Miracle-dz/UniFAR-FTUs huggingface dataset:
WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue

Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal cameras, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications. Link to dataset: https://sites.google. com/uw.edu/wisard/
Multi-ionic Kinematical Study of the H II Region Sharpless 2-158

The Sh 2-158 gas complex kinematics is analyzed using four lines from three different ions (Hα, [O III] λ5007, and [S II] λλ6727, 6731) and the CO 1-0 line. The O++ and S+ data cubes were obtained using MOS FP at the CFHT, while the H+ data cube was obtained using FaNTOmM at the Observatoire du mont Mégantic. More than 200,000 spectra (S/N ≥ 5) were obtained. Maps of the radial velocity fields, the velocity line width field, and the electronic density field are discussed. All ionic mean radial velocities are blueshifted with respect to the molecular cloud (VLSR = -55.16 ± 0.02 km s-1), more so as one goes from [S II] to Hα to [O III]. Two flows originating from the molecular cloud are identified. The encounter of the two flows, and possibly stellar winds, induces turbulence near the stars, causing larger line widths (≈10 km s-1) than elsewhere in the field. The mean [S II] line width is smaller than the mean [O III] line width, which is smaller than the mean Hα line width. Those differences between the ions are explained by the smaller [O III] Strömgren sphere compared to the Hα Strömgren sphere and the restriction of [S II] to a shell of gas near the molecular cloud. The most probable density value is 225 ± 25 cm-3. The observations are mostly in agreement with the Champagne model. A geometrical model is proposed for Sh 2-158.
A Method for Component Evaluation for Live Testing of Cloud Systems

Live testing is about testing a subsystem in production without causing any unacceptable disturbance to the production traffic. A subsystem is tested in production for multiple purposes such as deployment verification, fault prediction, fault localization, etc. The main challenge of live testing is alleviating the risk of test interferences as it may lead to a violation of a system's functional or non-functional requirements. To properly handle this risk, one needs to know which components present a risk of test interferences and what is the cost of the countermeasures to handle that risk. Existing literature relies heavily on human judgement, which can be time consuming, not always feasible, may provide misleading insight. In this paper we go through the challenges of automating this evaluation process and propose a solution to overcome them. Our solution consists of a method for components evaluation which goes through three steps, evaluation of test interferences that may manifest in external behaviour, evaluation of test interferences that may manifest in resource consumption, and finally the evaluation of the cost of implementing the countermeasures to overcome the risk of test interferences.
Modeling the fuel consumption by a HEV vehicle – a case study

The article presents a mathematical model demonstrating the synergy of HEV energetic machines in accordance with the model predictive control. Then the results of road tests are presented. They were based on the factory control of the above-mentioned system. The results of the operating parameters of the system according to the factory control and the results of the operating parameters according to the model predictive control were compared. On their basis, it could be concluded that the model predic-tive control contributed to changes in the power and electrochemical charge level of the energy storage system from 50.1% (the beginning) to 56.1% (the end of course) and for MPC from 50.1% (the begin-ning) to 59.9% (the end of the course). The applied MPC with 13 reference trajectories (LQT) of power machines of the series-parallel HEV allowed for fuel savings on the level of 4%.
Phase-shifted helical long-period grating-based temperature-insensitive optical fiber twist sensors

In smart structure monitoring, twist angle is one of the most critical mechanical parameters for infrastructure deterioration. A compact temperature-insensitive optical fiber twist sensor based on multi-phase-shifted helical long period fiber grating has been proposed and experimentally demonstrated in this paper. A multi-phase-shifted helical long period fiber grating is fabricated with a multi-period rotation technology. A π / 2 and a 3π / 2 phase shift is introduced in the helical long period fiber grating by changing the period. The helical pitch can be effectively changed with a different twist rate, which is measured by calculating the wavelength difference between two phase shift peaks. Although the wavelength of the phase shift peak also shifts with a change of the temperature, the wavelength difference between two phase shift peaks is constant due to two fixed phase shifts in the helical long period fiber grating, which is extremely insensitive to temperature change for the multi-phase-shifted helical long period fiber grating. The experimental results show that a sensitivity of up to 1.959 nm/(rad/m) is achieved.
A passive, asymmetrically-compliant knee joint improves obstacle traversal in an insect-scale legged robot

