Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep-Learning based Spectrum Prediction for Cognitive Automotive Radar Interference Mitigation

Interference affects the robustness of driver assistance systems that rely on automotive radars. Today's radar sensors use algorithms for suppression of interference in the received radar signal. In contrast, interference-aware cognitive radar (IACR) avoids interference based on spectral awareness, as opposed to suppressing it in post-processing. In this paper, we propose a machine learning (ML) based spectrum prediction model for IACR that infers the spectral occupation in the upcoming measurement cycle. Subsequently, interference is avoided by adapting the waveform to use the spectral resources with the least interference potential. By closing the cognitive loop, we show that IACR is capable of achieving an order of magnitude less average interference power compared to radars without a cognitive interference avoidance strategy.
Local Theta Correspondences of Supercuspidal Representations

By the works of Yu, Kim and Hakim-Murnaghan, we have a parameterization and construction of all supercuspidal representations of a reductive $p$-adic group in terms of supercuspidal data, when $p$ is sufficiently large. In this paper, we will define a notion of correspondence of supercuspidal data via moment maps and theta correspondences over finite fields. Then we will show that local theta correspondences between supercuspidal representations is completely described by this notion. In Appendix B, we give a short proof of a result of Pan on "depth preservation".
Investigating adversarial attacks against Random Forest-based network attack detection systems

A significant research effort in cybersecurity currently deals with Machine Learning-based attack detection. It is aimed at providing autonomous attack detection systems that require less human expert resources, and are then less expensive in time and money. Indeed, such systems are able to autonomously learn about benign and malicious traffic, and to classify further traffic samples accordingly. In such context, hackers start designing adversarial learning approaches in order to design new attacks able to evade from the Machine Learningbased detection systems. The work presented in this paper aims at exhibiting how easy it is to modify existing attacks to make them evade from the Machine Learning-based attack detectors. The Random Forest algorithm has been selected for this work as it is globally evaluated as one of the best Machine Learning algorithm for cybersecurity, and it provides informations on how a decision is made. Indeed, the analysis of the related Random Forest trees helps explaining the limits of this Machine Learning algorithm, and gives some information that could be helpful for making attack detection somewhat explainable. Several other Machine Learning algorithms as SVM, kNN ans LSTM have been selected for evaluating their ability to detect the adversarial attack presented in this paper.
Combinatorial-Based Auction for the Transportation Procurement: An Optimization-Oriented Review

This paper conducts a literature review on freight transport service procurements (FTSP) and explores the application of combinatorial auctions (CAs) mechanism and the mathematical modeling approach of the associated problems. It provides an overview of modeling the problems and their solution strategies. The results demonstrate that there has been limited scholarly attention to sustainable issues, risk mitigation and the stochastic nature of parameters. Finally, several promising future directions for FTSP research have been proposed, including FTSP for green orientation in the context of carbon reduction, shipper’s reputation, carrier collaboration for bid generation, etc.
Passive Thermal Performance of Earth-Sheltered Thin-Shell Concrete Dome Structures

The objective of this research was to evaluate an insulated concrete thin-shell dome structure for thermal flux with various depths of soil coverings. Computational thermal analysis was performed to model temperature fluc- tuations inside a representative section of an experimental concrete dome structure during typical winter and summer seasons in northern Utah. The results are compared against the baseline of a traditional insulated wood-framed building envelope and indicate that thermal flux is significantly reduced in the earth-sheltered concrete dome structure with foam on the outside of the concrete. The addition of soil dampens the amplitude of the flux and increases the time lag between maximal exterior and interior temperatures.
Activity During High-Repetition Practice of Syntax

