Miracle-dz/UniFAR-FTUs huggingface dataset:
EPCP: An Efficient Point Cloud Classification Network with Position Fusion

Recently, deep learning on 3D point cloud data has driven rapid development in the field of autonomous driving. However, processing 3D point clouds is a challenging task. Transformers are widely used in various fields of deep learning. In the community of 3D point clouds, Point Cloud Transformer(PCT, a transformer-based framework) also achieves excellent performance on 3D point cloud tasks. However, the well-known attention mechanism benefits performance improvement while it also leads to expensive computational costs. To alleviate the problem, we propose a novel structure named efficient Point Cloud Classification Network with Position Fusion(EPCP), which uses the strategy of fusing absolute coordinates-based features and relative position information instead of the attention mechanism. By fusing relative position information, our method ensures that the output features maintain different sensitivity to specific areas, which plays a similar role as the attention mechanism. To verify the effectiveness of our method, we conduct experiments on ModelNet40 and real-world ScanObjectNN datasets. Compared with PCT, our method maintains the same accuracy on ModelNet40 and improves by 5.5% on ScanObjectNN. At the same time, our method reduces the number of parameters by 91% and FLOPs by 50% compared to PCT on the ModelNet40 dataset.
A Wide-band Millimeter Wave RWG to Air-Filled SIW Transition

In this paper, a simple linearly tapered transition from a rectangular waveguide (RWG) to an air-filled SIW (AFSIW) is designed by analytically formulating its length, which optimizes the impedance matching and insertion loss over the wide millimeter wave frequency range. Several of these transitions are used to realize back-to-back and transmission systems supporting different waveguides. Their performances in terms of S11 and S21 are simulated and found to be <-10 dB and >-0.21 dB across the frequency bands of operation, respectively. The prototype of the transition in transmission system configuration supporting the WR-15 waveguide is developed, and its measured values from 50 to 75 GHz correlate very well with the simulated results. When the measured S21 and BW of the developed prototype are compared to similar works reported in the literature, the benefits of the proposed transition are confirmed for developing low-cost future generations of wireless technology systems.
Evaluating ML Algorithm precision over indoor thermal comfort through comparison of popular metrics

Predicting the temperature in indoor spaces is essential for upholding cozy living and working conditions as well as for assuring the security and preservation of delicate tools and materials. This parameter's forecast beforehand can aid in reducing hazards, enhancing operations, and improving energy efficiency. In this paper, data retrieved from sensors, positioned within a particular building, has been simulated through three machine learning ML models. Machine Learnings (ML) become a potent tool for temperature prediction based on historical data, weather patterns, and other related elements in recent years. We evaluated the performance of three machine learning (ML) models using a train and test dataset. This paper focuses on identifying the best model selection comparing the three chosen models.
Kex at the SBFT 2023 Java Tool Competition

Kex is a platform for analysis of JVM programs which mainly focuses on automatic test generation with the aim to maximize branch coverage criterion. Kex takes a set of compiled classes as an input and uses symbolic execution to analyze control flow graphs of the program under test (PUT). Symbolic engine produces a set of interesting inputs which are converted into a JUnit 4 test suite using Java reflection library. Kex can generate tests in fully static mode without running any actual code (Kex-symbolic) and in concolic mode (Kex-concolic) which combines symbolic and concrete executions. This paper summarizes the results and experiences of Kex-symbolic and Kex-concolic participation in the eleventh edition of the Java unit testing tool competition at the International Workshop on Search-Based and Fuzz Testing (SBFT) 2023.
A Metamorphic Testing Framework and Toolkit for Modular Automated Driving Systems

Autonomous vehicles (AV), at their highest potentials, will provide a greater mobility, an increased traffic efficiency and, more importantly, safer trips for millions of people every day. While assuring their safety and reliability is, thus, of great importance, it is also a huge challenge. Metamorphic testing (MT) has been shown to be one of the most successful testing techniques to validate automated driving systems (ADS) underpinning the AV. Having said that, there are still lots of rooms for further improving the ADS testing with MT. On one hand, the non-determinism in ADS’ behaviors poses great challenges for precisely judging their correctness. On the other hand, the testing scenarios used in the existing studies are still not very much complex for mimicking various realistic traffic conditions. In this study, we propose a new framework which takes into account the hypothesis testing to provide a more solid way for judging the non-deterministic behaviors of test outcomes. On top of that, we develop a new toolkit to implement more complex and realistic ADS testing scenarios. To demonstrate its practicability, we design complex traffic scenarios and pay attention to examine the ADS’ behaviors in non-collision cases which are often unable to be detected by conventional testing methods. It is then applied to test Autoware, a state-of-the-art modular ADS using the Carla simulator. An analysis of results with the Mann-Whitney-Wilcoxon test and Cohen’s d values reveals a large number of consistencies and reliability issues of Autoware. The findings highlight the flexibility and capability of our MT-based framework in validating the AV using a nondeterministic measure and realistic scenarios that can work in the absence of ground truth datasets.
A Compact 140-GHz Radar MMIC with I/Q Downconverter in SiGe BiCMOS Technology