Insects can locomote readily in challenging envi-ronments, such as over steep inclines and across obstacle-laden terrains, which still frustrate robots of similar size. In this work, inspired by the passive compliant properties of insect limbs, we use the insect-scale Harvard Ambulatory Microrobot and multilayer microfabrication techniques as platform to study the ability of passive mechanisms to improve open-loop running in rough terrains. We tested the performance of different limb designs in vivo, exploring how the magnitude, directionality, and distribution of compliance incorporated into the leg impacted robot performance. Limbs were evaluated on both a featureless substrate and an increasingly-adversarial 3D-printed terrain designed to mimic natural environments. We tested the limbs using a trotting gait in the quasi-static (2 Hz) and body dynamics (25 Hz) stride frequency regimes. Performance was reported as bodylengths traveled per gait cycle on the featureless substrate, and as the largest feature height the robot was able to overcome in the terrain. The work presented here provides design principles for a passive limb that expands the terrain accessible to small robot; we find a limb with a single asymmetrical joint is able to improve quasi-static terrain traversal by 203 % relative to a rigid limb.
Visual Manipulation Relationship Detection based on Gated Graph Neural Network for Robotic Grasping

Exploring the relationship among objects and giving the correct operation sequence is vital for robotic manipulation. However, most previous algorithms only model the relationship between pairs of objects independently, ignoring the interaction effect between them, which may generate redundant or missing relations in complex scenes, such as multi-object stacking and partial occlusion. To solve this problem, a Gated Graph Neural Network (GGNN) is designed for visual manipulation relationship detection, which can help robots detect targets in complex scenes and obtain the appropriate grasping order. Firstly, the robot extracts feature from the input image and estimate object categories. Then GGNN is used to effectively capture the dependencies between objects in the whole scene, update the relevant features, and output the grasping sequence. In addition, by embedding positional encoding into pair object features, accurate context information is obtained to reduce the adverse effects of complex scenes. Finally, the constructed algorithm is applied to the physical robot for grasping. Experiment results on the Visual Ma-nipulation Relationship Dataset (VMRD) and the large-scale relational grasp dataset named REGRAD show that our method significantly improves the accuracy of relationship detection in complex scenes, and can be well generalized in the real world.
Phonological neighborhoods and phonetic similarity in spoken word recognition

English neighborhood literature has demonstrated that neighborhood density affects the auditory word recognition. However, an unresolved question is exactly how neighborhood density should be calculated. In this paper the definition of a lexical neighborhood is explored in Japanese. Data for the analyses were collected from Japanese neighborhood experiments using the same 700 test words and a lexicon that consisted of only nouns from the NTT psycholinguistic database [Amano and Konodo, 1999]. Three different neighborhood calculations were used to analyze the data. The first calculation was based on Greenberg–Jenkins phoneme substitution, deletion, and insertion rules. The second calculation included prosodic information as another dimension in the neighborhood calculation in order to reflect the finding that prosodic information has a vital role in Japanese word recognition. The third calculation was based on the auditory properties of the words in the lexicon. Neighborhood density was measured by comparing the similarity of cochleagrams of the 66 000 audio files. The results of the analyses demonstrated that phonological similarity within the lexicon seems to be calculated based on higher‐level abstract representations rather than on a lower‐level acoustic‐auditory representation in any of the experiments. The implications from the results to the current word recognition theories will also be discussed. [Work supported by NIH. ]
The for gene as one of the drivers of foraging variations in a parasitic wasp

Foraging behaviours encompass strategies to locate resources and to exploit them. In many taxa, these behaviours are driven by a major gene called for, but the mechanisms of gene regulation vary between species. In the parasitoid wasp Venturia canescens, sexual and asexual populations coexist in sympatry but differ in life‐history traits, physiology and behaviours, which could impact their foraging strategies. Here, we explored the molecular bases underpinning divergence in behaviours by testing two mutually nonexclusive hypotheses: first, the divergence in the for gene correlates with differences in foraging strategies, and second, the latter rely on a divergence in whole‐genome expression. Using comparative genomics, we showed that the for gene was conserved across insects considering both sequence and gene model complexity. Polymorphism analysis did not support the occurrence of two allelic variants diverging across the two populations, yet the asexual population exhibited less polymorphism than the sexual population. Sexual and asexual transcriptomes split sharply, with 10.9% differentially expressed genes, but these were not enriched in behaviour‐related genes. We showed that the for gene was more highly expressed in asexual female heads than in sexual heads and that those differences correlate with divergence in foraging behaviours in our experiment given that asexuals explored the environment more and exploited more host patches. Overall, these results suggested that fine tuning of for gene expression between populations may have led to distinct foraging behaviours. We hypothesized that reproductive polymorphism and coexistence in sympatry of sexual and asexual populations specialized to different ecological niches via divergent optima on phenotypic traits could imply adaptation through different expression patterns of the for gene and at many other loci throughout the genome.
Handover Enhancement in High-Speed Railway 5G Networks: A LSTM-based Prediction Method

