Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of 3D Virtual Neuropsychological Rehabilitation Activities

Virtual environments can be valid and efficient tools for neuropsychological rehabilitation of cognitive impairments. However,the specification of the virtual task is not easy for neuropsychologists who are not aware of game design and implementation issues. We present the method that we have followed to help therapists to describe the expected task functioning. Our system is split into different subsystems: interactions, task logic, messages,scoring and feedback. This way, therapists specify the requirements of each subsystem separately, which comes out to be easier and clearer. The game structure manages and composes the input and output of the different subsystems to run the game. All the interactions are specified according to a common syntax. This provides the desired flexibility to add, remove and modify interactions in run-time. In order to control the location of all the objects of the scenario, for each receiver surface, we define a pick-and-place grid that keeps record of the objects piled onto it. The game logic is implemented as a non-deterministic automaton that controls each task state, modifies in run-time the different subsystems and programs new interactions.Moreover, we have designed a graphical editor to describe the game logic in a simple way, focusing only on relevant states of the application.
Receptive Fields and Visual Responses 2.1 Introduction

The methods discussed in chapter 1, particularly spike-triggered averages and reverse correlation techniques, are used extensively to study properties of neurons at early stages of the visual system, in particular retina (retinal ganglion cells), lateral geniculate nucleus (LGN), and primary visual cortex (V1, or area 17 in the cat). The response properties of neurons at successive stages of the visual pathway get more complex and nonlin-ear. At early stages, however, some neurons (X cells in the cat or P cells in the monkey retina and LGN, and simple cells in primary visual cortex) can be described using linear filters with relatively simple static nonlinearities such as thresholds. Other neurons (complex cells in primary viusal cortex) can be modeled with slight extensions of this formalism. These models are approximations, not exact descriptions of neural responses, and they are purely descriptive not mechanistic. Nevertheless, they play an important role in our understanding of visual processing for a number of reasons. The spike-triggered averages upon which these models are based provide a framework for characterizing the selectivity of visually responsive neu-rons. In addition, models based on linear filters and static nonlinearities provide a reference point for identifying and characterizing novel effects in experimental data. Finally, by providing a first-order description of neu-ral responses, these models form a basis for developing mechanistic models that attempt to explain how visual responses arise from the synaptic, cellular and network properties of retinal, LGN, and cortical circuits. Models based on linear filters and static nonlinearities have also been used to describe responses in V2, area 18 and area MT, but they fail to capture the response characteristics at later stages of the visual system.
Aging Adaption in Integrated Circuits Using a Novel Built-In Sensor

As process technology further scales, aging, noise and variations in integrated circuits (ICs) and systems become a major challenge to both the semiconductor and electronic design automation (EDA) industries, which may cause significantly increased mismatch between modeled and actual silicon behavior, and even IC failure in field. Therefore, the addition of accurate and low-cost on-chip sensors is of great value to reduce the mismatch and perform in-field measurements. This paper presents a novel standard-cell-based sensor for reliability analysis of digital ICs (called Radic), in order to better understand the characteristics of gate, functional path aging and process variations' impact on timing performance, and perform in-field aging measurements. The Radic sensor has been fabricated on two floating gate Freescale SoCs in very advanced technology. The measurement results demonstrate that the resolution can be better than 0.1 ps, and the accuracy is kept throughout aging/process variation. Additionally, a built-in aging adaption system based on Radic sensor is proposed to perform in-field aging adaption. Simulation results verify that, comparing with designs with fixed aging guardband, the proposed aging adaption system releases 80% of aging timing margin, saves silicon area by 1.02%-3.16% at most targeting frequencies, and prevents aging induced failure.
Multiple-surface sliding mode control of pneumatic actuator with mismatched uncertainties

This paper deals with the position control of a servo-pneumatic system in presence of mismatched uncertainties. Pneumatic actuators are used in many industrial applications; such as haptic interfaces, mechatronic systems and robotics. Restriction of control effort, to apply directly on the mismatched uncertainties, causes problem to cope with these kinds of uncertainties. Nonlinearities due to internal and external disturbances such as friction force between piston seal and cylinder wall; make it difficult to control these actuators. Thus modeling and identification of friction parameters is an essential part of the controller design procedure. Using simple model for friction such as stribeck model could help to put more effort on design a controller. Also bounded uncertainty owing to unmodel dynamics of friction is considered. First the system dynamics represented. Then By using multi surface sliding mode control scheme, mismatched uncertainties that appear in state equation of system dynamics are compensated. The simulation results are compared with experimental measured system output. Asymptotic stability of the closed loop system is proven by using Lyapunov method; experimental results show that the proposed controller can deliver good tracking performance and it is robust to uncertainties.
Arabidopsis MALE STERILITY1 Encodes a PHD-Type Transcription Factor and Regulates Pollen and Tapetum Development[W]

