Miracle-dz/UniFAR-FTUs huggingface dataset:
A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain–computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time–frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.
MBE growth of GaSb-based photodetectors on 6-inch diameter GaAs substrates via select buffers

GaSb-based, 6.1 A lattice-constant, infrared photodetector materials were grown on large diameter, 6-in. GaAs substrates by molecular beam epitaxy. Multiple metamorphic buffer architectures, including bulk GaSb nucleation, AlAsSb superlattices, and graded GaAsSb ternary alloys, were investigated to bridge the 7.8% mismatch gap between the GaAs substrates and the GaSb-based epitaxial layers. Unique surface morphologies and crystal structure properties, as revealed by atomic force microscopy and cross-section transmission electron microscopy, pointed to different relaxation mechanisms for different buffer architectures. GaSb nucleation results in a more island-like surface morphology with a mix of 90° misfit and 60°-type threading dislocations, while the graded ternary buffer results in a cross-hatch surface morphology with effective filtering of the threading dislocations. Low root-mean-square roughness values of 5–20 A were obtained for this type of metamorphic epilayer growth. A generic InAsSb/AlAsSb nBn.
Determination of optimum number of ground contact points in modeling a snake-like robot with maneuverability in three dimensions

In this paper, effects of number of contact points with ground in each link for three-dimensional modeling of a snake-like robot are discussed. The snake-like robot is modeled in Matlab's Simmechanics toolbox. Linear spring-damper system is utilized in order to model the ground for each contact point. Considering the increase of number of contact points in our model leads to a more realistic performance and consequently more realistic results, we evaluated how adding to this number affects outcomes of position, velocity, acceleration and motors torque. Finally, three optimum numbers of contact points are suggested for three different snake-like gaits using SPSS Statistics software. The investigated gaits are serpentine and traveling wave, as two dimensional motions, and sidewinding as an example of three-dimensional gaits.
Dimensionality Reduction: An Effective Technique for Feature Selection

knowledge gaining the dimensionality reduction is a significant technique. It has been observed that most of the time dataset is multidimensional and larger in size. When we are using same dataset for classification it may create wrong results and it may also requires more requirements in terms of storage as well as processing capability. Most of the features present are redundant, inconsistent and degrade the performance. To increase the effectiveness of classification these duplicate and inconsistent features must be removed. In this research we have introduced a new method for dealing with the problem of dimensionality reduction. By reducing the unrelated (irrelevant) and unnecessary features related to data, or by means of effectively merging original features to produce a smaller set of feature with more discriminative control, dimensionality reduction methods convey the instant effects of rapid the data mining algorithms, better performance, and increase in unambiguous of data model Keywordsreduction, Fuzzy ARTMAP, Feature selection, Feature extraction, Supervised and Unsupervised techniques, semi-supervised techniques.
Learning Game Design and Software Engineering through a Game Prototyping Experience: A Case Study

This report describes a case study of small-scale effort in employing game playtesting as a starting point for learning about mainstream issues and challenges found in modern software engineering projects and practices. The goal is to be descriptive and informing through a qualitative rendering, rather than prescriptive and quantitative analysis. This study draws attention to the case of where a student with no prior experience in software development or programming must take on the task of learning how to make a game, and along the way learn about many common challenges in modern SE practice through personal discovery and experience. The game itself also imposes challenges in that we have chosen a new, unfamiliar game genre and domain that emphasizes science learning as its purpose for play. Along the way, we discuss issues in requirements, design, prototyping, testing, user experience assessment, and evolutionary software extension, all prior to a formal education in coding or introductory level Computer Science or SE. Though our efforts may seem unusual or anomalous, we believe our methods are open for adoption and reuse by those interested in lowering the barriers to entry into game software development in specific, and into SE more generally.
Agreeing over quantum hybrid networks: Centralized and distributed solutions

