Miracle-dz/UniFAR-FTUs huggingface dataset:
AVTPnet: Convolutional Autoencoder for AVTP anomaly detection in Automotive Ethernet Networks

—Network Intrusion Detection Systems are well considered as efﬁcient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattack are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system (IDS) which play an important role in detecting unknown attack patterns in network trafﬁc. To our knowledge, no previous research work has been done to detect anomalies on automotive ethernet based in-vehicle networks using anomaly based approaches. Hence, in this paper, we propose a convolutional autoencoder (CAE) for ofﬂine detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent in-vehicle network Automotive Ethernet. The CAE consists of an encoder and a decoder with CNN structures that are asymmetrical. Anomalies in AVTP packet stream, which may lead to critical interruption of media streams, are therefore detected by measuring the reconstruction error of each sliding window of AVTP packets. Our proposed approach is evaluated on the recently published ”Automotive Ethernet Intrusion Dataset”, and is also compared with other state-of-the art traditional anomaly detection and signature based models in machine learning. The numerical results show that our proposed model outperfoms the other methods and excel at predicting unknown in-vehicle intrusions, with 0.94 accuracy. Moreover, our model has a low level of false alarm and miss detection rates for different AVTP attack types.
Auto-ABSA: Automatic Detection of Aspects in Aspect-Based Sentiment Analysis

Following the proposal of the transformer, many pre-trained language models were developed, and the sentiment analysis (SA) task was improved.In this paper, we proposed a method that uses an auxiliary sentence to describe aspects that the sentence contains to help sentiment prediction. The ﬁrst is aspect detection, which uses a multi-aspects detection model to predict all the aspects that the sentence has. Combining the predicted aspects and the original sentence as the Sentiment Analysis (SA) model’s input. The second is to do out-of-domain aspect-based sentiment analysis (ABSA), train a sentiment classiﬁcation model with one kind of dataset and validate it with another kind of dataset. Finally, we created two baselines, they use no aspect and all aspects as the sentiment classiﬁcation model’s input, respectively. Compared two baselines’ performance to our method, we found that our method really makes sense.
A Novel Method to Estimate Tilt Angle of a Body using a Pendulum

Most of the advanced control systems use sensorbased feedback for robust control. Tilt angle estimation is key feedback for many robotics and mechatronics applications in order to stabilize a system. Tilt angle cannot be directly measured when the system in consideration is not attached to a stationary frame. it is usually estimated through indirect measurements in such systems. The precision of this estimation depends on the measurements; hence it can get expensive and complicated as the precision requirement increases. This research is aimed at developing a novel and economic method to estimate tilt angle with a relatively less sophisticated and complicated system, while maintaining precision in estimating tilt angle. The method is developed to explore a pendulum as an inertial measurement sensor and estimates tilt angle based on dynamics of pendulum and parameter estimation models. Further, algorithms are developed with varying order of complexity and accuracy to have customization for different applications. Furthermore, this study will validate the developed algorithms by experimental testing. This method focuses on developing algorithms to reduce the input measurement error in the Kalman filter.
Derivation functors and Lusztig's induction functors

Lusztig proved the compatibility of induction functors and restriction functors for Lusztig’s perverse sheaves. Fang-Lan-Xiao established a categorification of Green’s formula and gave a sheaf-level proof of this compatibility for all semisimple complexes. As an application, we study the relation between induction functors and derivation functors, which is a kind of special restriction functors.
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction

While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that without training the synthesis algorithm on the specific domain, our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario.
Ground Experiment of Full-Duplex Multi-UAV System Enabled by Directional Antennas