The Arabidopsis thaliana MALE STERILITY1 (MS1) gene encodes a nuclear protein with Leu zipper–like and PHD-finger motifs and is important for postmeiotic pollen development. Here, we examined MS1 function using both cell biological and molecular biological approaches. We introduced a fusion construct of MS1 and a transcriptional repression domain (MS1-SRDX) into wild-type Arabidopsis, and the transgenic plants showed a semisterile phenotype similar to that of ms1. Since the repression domain can convert various kinds of transcriptional activators to dominant repressors, this suggested that MS1 functioned as a transcriptional activator. The Leu zipper–like region and the PHD motif were required for the MS1 function. Phenotypic analysis of the ms1 mutant and the MS1-SRDX transgenic Arabidopsis indicated that MS1 was involved in formation of pollen exine and pollen cytosolic components as well as tapetum development. Next, we searched for MS1 downstream genes by analyzing publicly available microarray data and identified 95 genes affected by MS1. Using a transgenic ms1 plant showing dexamethasone-inducible recovery of fertility, we further examined whether these genes were immediately downstream of MS1. From these results, we discuss a role of MS1 in pollen and tapetum development and the conservation of MS1 function in flowering plants.
Deformable Registration Combined with 3 D SIFT Matching and Moving Least Squares

Free-form deformation (FFD) is widely used in deformable image registration. FFD uses a regular grid of control points to generate image deformation. Accurate optimization of the control-point displacement relies on an appropriate initial-deformation of the regular grid. In this work, a hybrid registration of landmark-based and free-form deformation is proposed and applied to lung CT images. Corresponding landmark-pairs are detected and matched by 3D SIFT (scale-invariant feature transform). Using the landmark pairs, Moving Least Squares (MLS) is applied for deforming the regular grid. Utilizing the deformed control-point grid, a landmark-constrained FFD registration obtains a final registration result. Since this landmark-based deformation approach can obtain smooth and local deformation of the control-point grid, the proposed registration can directly start with the initialized control-point grid, without the need for any coarse-to-fine processing. It was experimentally demonstrated that the proposed hybrid-registration method outperforms conventioal FFD registration in terms of efficiency and accuracy.
Background memory area estimation for multidimensional signal processing systems

Memory cost is responsible for a large amount of the chip and/or board area of customized video and image processing system realizations. In this paper, we present a novel technique-founded on data-flow analysis which allows one to address the problem of background memory size evaluation for a given nonprocedural algorithm specification, operating on multidimensional signals with affine indexes. Most of the target applications are characterized by a huge number of signals, so a new polyhedral data-flow model operating on groups of scalar signals is proposed. These groups are obtained by a novel analytical partitioning technique, allowing to select a desired granularity, depending on the application complexity. The method incorporates a way to tradeoff memory size with computational and controller complexity. >
Adaptive play in Texas Hold'em Poker

We present a Texas Hold'em poker player for limit headsup games. Our bot is designed to adapt automatically to the strategy of the opponent and is not based on Nash equilibrium computation. The main idea is to design a bot that builds beliefs on his opponent's hand. A forest of game trees is generated according to those beliefs and the solutions of the trees are combined to make the best decision. The beliefs are updated during the game according to several methods, each of which corresponding to a basic strategy. We then use an exploration-exploitation bandit algorithm, namely the UCB (Upper Confidence Bound), to select a strategy to follow. This results in a global play that takes into account the opponent's strategy, and which turns out to be rather unpredictable. Indeed, if a given strategy is exploited by an opponent, the UCB algorithm will detect it using change point detection, and will choose another one. The initial resulting program, called Brennus, participated to the AAAI'07 Computer Poker Competition in both online and equilibrium competition and ranked eight out of seventeen competitors.
Neural Networks and Graph K-Partitioning

With the emergence of neural network architectures, com binatorial optimization problems and NP-complete problems may be tackled with a new attention combining biology, physics and data processing. This paper deals with one of these problems: the graph K-partitioning. After a brief critical review of the conventional meth ods, we show how a particular vectorial encoding associated with this problem produces original neural network methods. Through different graph families, a comparative analysis of our approaches with one of the best conventional algorithms is developed.
Learning from the History of Distributed Query Processing - A Heretic View on Linked Data Management