Recent work has shown that repetitive, low cognitive load exercises may have benefits in gaining mastery of computer programming language syntax as well as positive scaffolding effects when learning problem solving. This paper presents results from a qualitative think-aloud study that explores two questions relating to what types of activities students engage in while completing syntax exercises and how the distribution of those activities changes with practice. We coded, with high temporal resolution, activities that study participants engaged in while completing syntax exercises. We find that students can gain mastery of simpler syntactic constructs through practice, while learning of more complex constructs can benefit from debugging and referring to previously completed exercises. In all cases, however, students appear to achieve proficiency in writing constructs after surprisingly few exercises. Our results suggest that a high-repetition learning framework for syntax meets stated goals of a safe and productive environment for students to learn syntax. We expect that our results will be generalizable to other settings in which practice is utilized as a pedagogical tool.
Image Restoration for Blast Furnace Burden Surface Based on Dust Multiscattering Model

A clear image of the blast furnace burden surface (BF-BS) is essential for monitoring abnormal furnace conditions, optimizing burden distribution operation, and ensuring stable operation of the blast furnace. Aiming at the problem that the quality of BS image is seriously degraded due to the strong light interference and dust particle scattering, a novel image restoration method for BF-BS is presented in this article. First, a structure-perceivable variational model is designed to extract the dust layer from the original image. Second, a dust multiscattering model is established based on the radiation transfer theory, and then, the image degradation model of the BS is further derived, which quantitatively describes the relationship between the degraded and restored images. On this basis, an image restoration algorithm is proposed to estimate the unknown parameters in the model so as to recover the BS image with high visibility. The experimental results show that the method can effectively improve image quality, enhance overall brightness, remove the influence of dust, and restore the detail and edge information of the BS. The restored image of the BS has important guiding significance for implementing fine regulation of the blast furnace. In addition, this method can be applied to image restoration and object detection in harsh industrial environments.
Adiabatic Homogeneous Model for Flow Around a Multiperforated Plate

An adiabatic homogeneous model to account for multiperforated liners in combustion chamber flow simulations is described. It is based on a suction and an injection model to reproduce the average effect of effusion cooling on both sides of the plate. The coupled suction/injection model has been specifically designed to be used in industrial full-scale computations of gas turbine combustion chambers, where effusion cooling is commonly used for controlling the temperature of the liners. Notably, it can be used with a coarse grid, the real perforated plate being replaced by a homogeneous boundary condition where the model is applied. The new modeled boundary condition conserves the inviscid part of the wall fluxes, which are shown to be the main contribution, as evidenced by the analysis of former wall-resolved simulations. Conserving the wall fluxes allows reproduction of the global structure of the flow and leads to reasonable comparisons with experimental data. Hence, the proposed new model provides a practical way to account for multiperforated plates with inclined perforations without resolving the flow in the perforations.
Light-Weight Learning Model with Patch Embeddings for Radar-based Fall Event Classification: A Multi-domain Decision Fusion Approach

With the increasing morbidity and mortality rate in older adults above 65 years of age due to accidental fall, privacy-preserving radar-based fall event detection is becoming crucial. Deep learning algorithm like vision transformers (ViT) for human fall-event detection using different radar domain representation have shown excellent fall-detection accuracy. However, such techniques are computationally very expensive and unsuitable when training datasets are small. Patch-based learning models such as Multi-Layer Perceptron-Mixer (MLP-Mixer) and Convolutional-Mixer (ConvMixer) models have been developed as alternatives to ViT. In this work, the decision outputs of light-weight ConvMixer models with different domain representations of radar returns as inputs are fused for classifying the events as fall or non-fall. This proposed approach of event classification utilizes supplementary information present in different domains for enhancing the classification accuracy. Evaluation done on publicly available dataset shows an improved performance of the multi-domain ConvMixer model over ViT and MLP-Mixer. This further justifies the choice of light weight ConvMixer as a preferred learnable model when only limited training dataset is available.
Understanding Controlled Ion Doping Mechanism of Vertical Sensing Electrochemical Random Access Memory Using Ion-Permeable Graphene Electrodes