A high performance multi-UAV communication system, which bridges multiple UAVs and ground station, is one of the key enablers to realize a variety of UAV-based systems. To address the issues such as the low spectrum efficiency caused by the co-channel interference, we have proposed a spectrum-efficient full-duplex multi-UAV communication system with low hardware complexity. In this paper, on-ground experiments are conducted to confirm the feasibility and effectiveness of the key feature of the proposed system, i.e., co-channel interference cancellation among UAVs by directional antennas and UAV position control, instead of energy-consuming dedicated self-interference cancellers on UAVs in traditional full-duplex systems. Channel power of interference link between a pair of two UAVs reusing the same channel is measured, and the achievable channel capacity is also measured by a prototype system implemented by software-defined radio devices. The results of different antennas and different antenna heights are also compared. The experimental results agree well with the designs and confirm the feasibility and effectiveness of the proposed system. This ground experiment is a work in progress to provide preliminary results for the multi-UAV-based experiments in the air in the future.
5G enabled Mobile Edge Computing security for Autonomous Vehicles

The world is moving into a new era with the deployment of 5G communication infrastructure. Many new developments are deployed centred around this technology. One such advancement is 5G Vehicle to Everything communication. This technology can be used for applications such as driverless delivery of goods, immediate response to emergencies and improving traffic efficiency. The concept of Intelligent Transport Systems (ITS) is built around this system which is completely autonomous. This paper studies the Distributed Denial of Service (DDoS) attack carried out over a 5G network and analyses security attacks, particularly the DDoS attack. The aim is to implement a machine learning model capable of classifying different types of DDoS attacks and predicting the quality of 5G latency. The initial steps of implementation involved the synthetic addition of 5G parameters into the dataset. Subsequently, the data was label encoded, and minority classes were oversampled to match the other classes. Finally, the data was split as training and testing, and machine learning models were applied. Although the paper resulted in a model that predicted DDoS attacks, the dataset acquired significantly lacked 5G related information. Furthermore, the 5G classification model needed more modification. The research was based on largely quantitative research methods in a simulated environment. Hence, the biggest limitation of this research has been the lack of resources for data collection and sole reliance on online data sets. Ideally, a Vehicle to Everything (V2X) project would greatly benefit from an autonomous 5G enabled vehicle connected to a mobile edge cloud. However, this project was conducted solely online on a single PC which further limits the outcomes. Although the model underperformed, this paper can be used as a framework for future research in Intelligent Transport System development.
Reinforcement Learning with Heterogeneous Data: Estimation and Inference

Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains. Classical RL methods focus on the mean of the total return and, thus, may provide misleading results in the setting of the heterogeneous populations that commonly underlie large-scale datasets. We introduce the K-Heterogeneous Markov Decision Process (K-Hetero MDP) to address sequential decision problems with population heterogeneity. We propose the Auto-Clustered Policy Evaluation (ACPE) for estimating the value of a given policy, and the Auto-Clustered Policy Iteration (ACPI) for estimating the optimal policy in a given policy class. Our autoclustered algorithms can automatically detect and identify homogeneous sub-populations, while estimating the Q function and the optimal policy for each sub-population. We establish convergence rates and construct confidence intervals for the estimators obtained by the ACPE and ACPI. We present simulations to support our theoretical findings, and we conduct an empirical study on the standard MIMIC-III dataset. The latter analysis show evidence of value heterogeneity and confirms the advantages of our new method. 1 ar X iv :2 20 2. 00 08 8v 1 [ cs . L G ] 3 1 Ja n 20 22
Fundamental Performance Limits for Sensor-Based Robot Control and Policy Learning

—Our goal is to develop theory and algorithms for establishing fundamental limits on performance for a given task imposed by a robot’s sensors. In order to achieve this, we deﬁne a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples: (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).
Runtime Monitoring and Statistical Approaches for Correlation Analysis of ECG and PPG

Biophysical signals such as Electrocardiogram (ECG) and Photoplethysmogram (PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally, ECG and PPG are signals, which provide a"different window"into the same phenomena, namely the cardiac cycle. While they are used separately, there are no studies regarding the exact correction of the different ECG and PPG events. Such correlation would be helpful in many fronts such as sensor fusion for improved accuracy using cheaper sensors and attack detection and mitigation methods using multiple signals to enhance the robustness, for example. Considering this, we present the first approach in formally establishing the key relationships between ECG and PPG signals. We combine formal run-time monitoring with statistical analysis and regression analysis for our results.
Quality of evidence supporting the role of Chinese herbal medicine for the treatment of poststroke depression