The vision of the Semantic Web has triggered the development of various new applications and opened up new directions in research. Recently, much effort has been put into the development of techniques for query processing over Linked Data. Being based upon techniques originally developed for distributed and federated databases, some of them inherit the same or similar problems. Thus, the goal of this paper is to point out pitfalls that the previous generation of researchers has already encountered and to introduce the Linked Data as a Service as an idea that has the potential to solve the problem in some scenarios. Hence, this paper discusses nine theses about Linked Data processing and sketches a research agenda for future endeavors in the area of Linked Data processing.
How Language Is Embodied in Bilinguals and Children with Specific Language Impairment

This manuscript explores the role of embodied views of language comprehension and production in bilingualism and specific language impairment. Reconceptualizing popular models of bilingual language processing, the embodied theory is first extended to this area. Issues such as semantic grounding in a second language and potential differences between early and late acquisition of a second language are discussed. Predictions are made about how this theory informs novel ways of thinking about teaching a second language. Secondly, the comorbidity of speech, language, and motor impairments and how embodiment theory informs the discussion of the etiology of these impairments is examined. A hypothesis is presented suggesting that what is often referred to as specific language impairment may not be so specific due to widespread subclinical motor deficits in this population. Predictions are made about how weaknesses and instabilities in speech motor control, even at a subclinical level, may disrupt the neural network that connects acoustic input, articulatory motor plans, and semantics. Finally, I make predictions about how this information informs clinical practice for professionals such as speech language pathologists and occupational and physical therapists. These new hypotheses are placed within the larger framework of the body of work pertaining to semantic grounding, action-based language acquisition, and action-perception links that underlie language learning and conceptual grounding.
Pattern-Based Statistical Machine Translation for NTCIR-10 PatentMT

Pattern-based machine translation is a very traditional machine translation method that uses translation patterns and translation word (phrase) dictionaries. The characteristic of this translation method is that high-quality translation results can be obtained if the input sentence matches the translation pattern and this translation pattern is correct. However, translation patterns and translation word dictionaries are usually made manually. Therefore, there are many costs in making a pattern-based machine translation system. We propose making translation patterns and translation word dictionaries automatically by using statistical machine translation methods. Using these methods, we decreased the costs in making a pattern-based machine translation system. We demonstrate the effectiveness of the proposed method in a Japanese-English machine translation patent task (NTCIR-10). We obtained good results.
Prediction of Distal Arm Posture from Shoulder Posture during Three-Dimensional

C5/C6 tetraplegic patients may be able to use voluntary shoulder motion as command signals for ipsilateral functional electrical stimulation (FES). We propose that natural synergies between the proximal and distal limb trajectories during goal oriented reaching can be used as a foundation for a high level FES controller that could predict distal joint kinematics from the voluntary movements of the shoulder. In this study we examined the ability of artificial neural networks to identify these synergies from data recorded during 3D reaching in a large extrapersonal space. Furthermore, we examined how the inclusion of additional inputs and outputs affected neural network performance.
A Sequential Frame Method for Traffic Sign Detection

Based on the sequential video frame, a method to detect the traffic sign was proposed. The color image was mapped from RGB to improved HSI color space first, and then converted it to binary image according the color of signs. The shape features were used to remove some noise, and the improved Hough transform was used to locate the areas of traffic signs. Compared to the existed detection algorithm, a previous frame was used as a priori information to reduce the treatment region, then to decrease the computation. In order to prevent the undetected region, a full inspection was processed when the number of consecutive frames which detected the traffic signs was more than the threshold. The results show that the method in this paper performs better than the traditional method.
The Benefits of Interaction for Learning Sequential Predictions

Sequential prediction problems arise commonly in many areas of robotics and information processing. Examples of such problems include: 1) imitation learning, where a learner must predict a sequence of actions given observations revealed over time to perform a task, based on previously observed demonstrations; 2) structured prediction, which can often be tackled by constructing the structured output from a sequence of interdependent predictions as in SEARN [1]; 3) model-based reinforcement learning (MBRL), where we learn a model of a system from observations in order to predict its behavior under various action sequences for planning. Learning in such sequential problems is challenging as the predictor and data-generation process are inextricably intertwined. This often leads to a significant mismatch between the distribution of observed data during training and test executions (see Figure 1). Naively applying statistical learning methods can yield a predictor that makes good predictions during training but performs badly at the task during test execution.
Moderating Effect of Hofstede's Cultural Values On The Locus of Control/Job Performance Relationship of Managers In USA, Mexico, South Korea, and Hong Kong