In this article, we propose vertical sensing electrochemical random-access memory (VS-ECRAM) with improved synaptic behaviors for the neuromorphic application. This new type of ECRAM uses a monolayer graphene layer located between the electrolyte and channel layer, serving as both a barrier and a drain electrode. Controlled ion doping behavior was achieved through the barrier effect of monolayer graphene, resulting in excellent weight-update linearity. The constant ion injection and uniform ion distribution in the channel were verified through device simulation using ionic current mapping. In addition, the graphene barrier was effective in ensuring robust endurance and retention by obstructing ion mixing at the interface between the channel and electrolyte. Furthermore, based on the excellent scalability of the vertical channel structure, the device was scaled down to <inline-formula> <tex-math notation="LaTeX">$30\times30$ </tex-math></inline-formula> nm<sup>2</sup>, resulting in reduced read/write energy consumption (<inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>5 fJ/pulse) and improved read latency (<inline-formula> <tex-math notation="LaTeX">$\times 20$ </tex-math></inline-formula>). Finally, a <inline-formula> <tex-math notation="LaTeX">$32\times32$ </tex-math></inline-formula> VS-ECRAM array was fabricated with <inline-formula> <tex-math notation="LaTeX">$4{F}^{\,{2}}$ </tex-math></inline-formula> of cell layout and a high Modified National Institute of Standards and Technology (MNIST) pattern recognition accuracy (95.39%) was evaluated with near-ideal synaptic characteristics.
The Systematic Mapping of Inter-organizational Strategic Relationship Studies: A Modern Business Administration Approach

Despite the importance of inter-organizational strategic relationships in reaching new sources of innovation and knowledge in modern business administration, this concept was unknown up to the 1980s and has become far more dominant in the last twenty years. The primary purpose of the present research is to provide a summation of the current literature that has studied the process and role of strategic relationships among organizations. This study used a systematic mapping review; and analyzed 93 high-quality peer-reviewed scholarly papers from several aspects. To analyze the articles, a categorized scheme was developed. The papers have been analyzed based on their type of relation, publication features, and research methodology. These aspects can help future readers comprehensively understand the nature of inter-organizational strategic relationships. Managerial implications have been provided besides a discussion of the study's findings.
Cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables

High-cost and physical-limitations limit the use of Electroencephalogram sensing (EEG) with respect to Affective Computing. Wrist-worn Wearable health devices, an intriguing alternative, are becoming cheap, inconspicuous, and common-place - driving down the cost of heart-rate, accelerometer, respiration, and similar sensing. In this paper, we describe a quantitative empirical study investigating the use of low-cost sensing as a replacement for EEG. Our results show significant correlation between wrist-sensed, low-cost physiological data features and established EEG measures of emotion and cognition. This work demonstrates the value of inexpensive sensors capable of inferring emotion and cognition in a wrist-worn form-factor.
A Novel Deep Learning Architecture With Image Diffusion for Robust Face Presentation Attack Detection

Face presentation attack detection (PAD) is considered to be an essential and critical step in modern face recognition systems. Face PAD aims at exposing an imposter or an unauthorized person seeking to deceive the authentication system. Presentation attacks are typically made using a fake ID through a digital/printed photograph, video, paper mask, 3D mask, and make-up etc. In this research, we propose a novel face PAD solution using an interpolation-based image diffusion augmented by transfer learning of a MobileNet convolutional neural network. The proposed interpolation-based image diffusion method and face PAD approach, implemented in a single framework, shows promising results on various anti-spoofing databases. The experimental results illustrate that the proposed face PAD method shows superior performance compared to most of the state-of-the-art methods.
Research on Autonomous Decision-Making in Manned/Unmanned Coordinated Air Combat

The manned/unmanned coordinated swarm is a typical type of aviation swarm, and its application in modern air combat can bring into play the performance advantages of manned platforms and the scale advantages of the number of unmanned aircraft, and bring into play the overall combat effectiveness through manned/unmanned coordination. This paper focuses on the key technologies for autonomous decision-making in manned/unmanned collaborative air combat. Firstly, the operational processes of defensive and offensive cooperative air combat are analysed in a given operational mission scenario, and then the key technologies of cooperative air combat are presented in three aspects: tactical guidance-oriented pre-engagement formation operations, real-time target allocation based on unbalanced mission assignment and tracking and guidance methods based on the advantages of cooperative guidance. Finally, the simulation results show that the key techniques of autonomous decision making given in this paper can effectively carry out strikes against the enemy and improve our victory rate.
C-HAR: Compressive Measurement-Based Human Activity Recognition