Quantum hybrid networks where classical and quantum components coexist play a fundamental role in quantum communications. We consider a basic quantum hybrid network model consisting of a number of nodes each holding a qubit, for which the aim is to drive the network to a consensus in the sense that all qubits reach a common state. Projective measurements are applied serving as control means, and the measurement results are exchanged among the nodes via classical communication channels. We show how to carry out centralized optimal path planning for the network with all-to-all classical communications, in which case the problem becomes a stochastic optimal control problem with a continuous action space. To overcome the computation and communication obstacles facing the centralized solutions, we also develop a distributed Pairwise Qubit Projection (PQP) algorithm, where pairs of nodes meet at a given time and respectively perform measurements at their geometric average. We show that the qubit states are driven to a consensus almost surely along the proposed PQP algorithm, and that the expected qubit density operators converge to the average of the network's initial values.
A new approach for unsupervised word sense disambiguation in Hindi language using graph connectivity measures

Word sense disambiguation (WSD) is an important task in computational linguistics as it is essential for many language understanding applications. In this paper, we propose a graph-based unsupervised WSD method for Hindi text which disambiguates multiple ambiguous words present in the sentence simultaneously. In our approach, we first construct the semantic graph for each interpretation of the given sentence by establishing semantic relations between the pair of words present in the sentence. We use Hindi WordNet to establish semantic relations between the pair of words and then we construct the graph. We find the cost of spanning tree corresponding to each semantic graph and the interpretation for which spanning tree has the minimum cost is identified. This interpretation is considered as the resulting interpretation. Our approach also considers all open class words unlike the previous approaches which focus only on noun.
Vector competence of Ixodes scapularis and Ixodes ricinus (Acari: Ixodidae) for three genospecies of Borrelia burgdorferi.

The vector competence of 2 tick species, Ixodes ricinus (L.) and Ixodes scapularis Say, was determined and compared for 3 genospecies of Borrelia burgdorferi. The 3 genospecies of B. burgdorferi used in the following experiments were Borrelia burgdorferi sensu stricto (B-31 and B-31.D1 clone), Borrelia afzelii (strain Pgau. C3), and Borrelia garinii (strain VS286 and VSBP). Spirochetes from all 5 strains were inoculated intradermally into outbred mice; larval ticks of both species were subsequently fed on those mice and replete larvae were assayed for infection by culture in BSK-H media every 7 d for 4 wk. Infection frequencies in I. scapularis exposed to the 5 strains were as follows: B-31 (90%), B-31.D1 (83%), Pgau.C3 (87%), VS286 (10%), and VSBP (5%). The comparable infection frequencies for I. ricinus were B-31 (3%), B-31.D1 (3%), Pgau.C3 (90%), VS286 (5%), and VSBP (3%). Resultant nymphal I. scapularis successfully transmitted B-31, B-31,D1, Pgau.C3, and VS286 to outbred mice. I. ricinus nymphs transmitted Pgau.C3 and VS286. Both species failed to transmit strain VSBP.
Radiation Synthesis of Superabsorbent Poly(acrylamide-co-acrylic acid)-Sodium Alginate Hydrogels

Sodium alginate (NaAlg) is considered to be a potential natural polymer for biomaterial applications, because of its hydrophilic properties that is capable to increase the swelling of hydrogels and biodegradable. This study describes the synthesis of a poly (acrylamide-co-acrylic acid) NaAlg superabsorbent hydrogels via crosslinking gamma radiation. The effect of irradiation dose (20-40 kGy) and NaAlg concentration (0.1-0.7 %) of on swelling of hydrogels were studied. The copolymers were characterized by Fourier transform infra red spectroscopy (FTIR) and scanning electron microscopy (SEM) micrograph. The presence of the lowest (0.1%) NaAlg concentration provokes a significant increasing the equilibrium degree solution (EDS) of hydrogel up 800 g/g ,and the extent of gel fraction increases as a function irradiation dose up to about 99 %. The hydrogels were also found to be sensitive to the ionic strength of medium. The FTIR spectra of hydrogels were shown crosslinking occurs between acrylamide and acrylic acid and pores structures in the hydrogel observed by using SEM.
Cl-Amidine Prevents Histone 3 Citrullination and Neutrophil Extracellular Trap Formation, and Improves Survival in a Murine Sepsis Model