This study evaluates relationships between job performance, locus of control,and cultural values. Survey data was collected from 265 managers working for UScontrolled firms located in the US, Hong Kong, South Korea and Mexico. Findingsindicate that there is a stronger relationship between reported job performance and amanager’s personality versus the manager’s culture. These results should help multinationalfirms better understand a significant influence on the job performance ofmid-level managers working in different countries. US controlled, manufacturingmultinational firms can feel confident when designing management control systemsat the individual level regardless of the country in which they operate.
MATE PREFERENCE FOR A PHENOTYPICALLY PLASTIC TRAIT IS LEARNED, AND MAY FACILITATE PREFERENCE‐PHENOTYPE MATCHING

Fixed, genetically determined, mate preferences for species whose adult phenotype varies with rearing environment may be maladaptive, as the phenotype that is most fit in the parental environment may be absent in the offspring environment. Mate preference in species with polyphenisms (environmentally dependent alternative phenotypes) should therefore either not focus on polyphenic traits, be polyphenic themselves, or learned each generation. Here, we test these alternative hypotheses by first describing a female‐limited seasonal polyphenism in a sexually dimorphic trait in the butterfly Bicyclus anynana, dorsal hindwing spot number (DHSN), and then testing whether male and female mate preferences for this trait exist, and whether they are seasonally polyphenic, or learned. Neither naïve males nor naïve females in either seasonal form exhibited mating preferences for DHSN. However, males, but not females, noticed DHSN variation and learned mate preferences for DHSN. These results suggest that individuals may accommodate environmentally dependent variation in morphological traits via learned mate preferences in each generation, and that learned mate preference plasticity can be sexually dimorphic.
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Here we seek to understand the similarities and differences between two speech recognition approaches, namely the HMM/ANN hybrid and the posterior-based segmental model. Both these techniques create local posterior probability estimates and combine these estimates into global posteriors – but they are built on somewhat different concepts and mathematical derivations. The HMM/ANN hybrid combines the local estimates via a formulation that is inherited from the generative HMM concept, while the components of the segment-based model correspond quite directly to the two subtasks of phonetic decoding: segmentation and classification. In this paper we attempt to identify the corresponding components of the segmental model within the hybrid model, with the intent of gaining an insight from this unusual point of view. As regards one of these components, the segment-based phone posteriors, we show that the independence-based product rule combination applied in the hybrid produces strongly biased estimates. As for the other component, the segmentation probability factor, we argue that it is present in the hybrid thanks to the bias of the product rule – that is, the product rule goes wrong in such a special way that it helps the model find the best segmentation of the input. To prove this assertion, we combine this bias with the posterior estimates obtained by averaging, and find that the resulting 'averaging hybrid' slightly outperforms the standard one on a phone recognition task and a word recognition task as well. Overall we conclude that the contribution of the product rule to the decoding process is just as important for the segmentation subtask as it is for the segment classification subtask.
Self-Motion and the Hippocampal Spatial Metric

Self-motion signals are sufficient for animal navigation (“path integration”) and for updating hippocampal location-specific firing. The contributions of ambulatory, vestibular, and optic self-motion signals to CA1 unit activity and EEG were studied while rats either walked or drove a car between locations on a circular track (referred to as WALK and CAR, respectively) or experienced pseudomotion, in which the animal was stationary and the environment was rotated (WORLD). Fewer pyramidal cells expressed place fields during CAR and those that did exhibited substantially larger place fields. The number of theta cycles required to traverse a place field increased, whereas the slope of the theta phase of firing versus position function was reduced. The presence and/or location of place fields were not well correlated between conditions. These effects were even more accentuated during WORLD. These results are not explainable by a simple “smearing out” of place fields but, in terms of size of place fields relative to the track size, are comparable with what would be observed if the track circumference was reduced and the animal moved around it at a correspondingly slower speed. Theta (and its 14–18 Hz harmonic) power were dependent on velocity, but the gain of this function was substantially reduced during CAR and WORLD, again as if the rat were moving more slowly. The spatial scale over which the hippocampal population vector is updated appears to be derived primarily from the gain of a self-motion velocity signal with approximately equal components derived from ambulation, vestibular, and optic-flow signals.
Quality evaluation of HEVC compressed CT images

Medical imaging and compression are very crucial areas in terms of complexity in Image acquisition and processing. Lossless or near lossless compression is desirable for telemedicine or for storing a large amount of data; as a better acquisition (varying from 1 Tesla to 7 Tesla) larger the data volume. In this work HEVC/H.265 encoding is used to compress Computer Tomography (CT) Images. Quality evaluation is done on the basis of an objective analysis with parameter SSIM, MSE, PSNR etc. This work also reported the scheme of HEVC and its results with INTRA prediction and CABAC coding.