This paper focuses on the handover performance enhancement of high-speed railway (HSR) 5G millimeter wave(mmWave) networks. As starting the handover at different time may yield very different performances, we propose a Long Short Term Memory (LSTM)-based prediction method to find a proper handover point in advance to alleviate the high handover delay and link interruption-prone problems of traditional A3 handover method. By learning the historical trend of Reference Signal Receiving Power (RSRP) and predicting the future changes of RSRP based on the LSTM encoder-decoder network, the proposed LSTM-based prediction method is able to enhance the handover performance of HSR 5G networks. Simulation results show that compared with traditional A3 method, our proposed LSTM-based prediction method reduces the probability of link interruption, improve the stability of the quality of service (QoS) during the handover process.
Event Driven Share Price Forecasting based on Change based Impact Analysis

Investing in stocks is considered one of the riskiest options to invest due to regular unpredictable market fluctuations. It is difficult to forecast stock price variations due to this reason which makes investment or divestment decisions extremely challenging. This paper proposes a mechanism for share price forecasting by quantifying the impact of market externalities such as news events. We propose a novel multivariate approach that forecasts the behaviour of stock prices — a projection modified for investor psychology and market features, more reliably compared to existing work. Our mechanism employs a strategy that models stock variations using a physical metaphor employing first-order derivatives of historical stock price and sentiment with respect to time. We do an extended forecast based on the sentimental impact on stock prices in response to an event using Kalman filtering, similarly to a trajectory of a physical object that is subject to a force. The proposed methodology achieves a significant accuracy of up to 97% for two-three days forecasts, which exceeds the forecast accuracy of related work.
Cross linking of thiolated carbon nanotubes: An ab initio study

We have studied the mechanism of covalent cross linking between carbon nanotubes functionalized with thiocarboxylic and dithiocarboxylic esters. The interconnected nanotube structures were modeled using density functional theory combined with the pseudopotential approximation. Our calculations revealed the important role of surface defects when forming chemical bonds that connect nanotubes to each other. The strength and stability of intertube bonds increased in the vicinity of defect sites. The computed binding energies and potential energy profiles of linked nanotubes were found to be sensitive to the choice of exchange-correlation functional used within the density functional formalism. The observed sensitivity could be explained by a nonuniform distribution of the electronic charge density near defect sites. This result suggests that gradient-corrected functionals are essential for accurate theoretical modeling of functionalized carbon nanotubes and nanotube-based composites.
Voiced Features and Artificial Neural Network to Diagnose Parkinson’s Disease Patients

This paper presents an algorithm to detect Parkinson’s disease using voiced features and an artificial neural network (ANN). It uses 44 audio features extracted from the sustained vowel’/a/’ sound of the Parkinson’s disease patients and healthy subjects. To fully characterize a healthy and a Parkinson’s patient’s voice, audio features of different domains have been investigated. A simple two-layer feed-forward neural network (FFNN) has been deployed to avoid an overwhelming computational burden on the system. Significant statistical parameters are measured to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm achieves an accuracy of 85% and G-mean of 85.19% in identifying Parkinson’s patients.
Error Model (EM)―A New Way of Doing Fault Simulation

This paper introduces the concept of error model (EM) that replaces a traditional fault-model-based simulation. EM is a temporal simulation of fault symptoms in an application processor. This paper shows that the application resiliency metrics, such as fault coverage, derived through EM are more comprehensive and accurate than those derived through empirical models like single stuck-at faults. In addition, EM can be used on high-level simulation models (behavioral RTL, emulation or in some cases in-silicon). EM approach gives greater than three orders of performance improvement over gate netlist models using stuck-at fault simulation. This paper shows that the coverage metrics, for a billion-logic-gate GPU design, obtained through in-silicon EM closely match the corresponding coverage metrics estimated from a low-level netlist with single stuck-at fault simulation.
Latency Insertion Method for FinFET DC Operating Point Simulation Based on BSIM-CMG

As the scaling of planar MOSFETs progresses, various short-channel effects become prominent. The 3-dimensional FinFET was invented to avoid these short-channel effects. Transistor-level simulation with FinFETs is traditionally conducted by SPICE which has super-linear computational complexity. We propose a new method for FinFET DC operating point simulation through the use of the latency insertion method (LIM) which exhibits linear computational complexity. The algorithm incorporates the BSIM-CMG industry-standard compact model. The method is tested on 10 nm and 20 nm FinFETs, and the results are compared with commercial simulators.
Deep Learning and Dynamic Mode Decomposition for Inflation and Interest rate Forecasting