Sepsis refers to the presence of a serious infection that correlates with systemic and uncontrolled immune activation. Posttranslational histone modification plays an important role in chromatin decondensation, which is regulated by citrullination. Citrullinated histone H3 (H3cit) has been identified as a component of neutrophil extracellular traps (NETs), which are released into the extracellular space as part of the neutrophil response to infection. The conversion of arginine to citrulline residues on histones is catalyzed by peptidylarginine deiminase 4 (PAD4). This study's goals were to characterize the presence of PAD4-catalyzed H3cit and NET formation during the onset of sepsis and elucidate the effects on the immune response when this mechanism of action is blocked. Adult C57BL/6 male mice were treated with Cl-amidine, an inhibitor of PAD4, 1 h prior to sepsis induced by cecal ligation and puncture (CLP). Twenty-four hours after CLP, cytokine levels, H3cit protein expression, neutrophil counts, and NET production were evaluated in the peritoneal cavity. Survival studies were also performed. Here we demonstrate that Cl-amidine treatment prior to CLP improves overall survival in sepsis and the abrogation of PAD4 has minimal effects on the proinflammatory immune response to sepsis, while it has no effect on overall neutrophil migration to the peritoneum.
Getting Robots, Agents and People to Cooperate: An Initial Report

Combining the unique capabilities of robots, agents and people (RAPs) promises to improve the safety, efficiency, reliability and cost at which some goals can be achieved, while allowing the achievement of other goals not previously achievable. Despite their heterogeneity, and indeed, because of their heterogeneity, our key hypothesis is that in order for RAPs to work together effectively, they must work as a team: they should be aware of the overall goals of the team, and coordinate their activities with their fellow team members in order to further the team’s goals. This poses a challenge, since different RAP entities may have differing social abilities and hence differing abilities to coordinate with their teammates. To construct such RAP teams, we make the RAPs “team ready” by providing each of them with teamwork-enabled proxies. While proxy-based architectures have been explored earlier for flexible multiagent teamwork, their application to RAP teams exposes two weaknesses. To address the problems with existing role allocation algorithms for RAP teams operating in dynamic environments, we provide a new approach for highly flexible role allocation and reallocation. Second, we enrich the communication between RAPs and between a RAP and its proxy, to improve teamwork flexibility while limiting the number of messages exchanged. This paper discusses the proxy based architecture and our initial attempts at developing algorithms that address the problems that emerge when the RAP teams are used in complex domains.
Cardiac output measured by electrical velocimetry in the CT suite correlates with coronary artery enhancement: A feasibility study

Background: Cardiac output (CO) is inversely related to vascular contrast medium (CM) enhancement during computed tomography (CT). Impedance cardiography with a new technique, electrical velocimetry (EV), may create opportunities to measure CO pre-examination for adaptation of CM injection parameters. Purpose: To relate COEV measured by radiology staff to aortic attenuation as a measure of coronary artery attenuation during CT coronary angiography (CTCA), and to formulate a tentative statistical model to adapt CM injection parameters to CO. Material and Methods: COEV was measured immediately before 100 kVp CTCA (64-multirow detector) in 27 patients with presumed coronary artery disease. For CTCA, 260 mg I/kg (maximum dosage weight: 80/90 kg for women/men) was injected intravenously during 12 s. Simple linear regression analysis was performed to explore the correlation between aortic attenuation (Hounsfield units, HU) and body weight, the influence of COEV on aortic attenuation adjusted to injected CM dose rate (HU per mg I/kg/s), and to establish a tentative formula on how to adapt CM injection parameters to COEV and desired aortic attenuation. Results: The correlation between aortic attenuation and body weight was weak and non-significant (r=−0.14 after outlier exclusion). A significant negative correlation (r=−0.63) was found between aortic attenuation adjusted to injected CM dose rate (HU per mg I/kg/s) and COEV. The resulting formula, CM dose rate=COEV×(aortic attenuation−240)/55, made it possible to calculate CM volumes and injection rates at various COs and, for example, the present mean aortic attenuation (438 HU), injection time (12 s), CM concentration (320 mg I/ml), and a certain body weight. Conclusion: EV makes it possible to measure CO in the CT suite before vascular examinations. Hence, CM doses may be decreased in low CO states to reduce the risk of CM-induced nephropathy without jeopardizing diagnostic quality and may be increased in high CO states to avoid poor enhancement.
Landmark extraction and state estimation for UAV operation in forest