A monostatic D-band Doppler radar transceiver with a newly developed diode-based mixer is proposed which provides an I/Q signal at the baseband output and thus allows to determine the relative radial direction of a moving target. The quadrature downconverter shows a simple and compact structure since no phase shifters or couplers are needed. The transmitted signal is generated by a low-noise differential voltage-controlled oscillator (VCO) with a frequency band of 136 to 146 GHz. The whole transceiver consumes 74 mA from a 3.3 V supply, with the downconverter and baseband amplifiers contributing about 2 mA. The transceiver shows reliable functionality over a wide range of silicon bulk temperature up to 125°C.
A Simple and Model-Free Path Filtering Algorithm for Smoothing and Accuracy

Predominantly, complex optimization techniques are used for path reconstruction given noisy measurements. However, optimization techniques often require the selection of suitable models, tedious parameter tuning and typically fail to generalize to higher-level tasks. In this paper, we present a model-free path filtering method based on the popular moving average method, namely the Curvature Corrected Moving Average (CCMA), which convinces by its simplicity and broad applicability. The moving average is characterized by its unique noise suppression property, albeit curves are bent inwards, which adversely affects its accuracy. By utilizing the relation between both curvatures, the original curvature can be inferred based on the curvature of filtered points. Extending the symmetric filtering not only succeeds in minimizing noise but retains the original shape of the path, making it a suitable algorithm for a variety of robotic applications. We demonstrate the practicality of the approach in a real-world convoy scenario: The accumulated estimates of the leader vehicle’s position, originating from an Extended Kalman filter, are smoothed using our novel approach to generate proper inputs for the Model Predictive Controller (MPC). This cascade structure of filtering provides both responsiveness and smoothness. Furthermore, we successfully applied this method in the off-road convoy scenario for the ELROB 2022, where we won first place. The source code is publicly available.
Investigating Nonlinear Dependence between Climate Fields

AbstractThe Earth’s ice–ocean–atmosphere system exhibits nonlinear responses, such as the difference in the magnitude of the atmospheric response to positive or negative ocean or sea ice anomalies. Two classes of methods that have previously been used to investigate the nonlinear dependence between climate fields are kernel methods and neural network methods. In this paper, a third methodology is introduced: gradient-based kernel dimension reduction. Gradient-based kernel methods are an extension of conventional kernel methods, but gradient-based methods focus on the directional derivatives of the regression surface between two fields. Specifically, a new gradient-based method is developed here: gradient kernel canonical correlation analysis (gKCCA). In gKCCA, the canonical directions maximize the directional derivatives between the predictor field and the response field, while the canonical components of the response field maximize the correlation with a nonlinear augmentation of the predictor canonical .
Health Professions

Purpose : Recruitment and retention of quali ﬁ ed health professionals in academia has continued to be a challenge as the ﬁ eld of healthcare continues to grow at a rapid pace. The purpose of this study was to examine the satisfaction of online health sciences instructors and if those satisfaction factors in ﬂ uenced their intent to leave. Method : A non-experimental survey design was utilized to gather the perceptions of online faculty satisfaction and related factors. A set of satisfaction and intent to leave surveys were answered by 83 online instructors in a higher education health sciences institution. Results : The results of the path analysis showed that satisfaction from support for online teaching indirectly impacted the faculty intent to leave while in ﬂ uencing job satisfaction and institution satisfaction. Job satisfaction and institution satisfaction directly in ﬂ uenced faculty intent to leave. Discussion : The theoretical model resulting from the study should allow administrators to follow to mitigate their risk of losing valuable faculty. Further, one could use the model as an overall picture of the indirect relationship of satisfaction from support for online teaching to intent to leave, with job satisfaction and institution satisfaction as the key in ﬂ uences over intent to leave.
Rapid Communication

The potential of double-stranded RNA interference (RNAi) technology was studied for down-regulation of gene expression in poplar. A set of vectors was constructed generating RNAs capable of duplex formation of sequences specific for the β-glucuronidase (GUS) reporter gene system. These gene cassettes are driven by the CaMV-35S promoter. To address the question of gene silencing, we tested the functionality of these vectors, both in transient assays by transforming protoplasts with the RNAi constructs, and in stably transformed GUSexpressing poplar plants. Agrobacterium-mediated transformation of those GUS-expressing plants with a GUS-specific RNAi construct showed a strong down-regulation of the reporter gene. From these results we conclude that RNAi is also functional in poplar.
Numerical simulation of lid-driven cavity flow of micropolar fluid