In this paper we present C-HAR, a low-cost,low-complexity, compressive measurement-based human activity recognitionframework for embedded devices, capable of simultaneously filtering andacquiring the sensor readings of a smartwatch's accelerometer and gyroscope atsub-Nyquist rates. A software simulation of the full system obtains an accuracyof 92.0% and 88.0% for the accelerometer-based and gyroscope-based systemsrespectively, obtained at a 5 Hz sample rate. A microcontroller implementationof the system's back-end obtains an accuracy of 90.3% and 87.1% for theaccelerometer-based and gyroscope-based systems respectively with a runtimeover twice as fast as that of a comparable baseline system.
Original Research: Can a Palliative Care Lay Health Advisor–Nurse Partnership Improve Health Equity for Latinos with Cancer?

ABSTRACT Purpose: A palliative care infrastructure is lacking for Latinos with life-threatening illness, especially in rural regions of the United States. The purpose of this study was to develop and evaluate a community-based palliative care lay health advisor (LHA) intervention for rural-dwelling Latino adults with cancer. Methods: An exploratory mixed-methods participatory action research design was carried out by an interprofessional research team that included community and academic members. Fifteen Latino community leaders completed a 10-hour palliative care training program and then served as palliative care LHAs. Although 45 Latinos with cancer initially agreed to participate, four withdrew or died and six were not reachable by the LHAs, for a final total of 35 patient participants. The trained palliative care LHAs delivered information on home symptom management and advance care planning to assigned participants. Palliative care nurses led the training and were available to the LHAs for consultation throughout the study. The LHAs made an average of three telephone calls to each participant. The Edmonton Symptom Assessment System–Revised (ESAS-r) and the four-item Advance Care Planning Engagement Survey (ACPES-4) were administered pre- and postintervention to determine the intervention's effectiveness. Encounter forms were transcribed, coded, and analyzed using case comparison. Results: The major finding was that significant improvements were shown for all four items of the ACPES-4 among both the LHAs (posttraining) and the participants (postintervention). Information on advance care planning was shared with 74.3% of the 35 participants. Participants showed clinical improvement in physical symptom scores and clinical deterioration in emotional symptom scores following the intervention, although these changes did not reach statistical significance. The advisors noted that participants were anxious about how to explain cancer to children, the uncertainty of their prognosis, and medical expenses. This sample was younger than those of other cancer studies; 51.4% were under age 50 and 73.1% had at least one child in the home. Conclusions: A community-based palliative care LHA–nurse partnership was shown to be a feasible way to engage in conversations and deliver information about advance care planning to rural-dwelling Latino adults with cancer. The positive results led to the regional cancer center's decision to select “cultural care” as its 2022 goal for maintaining its accreditation with the Commission on Cancer. The authors describe the implementation and assessment of a community-based palliative care intervention to deliver information about advance care planning to rural-dwelling Latino adults with cancer.
CNS Manifestations in Mucolipidosis Type II—A Retrospective Analysis of Longitudinal Data on Neurocognitive Development and Neuroimaging in Eleven Patients