Time- series forecasting in finance domain is a challenging task as financial data tends to be very volatile. In this paper, we forecast interest and inflation rates by comparing the performance of two forecasting methods, namely Long short term memory (LSTM) and Dynamic Mode Decomposition (DMD). LSTM and other Gated Recurrent Networks (GRUs) have proved their might in dealing with stochastic time series many times. However, DMD is a data-driven, spatio-temporal method that breaks down a system into spatial modes having varying temporal behaviour. Such modes assist us in evaluating how the system evolves and forecasting the system's future state. We then tune the hyper-parameters of the LSTM and DMD models using Bayesian Optimization (BO) technique. Eventually we compare the forecasts from both methods and tabulate the results.
Loading an Autonomous Large-Scale Dump Truck: Path Planning Based on Motion Data from Human-Operated Construction Vehicles

A large-scale dump truck that automatically transports earth and sand in cooperation with a human-operated backhoe is of interest to the construction industry. A human-operated dump truck generally drives slightly past the desired loading position and then backs up to it for loading the sediment. The turning and loading positions are subjectively decided according to the working posture of the backhoe and the surrounding environment, and the safety margin of cooperative works. Backhoe operators want to perform the same maneuvers for human-operated/automated dump trucks. The movements of the autonomous vehicle should be similar to those of a human-operated one. However, it is difficult to derive a human-like path that does more than minimize costs. This study proposes a path-planning method that generates a path including a turning back, according to the changing backhoe position and surrounding conditions. We modeled the positional relationship during loading between a backhoe and dump truck, determining the loading and turning positions and related parameters from operational data collected in trials with human-operated construction vehicles. The proposed method allowed the autonomous dump truck path to resemble a human-like one. The authors have retrofitted an existing large-scale six-wheeled dump truck for automatic operation. Automatic loading in cooperation with a human-operated backhoe was realized all 17 times using the retrofitted dump. The average stopping accuracy was 0.57 m and 9.7°.
Functional and Structural Analysis of BPSS0140-BPSS0142 ABC Transporter That Mediates Fructose Import in Burkholderia pseudomallei

ATP-binding cassette (ABC) transporters mediate bacteria uptake or export of a variety of solutes across biological membranes. Bacterial uptake of the monosaccharides is important as a source of carbohydrate building blocks that contribute to the bacteria’s major structure. Burkholderia pseudomallei is the etiological agent of melioidosis and within its genome, 33 genes related to monosaccharide ABC transporters have been predicted. The presence of these transporters is believed to assist in bacterial survival and adaptation in various environments. Despite a large number of genes in the genome, most of these systems have yet to be characterized, including the bpss0140-bpss0142 operon. Here, we predicted the 3D structure of each protein encoded by bpss0140-0142 and identified the specifically associated monosaccharides. In silico analyses of the structures demonstrated that BPSS0140 is a sugar-binding protein, BPSS0141 is a transmembrane permease and BPSS0142 is an ATPase. Through protein structure modeling and protein-ligand docking, several specific monosaccharide sugars were found to interact with the BPSS0140-BPSS0142 ABC transporter. To validate the in silico prediction, knock-out mutants for each of the genes were constructed. A growth profile between wild-type and mutants in an M9 medium supplemented with glucose, fructose, ribose, and galactose as predicted from the protein-ligand docking was then performed. The growth of mutants decreased significantly compared to the wild-type bacteria when grown in M9 supplemented with fructose as the sole carbon source indicating that this transporter is potentially the main fructose transporter in B. pseudomallei.
Modelling of VLF radio propagation in the earth-ionosphere waveguide.

Understanding the propagation of Very Low Frequency radio waves in the waveguide formed between the surface of the Earth and the lower edge of the ionosphere is of great interest and practical use. Very Low Frequency radio waves serve as a useful tool to investigate the ionosphere and different factors influencing it, most notably solar conditions and events. In this thesis, two methods of simulating the propagation of narrowband Very Low Frequency radio waves are presented, namely Waveguide Mode Theory and the Finite-Difference Time-Domain method. The principles behind these methods are introduced and the advantages and drawbacks of each are discussed. Wait’s two ionospheric parameters, H ′ and β, are used in both cases to simulate the electron density and conductivity of the lower ionosphere; probably the most important factors in this field of study. The two models are first used to investigate some theoretical aspects and concepts. Applications of the models are then illustrated through the use of data from UltraMSK and the DEMETER satellite.