In this paper, we present the essential problems and solutions about feature extraction and state estimation for UAV operation in GPS-denied environment - forest. Tree trunks, which are selected to be the features existing in the operational environment, help the UAV localize itself in an unknown forest. The UAV is equipped with an inertial measurement unit (IMU) and a scanning laser rangefinder (LRF). The raw laser data is preprocessed to remove outliers. A clustering algorithm based on the spatial discontinuity of the consecutive laser beams is applied to the preprocessed laser scan data. The clustered segments are characterized by a group of geometric descriptors, which are subjected to a series of thresholds to remove the false tree stems such as possible ground hit and bushes. To ensure fast and correct data association, the extracted features in consecutive scans are first aligned in rotation using the yaw angle measurement of IMU. Then the scan matching algorithm is applied to estimate the incremental rotation and translation of the UAV. The rotation and translation are fed into an IMU-driven Kalman filter to estimate the UAV position and velocity.
Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling

Deep learning models (DLMs) are state-of-the-art techniques in speech recognition. However, training good DLMs can be time consuming especially for production-size models and corpora. Although several parallel training algorithms have been proposed to improve training efficiency, there is no clear guidance on which one to choose for the task in hand due to lack of systematic and fair comparison among them. In this paper we aim at filling this gap by comparing four popular parallel training algorithms in speech recognition, namely asynchronous stochastic gradient descent (ASGD), blockwise model-update filtering (BMUF), bulk synchronous parallel (BSP) and elastic averaging stochastic gradient descent (EASGD), on 1000-hour LibriSpeech corpora using feed-forward deep neural networks (DNNs) and convolutional, long short-term memory, DNNs (CLDNNs). Based on our experiments, we recommend using BMUF as the top choice to train acoustic models since it is most stable, scales well with number of GPUs, can achieve reproducible results, and in many cases even outperforms single-GPU SGD. ASGD can be used as a substitute in some cases.
Capturing the quake player: using a BDI agent to model human behaviour

The BDI agent framework is a well-known agent framework, often discussed in the agents literature. The work described here takes advantage of the folk psychological roots of the philosophical underpinnings of BDI to develop and build models of human behaviour. Knowledge elicitation was closely tied to the folk psychological background, and this was then encoded with a BDI-based language. Although some accommodation did have to be made for the gaps between the BDI philosophy and the implementation language, this approach did provide intuitive way of developing and building these models.
A 0.8-V 48μW 82dB SNDR 10-kHz bandwidth ΣΔ modulator

This paper presents a low-power chopper stabilized discrete-time 2nd-order feed-forward ΣΔ modulator with a 4-bit asynchronous successive approximation register (SAR) quantizer. The weighted sum of integrated and feed-forward signals is merged with the sampling phase of the SAR quantizer to minimize the distortion sources and associated hardware overhead. The 1st integrator uses a partially switched operational amplifier biased in weak inversion to reduce power consumption. The 4-bit SAR quantizer further employs an asynchronous control scheme to reduce the loop delay and power consumption. A 0.13-μm CMOS experimental prototype achieves 84dB dynamic range, 84dB peak SNR, and 82dB peak SNDR over an input bandwidth of 10-kHz. The total power consumption of the modulator is 48μW from a 0.8-V supply at an 800-kHz sampling rate.
Finding any nontrivial coarse correlated equilibrium is hard