Abstract Background: Poststroke depression (PSD) is a syndrome that occurs after stroke. The efficacy of Chinese herbal medicine (CHM) for PSD has also received widespread attention, but there is still a lack of clinical evidence because this overview evaluates the published meta-analyses (MAs)/Systematic reviews (SRs). To provide evidence for the clinical application of CHM in the treatment of PSD Methods: Two researchers searched 7 databases for SRs/MAs which are about randomized controlled trials on CHM for PSD. Two investigators use the systematic review assessment tool (AMSTAR-2), the risk of bias in systematic scale, the list of preferred reporting items for systematic reviews and meta-analysis, and the classification of recommended assessments for evaluation, development and evaluation system to assess the included SRs/MAs. Results: Our findings will be published in peer-reviewed journals. Conclusion: This study provides evidence-based medical evidence for the impact of CHM on PSD. Registration number: INPLASY202210001
A prospective, multicenter study of bortezomib, cyclophosphamide, and dexamethasone in relapsed/refractory iMCD

Abstract Relapsed and refractory (R/R) idiopathic Multicentric Castleman disease (iMCD) is a clinical challenge with few treatment options. In this first multicenter, prospective trial which implemented the recently published CDCN response criteria, we evaluated the efficacy and safety profiles of bortezomib-cyclophosphamide-dexamethasone (BCD) regimen in 24 R/R iMCD patients. By 6 months, 15 patients (62.5%) achieved overall treatment responses; four patients (16.7%) had stable disease and five patients (20.8%) suffered from progression of disease. Even when considering all patients, there were significant (p < .05) improvements in median symptom score, hemoglobin, platelet count, C-reactive protein (CRP) erythrocyte sedimentation rate (ESR), IL-6, albumin, and immunoglobin G (IgG) after treatment. The regimen was well tolerated without grade 3 or higher adverse events. Estimated 1-year progression-free survival (PFS) and overall survival (OS) were 79% and 92%, respectively. BCD regimen is an effective and safe treatment option for R/R iMCD patients. This trial was registered at www.chictr. org.cn as # ChiCTR1800019342.
Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction

Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.
An Experimental and Computational Study of Pulsed Detonations in a Single Tube

An experimental facility capable of operating multiple pulsed detonation tubes in cyclic fashion has recently been developed. The facility makes use of automotive valving via the head of a 4-cylinder Mitsubishi Eclipse engine. Thus far, cyclic detonations have been achieved in ethylene and propane air mixtures at frequencies up to 15 Hz. Numerical studies are also underway to compliment the experimental e orts. A computer program based on a Space-Time Conservation Element and Solution Element (CE/SE) method and a Low-Reynolds Number k turbulence model has been developed. Results of the simulations are presented at conditions similar to those in the experiments.
Canonical correlation analysis on the association between HIV-related stress and health-related quality of life among newly diagnosed people living with HIV

ABSTRACT The overall negative correlation between HIV-related stress and health related quality of life (HRQoL) among people living with HIV (PLWH) has been established, but less is known about the associations between them from various dimensions. We aimed to give a deep understanding of the relationship between these two multidimensional variables. A cross-sectional study of 557 PLWH with diagnosis less than 1 month was conducted. The HIV/AIDS Stress Scale (SS-HIV) and the Medical Outcomes Study HIV Survey (MOS-HIV) were used to assess the HIV-related stress and HRQoL, respectively. Canonical correlation analysis was performed to analyze their correlation. The association between HIV-related stress and HRQoL among PLWH was mainly determined by the emotional stress and four HRQoL dimensions including health transition, heath stress, mental health function and the attitude towards general quality of life, which should be taken as important considerations in the management of HIV.
Therapeutic Potential and Ethnopharmacology of Dominant Mangroves of Bhitarkanika National Park, Odisha, India