Mucolipidosis type II (MLII), an ultra-rare lysosomal storage disorder, manifests as a fatal multi-systemic disease. Mental inhibition and progressive neurodegeneration are commonly reported disease manifestations. Nevertheless, longitudinal data on neurocognitive testing and neuroimaging lack in current literature. This study aimed to provide details on central nervous system manifestations in MLII. All MLII patients with at least one standardized developmental assessment performed between 2005 and 2022 were included by retrospective chart review. A multiple mixed linear regression model was applied. Eleven patients with a median age of 34.0 months (range 1.6–159.6) underwent 32 neurocognitive and 28 adaptive behaviour assessments as well as 14 brain magnetic resonance imagings. The scales used were mainly BSID-III (42%) and VABS-II (47%). Neurocognitive testing (per patient: mean 2.9, standard deviation (SD) 2.0) performed over 0–52.1 months (median 12.1) revealed profound impairment with a mean developmental quotient of 36.7% (SD 20.4) at last assessment. The patients showed sustained development; on average, they gained 0.28 age-equivalent score points per month (confidence interval 0.17–0.38). Apart from common (63%) cervical spinal stenosis, neuroimaging revealed unspecific, non-progressive abnormalities (i.e., mild brain atrophy, white matter lesions). In summary, MLII is associated with profound developmental impairment, but not with neurodegeneration and neurocognitive decline.
Physiological and Histological Characterization of the ESB1 TILLING Mutant of Brassica rapa L.: Potential Use in Biofortification and Phytoremediation Programs

Enhanced suberin1 (ESB1) is a protein whose mutation is correlated with an increase in root suberin and altered nutrient concentrations. Here, we show a physiological and histological characterization of esb1 mutant plants of Brassica rapa L. Therefore, the potential use of this mutant in selenium (Se) biofortification and/or cadmium (Cd) phytoremediation programs was also evaluated by applying 20 μM of Na2SeO4 and 0.49 μM of CdCl2 to a nutrient solution. With respect to wild type (WT) plants, an increase in root suberin was observed in esb1 at the level of the exodermis. This increase in root suberin did not affect photosynthesis performance. However, the esb1 mutant showed an increase in transpiration rate and a decrease in water use efficiency. Additionally, root histological changes affected the transport and concentration of some mineral elements. Thus, our results suggest that esb1 mutants of B. rapa would not be useful for Se biofortification because no significant differences were observed between the two genotypes at the leaf level. Nevertheless, the esb1 mutant reduced Cd translocation to the leaves and increased Fe and Cu uptake, so ESB1 mutation could be useful for Cd phytoremediation and Fe and Cu biofortification, although further research is needed. Therefore, this study provides detailed information on the effect of ESB1 mutation in B. rapa and suggests its potential use in biofortification and phytoremediation programs.
Development of Thermally Stable Nanobodies for Detection and Neutralization of Staphylococcal Enterotoxin B

In this study, sixteen unique staphylococcal enterotoxin B (SEB)-reactive nanobodies (nbs), including ten monovalent and six bivalent nbs, were developed. All characterized nbs were highly specific for SEB and did not cross-react with other staphylococcal enterotoxins (SE). Several formats of highly sensitive enzyme-linked immunosorbent assays (ELISAs) were established using SEB nbs and a polyclonal antibody (pAb). The lowest limit of detection (LOD) reached 50 pg/mL in PBS. When applied to an ELISA to detect SEB-spiked milk (a commonly contaminated foodstuff), a LOD as low as 190 pg/mL was obtained. The sensitivity of ELISA was found to increase concurrently with the valency of nbs used in the assay. In addition, a wide range of thermal tolerance was observed among the sixteen nbs, with a subset of nbs, SEB-5, SEB-9, and SEB-62, retaining activity even after exposure to 95 °C for 10 min, whereas the conventional monoclonal and polyclonal antibodies exhibited heat-labile properties. Several nbs demonstrated a long shelf-life, with one nb (SEB-9) retaining 93% of its activity after two weeks of storage at room temperature. In addition to their usage in toxin detection, eleven out of fifteen nbs were capable of neutralizing SEB’s super-antigenic activity, demonstrated by their inhibition on IL-2 expression in an ex vivo human PBMC assay. Compared to monoclonal and polyclonal antibodies, the nbs are relatively small, thermally stable, and easy to produce, making them useful in applications for sensitive, specific, and cost-effective detection and management of SEB contamination in food products.