Incompressible unsteady laminar flow of a micropolar fluid in a lid driven square cavity has been examined numerically with finite difference scheme. In this paper, we verify the code validation of Newtonian incompressible fluid (K = 0) with standard bench mark problem in the literature and then proceed the present computation. The vorticity-stream function formulation of Navier-Stokes equations and angular momentum equation is solved by the Euler’s explicit method. The effect of various values of Reynolds number (Re) and vortex viscosity parameter (K) of the flow characteristics is studied and discussed through several graphs.
Multilevel Matrix-Variate Analysis and its Application to Accelerometry-Measured Physical Activity in Clinical Populations

ABSTRACT The number of studies where the primary measurement is a matrix is exploding. In response to this, we propose a statistical framework for modeling populations of repeatedly observed matrix-variate measurements. The 2D structure is handled via a matrix-variate distribution with decomposable row/column-specific covariance matrices and a linear mixed effect framework is used to model the multilevel design. The proposed framework flexibly expands to accommodate many common crossed and nested designs and introduces two important concepts: the between-subject distance and intraclass correlation coefficient, both defined for matrix-variate data. The computational feasibility and performance of the approach is shown in extensive simulation studies. The method is motivated by and applied to a study that monitored physical activity of individuals diagnosed with congestive heart failure (CHF) over a 4- to 9-month period. The long-term patterns of physical activity are studied and compared in two CHF subgroups: with and without adverse clinical events. Supplementary materials for this article, that include de-identified accelerometry and clinical data, are available online.
Multi-objective programming in the USSR

Origins of Soviet multi-objective programming a framework for approaching Soviet multi-objective programming. Part 1 No articulation of preferences: Salukvadze's ideal distance minimization method multi-objective programming and the maximal effectiveness principle Velichenko's minimax method. Part 2 A priori articulation of preferences: multi-objective decomposition for scalar problems. Part 3 Progressive articulation of preferences: the hierarchical decomposition approach to high dimension multi-objective programming problems the STEM method multi-objective graph theory method of constraints parameter space investigation method the random search method the vector-relaxation method the interactive grid method adaptive search method for multi-objective optimization pareto boundary maps. Part 4 Posteriori articulation of preferences: dynamic and stochastic multi-objective programming the reachable sets method piecewise linear approximation. Part 5 Other noteworthy research in multi-objective programming: methodological research at VNIISI Rastrigin and Eiduk's methodological work the theory of parametric scalarization miscellaneous other research some concluding observations. Appendices: method of constraints - theoretical considerations illustrative example - linear programming problem method of sequential analysis illustrative example - integer programming problem without side constraints computational experience terminology Soviet books on multi-objective subjects Soviet researchers' institutional affiliations cyrillic-Roman transliteration conventions Soviet sources, non-Soviet sources.
Real-Time Deployment of Simplified Volterra Nonlinear Equalizer in High-Speed PON

In cost-sensitive passive optical networks (PON), economical channel equalization is a promising technique. The electronic equalization is realized by a digital signal processing (DSP) chip with an integrated analog-to-digital converter (ADC) commonly implemented at the receiver side. Field programmable gate array (FPGA), one of the major hardware platforms nowadays, can provide prototype verification for chip design due to the programmability and short iteration cycle. In this letter, using the FPGA and a cascaded 30-GSa/s ADC, a real-time 1310nm band-limited intensity modulation and direct detection PON system is constructed. With a powerful Volterra nonlinear equalizer (VNLE) running on FPGA, a 31-dB power budget is achieved at a 30-Gb/s line rate. Meanwhile, by pruning and clustering the weights in VNLE, the power consumption is declined by 71.6%.
Impact of Membership to Certified Coffee Marketing Cooperatives on the Income of Smallholder Farmers in Jimma Zone of Oromia Region, Ethiopia