Bhitarkanika National Park is the second largest contiguous mangrove forest of India. Approximately 0.15 million mangrove depending population are found residing in and around 307 villages within the National Park. Despite being one of the most diverse mangrove habitations of India, the ethnopharmacological practices are meager in comparison to the other mangrove regions of India and Southeast Asia. The present review is aimed to congregate information on the therapeutic potential and ethnopharmacology of nine dominant mangrove species of the National Park, such as Aegiceras corniculatum, Avicenia marina, Avicenia officinalis, Ceriops decandra, Excoecaria agallocha, Heritiera fomes, Lumnitzera racemosa, Rhizophora mucronata, and Sonneratia apetala. Our aim is to generate social awareness among the mangrove dwellers to promote uses of folklore medicine using these tremendously potential mangrove plants, as a complementary step to strengthen community health. Further, we also want to grab the attention of researchers working in related disciplines, for their holistic and extensive studies towards bio‐prospectation of the dominant mangrove plants of Bhitarkanika National Park.
Gut Microbial Metabolite Trimethylamine N-Oxide Aggravates Pulmonary Hypertension.

Trimethylamine N-oxide (TMAO), a metabolite derived from intestine microbial flora, enhances vascular inflammation in a variety of cardiovascular disease, and the bacterial communities associated with trimethylamine N-oxide (TMAO) metabolism is higher in pulmonary hypertension (PH) patients. The effects of TMAO on PH, however, has not been elucidated. In the present study, we found that circulating TMAO was elevated in intermediate to high-risk PH patients when compared to healthy control or low-risk PH patients. In monocrotaline-induced rat PH models, circulating TMAO was elevated; and reduction of TMAO using 3,3-dimethyl-1-butanol (DMB) significantly decreased right ventricle systolic pressure, pulmonary vascular muscularization in both monocrotaline-induced rat PH and hypoxia induced mice PH models. RNA sequencing of rat lungs revealed that DMB treatment significant suppressed the pathways involved in cytokine-cytokine receptor interaction, and cytokine and chemokine signaling. Protein-protein interaction analysis of the differentially expressed transcripts regulated by DMB showed 5 hub genes with a strong connectivity of proinflammatory cytokines and chemokines including Kng1, Cxcl1, Cxcl2, CxcL6 and Il6. In vitro, TMAO significantly increased the expression of Kng1, Cxcl1, Cxcl2, CxcL6 and Il6 in bone marrow derived macrophage. And TMAO-treated conditioned medium from macrophage increased the proliferation and migration of pulmonary artery smooth muscle cells; but TMAO treatment did not change the proliferation or migration of pulmonary artery smooth muscle cells. In conclusion, our study demonstrates that TMAO is increased in severe PH, and the reduction of TMAO decreases pulmonary vascular muscularization and alleviates PH via suppressing the macrophage production of chemokines and cytokines.
Janus 2H-VSSe monolayer: two-dimensional valleytronic semiconductor with nonvolatile valley polarization

Valleytronic as a hot topic in recent years focuses on electrons’ valley degree of freedom as a quantum information carrier. Here, by combining two-band k.p model with high-throughput density functional theory (DFT) calculations, the valley states of Janus 2H-VSSe monolayer are studied which have spontaneous polarization. Nonvolatile valley polarization state is mainly arises from intrinsic ferromagnetism contributed by V-3d electronic configuration and not the spontaneous out-of-plane dipole moment of VSSe monolayer. The effective Hamiltonian model and DFT calculations both showed that the valley splitting mainly originates from the smaller spin splitting coming from the spin–orbit coupling effect rather than the spin splitting of magnetic exchange field. By using the effective Dirac Hamiltonian and Kubo formula, we further calculated the longitudinal and transversal conductivities and absorption spectra of VSSe monolayer which exhibits an anomalous valley Hall effect and clear valley-selective circular dichroism. Our calculations indicate that the modification of valley and spin splitting related to Berry curvature by applying an external strain is more noticeable than by the change of the magnetic moment orientation and electric field. We found that carriers accumulation with particular spin and valley label can be manipulated by tuning effective Hamiltonian parameters. The coexistence of robust in-plane magnetic ordering and spontaneous valley polarization of 2H-VSSe monolayer supports the possibility of applications in spintronics, valleytronics and optoelectronics devices.