the nozzle shape, nozzle-to-substrate distance, downstream pressure, and molecular mass of the carrier gas. Quantitative models are developed using a combination of scaling analysis, direct simulation Monte Carlo modeling, and printing experiments. Pattern resolutions of up to 1000dpi and local deposition rates exceeding 2300A∕s are achieved. Pentacene channel thin film transistors are printed at a local deposition rate of 700A∕s at both low and atmospheric pressures, resulting in a field-effect mobility of 0.25cm2∕Vs and a current on/off ratio of 7×105 for devices grown at a backgro.
Detecting the Semantic Shift of Values in Cultural Heritage Document Collections (short paper)

The paper presents the main features and goals of the EU H2020 VAST ( Values Across Space and Time ) project about the transformation of moral values across space and time, with particular emphasis on the core European Values that represent the essential pillars of the EU society. In particular, we discuss the preliminary results obtained by analysing a selected collection of historical documents by employing machine learning techniques. The aim is to classify document annotations and their relationships with values to discover possible shifts in the value meaning when different temporal contexts are considered