Purpose: Coffee is one of the most important agricultural commodities with a significant contribution to the growth and well-functioning of Ethiopia’s economy, and to the livelihoods of millions of smallholder farmers and laborers. Despite its importance, smallholder coffee production and marketing performance have been unsatisfactory due to various reasons. The introduction of voluntary coffee certification schemes such as Fairtrade (FT) and Organic (Org) certification schemes through cooperatives are viewed as mechanisms to overcome the constraints smallholder farmers face in accessing high value coffee markets and earn better income. However, the impacts of these schemes on the livelihoods of smallholder farmers were not analyzed yet. The main purpose of this study was to estimate the impact of joining (FT, Org or dual FT-Org) certified coffee cooperatives on gross annual incomes earned by member farmers. Methodology: The study employed cross-sectional data collected from randomly selected sample smallholder coffee farmers through a semi-structured questionnaire. Descriptive and simple inferential statistical tests (e.g., frequency, percentage, mean, t-and chi2-tests), and PSM methods were employed to analyze the data. Findings: Results of the descriptive statistics depict that 234 (62.07%) of the total 377 samples farmers were members of certified coffee marketing cooperatives. Among the cooperative members, 83 (35.47%), 84 (35.90%) and 67 (28.63%) were members of FT, Org and dual FT-Org certified coffee marketing cooperatives, respectively. The results of the binary probit model however show that the decisions to join certified coffee marketing cooperatives was significantly influenced by sex, marital status, total livestock holding size, total coffee land size (ha), log total quantity of coffee produced (kg), credit access, and walking distances to development agent’s office, coffee marketing center and all-weather road in minutes, respectively. The PSM analysis results show that membership to certified coffee marketing cooperatives has a positive and significant impact on average annual gross income (ETB) earned. The average gross annual income earned by coop member farmers was ETB 14639.15, which is by 36.51% higher than their counterpart non-coop member farmers. The difference is statistically significant at 1% probability level. Unique Contribution to Theory, Practice and Policy: The study recommended that Cooperatives should be encouraged to establish credit and saving units in their internal structure and/or work in collaboration with other saving and credit providing institutions (such as Cooperative Bank of Oromia) to be able to provide demand-driven credit services to member farmers.
Entrepreneurial growth aspirations during the COVID-19 pandemic: the role of ICT infrastructure quality versus policy response

ABSTRACT We posit that the quality of information and communication technology (ICT) infrastructure and the effectiveness of crisis-specific policy response are essential for entrepreneurial growth aspirations during major external shocks. Enhancing the quality of ICT infrastructure is a relevant strategy for building ecosystems that are resilient to multiple types of crises. It enhances entrepreneurs’ growth ambitions during the crisis, and makes them less reliant on crisis-specific response policies adopted by governments. We provide empirical support for this, utilizing Global Entrepreneurship Monitor (GEM) data from the pandemic period in Chile.
Effects of cholinesterase inhibition on attention and working memory in Lewy body dementias

Abstract Cholinesterase inhibitors are frequently used to treat cognitive symptoms in Lewy body dementias (Parkinson’s disease dementia and dementia with Lewy bodies). However, the selectivity of their effects remains unclear. In a novel rivastigmine withdrawal design, Parkinson’s disease dementia and dementia with Lewy bodies patients were tested twice: once when taking rivastigmine as usual and once when they had missed one dose. In each session, they performed a suite of tasks (sustained attention, simple short-term recall, distractor resistance and manipulating the focus of attention) that allowed us to investigate the cognitive mechanisms through which rivastigmine affects attentional control. Consistent with previous literature, rivastigmine withdrawal significantly impaired attentional efficacy (quicker response latencies without a change in accuracy). However, it had no effects on cognitive control as assessed by the ability to withhold a response (inhibitory control). Worse short-term memory performance was also observed when patients were OFF rivastigmine, but these effects were delay and load independent, likely due to impaired visual attention. In contrast to previous studies that have examined the effects of dopamine withdrawal, cognitively complex tasks requiring control over the contents of working memory (ignoring, updating or shifting the focus of attention) were not significantly impaired by rivastigmine withdrawal. Cumulatively, these data support that the conclusion that cholinesterase inhibition has relatively specific and circumscribed—rather than global—effects on attention that may also affect performance on simple short-term memory tasks, but not when cognitive control over working memory is required. The results also indicate that the withdrawal of a single dose of rivastigmine is sufficient to reveal these impairments, demonstrating that cholinergic withdrawal can be an informative clinical as well as an investigative tool.
Fuzzy Adaptive Output Feedback Control for MIMO Switched Nontriangular Structure Nonlinear Systems With Unknown Control Directions

This paper investigates an adaptive fuzzy output feedback control design problem for the switched multiple-input and multiple-output (MIMO) nontriangular structure nonlinear systems. The discussed system contains the unknown nonlinear dynamics, the unknown backlash hysteresis, the immeasurable states, and the unknown control directions under a class of switching signals. The fuzzy logic systems are utilized to learn the unknown nonlinear dynamics and construct an MIMO fuzzy switched nonlinear observer. By combining the property of the Nussbaum gain function with the Prandtl-Ishlinskii models, a novel observer-based fuzzy adaptive backstepping schematic design algorithm is presented. Furthermore, the stability of the whole controlled system is proved via Lyapunov stability theory and the average dwell time method. The simulation results are presented to verify the validity of the proposed control scheme.