One of the most appealing aspects of correlated equilibria and coarse correlated equilibria is that natural dynamics quickly arrive at approximations of such equilibria, even in games with many players. In addition, there exist polynomial-time algorithms that compute exact correlated and coarse correlated equilibria. However, in general these dynamics and algorithms do not provide a guarantee on the quality (say, in terms of social welfare) of the resulting equilibrium. In light of these results, a natural question is how good are the correlated and coarse correlated equilibria---in terms natural objectives such as social welfare or Pareto optimality---that can arise from any efficient algorithm or dynamics. We address this question, and establish strong negative results. In particular, we show that in multiplayer games that have a succinct representation, it is NP-hard to compute any coarse correlated equilibrium (or approximate coarse correlated equilibrium) with welfare strictly better than the worst possible. The focus on succinct games ensures that the underlying complexity question is interesting; many multiplayer games of interest are in fact succinct. We show that analogous hardness results hold for correlated equilibria, and persist under the egalitarian objective or Pareto optimality. To complement the hardness results, we develop an algorithmic framework that identifies settings in which we can efficiently compute an approximate correlated equilibrium with near-optimal welfare. We use this framework to develop an efficient algorithm for computing an approximate correlated equilibrium with near-optimal welfare in aggregative games.
Assessment of Eye Fatigue Caused by 3D Displays Based on Multimodal Measurements

With the development of 3D displays, user's eye fatigue has been an important issue when viewing these displays. There have been previous studies conducted on eye fatigue related to 3D display use, however, most of these have employed a limited number of modalities for measurements, such as electroencephalograms (EEGs), biomedical signals, and eye responses. In this paper, we propose a new assessment of eye fatigue related to 3D display use based on multimodal measurements. compared to previous works Our research is novel in the following four ways: first, to enhance the accuracy of assessment of eye fatigue, we measure EEG signals, eye blinking rate (BR), facial temperature (FT), and a subjective evaluation (SE) score before and after a user watches a 3D display; second, in order to accurately measure BR in a manner that is convenient for the user, we implement a remote gaze-tracking system using a high speed (mega-pixel) camera that measures eye blinks of both eyes; thirdly, changes in the FT are measured using a remote thermal camera, which can enhance the measurement of eye fatigue, and fourth, we perform various statistical analyses to evaluate the correlation between the EEG signal, eye BR, FT, and the SE score based on the T-test, correlation matrix, and effect size. Results show that the correlation of the SE with other data (FT, BR, and EEG) is the highest, while those of the FT, BR, and EEG with other data are second, third, and fourth highest, respectively.
Caregiver-Administered Neuropsychiatric Inventory (CGA-NPI)

The Neuropsychiatric Inventory (NPI) is used to assess neuropsychiatric symptoms in dementia patients. To reduce clinicians’ time taken to administer the NPI, the authors studied a caregiver-administered NPI (CGA-NPI), in which caregivers completed the written form of the NPI worksheet. After a brief presupervision session, the caregivers of 61 dementia patients completed the CGA-NPI by reading through the worksheet. This was followed by a postsupervision session to check if the caregivers had completed the form appropriately. The correlation between the prevalence rates of each neuropsychiatric symptom obtained by the CGA-NPI and those obtained by the NPI was fair to good (. = 0.57-0.90) in all domains. All frequency, severity, and caregivers’ distress scores of the CGA-NPI correlated significantly with those of the NPI (r> 0.6, P< .001). Total CGA-NPI scores also correlated highly with total NPI scores (r= 0.86, P< .001). These results suggest that the CGA-NPI can substitute for the NPI, saving administration time.