Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Representation of Limb Motor Programs in Arrays of Adjustable Pattern Generators

This paper describes the current state of our exploration of how motor program concepts may be related to neural mechanisms. We have proposed a model of sensorimotor networks with architectures inspired by the anatomy and physiology of the cerebellum and its interconnections with the red nucleus and the motor cortex. We proposed the concept of rubrocerebellar and corticocerebellar information processing modules that function as adjustable pattern generators (APGs) capable of the storage, recall, and execution of motor programs. The APG array model described in this paper extends the single APG model of Houk et al. (1990) to an array of APGs whose collective activity controls movement of a simple two degree-of-freedom simulated limb. Our objective was to examine the APG array theory in a simple computational framework with a plausible relationship to anatomy and physiology. Results of simulation experiments show that the APG array model is capable of learning how to control movement of the simulated limb by adjusting distributed motor programs. Although the model is based on many simplifying assumptions, and the simulated motor control task is much simpler than an actual reaching task, these results suggest that the APG array model may provide a useful step toward a more comprehensive understanding of how neural mechanisms may generate motor programs.
Nature of the scrapie agent: current status of facts and hypotheses.

Scrapie is a slow infection affecting the central nervous system (CNS) of sheep and goats (Dickinson, 1976). Experimentally the agent has been passaged in mice, hamsters and a number of other laboratory animal species. Kuru and Creutzfeldt-Jakob disease (CJD) are related diseases occurring in humans (Gajdusek, 1977). These diseases are caused by agents that have unconventional characteristics compared to those of known animal viruses (Gajdusek, 1977). The unusual physical, chemical and biological properties of the scrapie agent have led to an abundance of theories concerning its nature. Each of the major classes of macromolecules has been suggested as an important component of the scrapie agent. Hypotheses have suggested that scrapie agent is composed exclusively of protein (Griffith, 1967), exclusively of polysaccharide (Gibbons & Hunter, 1967), or exclusively of nucleic acid (Diener, 1972). Other hypotheses have suggested that it is a replicating membrane component (Gibbons & Hunter, 1967) or a filterable virus (Eklund et al., 1963). This is but a partial list of the theoretical constructs that have been proposed.
Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.
Web taxonomy integration using support vector machines

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
A Novel Approach to Hill Cipher

Cipher is a first polygraphic substitution cipher that works on digraphs, trigraphs (3 letter squares) or hypothetically blocks of any magnitude. The Hill Cipher utilizes a region of science called Linear Algebra, and specifically requires the client to have a rudimentary knowledge of matrices. It additionally makes utilization of Modulo Arithmetic (like the Affine Cipher). To perform decryption, the hill cipher requires the inverse of the key matrix. This is the major shortcoming of Hill cipher since every key matrix is not invertible. We will propose a new variant of hill cipher, which will find the decryption of the cipher text even when the key matrix is non invertible.
From Visual Attributes to Adjectives through Decompositional Distributional Semantics

As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as “visual phrases”, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.
Compiler mitigations for time attacks on modern x86 processors

This paper studies and evaluates the extent to which automated compiler techniques can defend against timing-based side channel attacks on modern x86 processors. We study how modern x86 processors can leak timing information through side channels that relate to data flow. We study the efficiency, effectiveness, portability, predictability and sensitivity of several mitigating code transformations that eliminate or minimize key-dependent execution time variations. Furthermore, we discuss the extent to which compiler backends are a suitable tool to provide automated support for the proposed mitigations.
Development and the Baldwin Effect

Baldwin's classic hypothesis states that behavioral plasticity can speed evolution by (a) smoothing the fitness landscape and (b) indirect genetic assimilation of acquired characteristics. This latter phase demands a strong correlation between genotype and phenotype space. But the natural world shows signs of this correlation at only a very coarse level, since the intervening developmental process greatly complicates the mapping from genetics to physiology and ethology. Hence, development appears to preclude a strong Baldwin effect. However, by adding a simple developmental mechanism to Hinton and Nowlan's classic model of the Baldwin effect, and by allowing evolution to determine the proper balance between direct and indirect mapping of genome to phenotype, this research reveals several different effects of development on the Baldwin effect, some promoting and others inhibiting. Perhaps the most interesting result is an evolved cooperation between direct blueprints and indirect developmental recipes in searching for unstructured and partially structured target patterns in large, needle-in-the-haystack fitness landscapes.
Molecular organization of Gram-negative peptidoglycan

The stress-bearing component of the bacterial cell wall—a multi-gigadalton bag-like molecule called the sacculus—is synthesized from peptidoglycan. Whereas the chemical composition and the 3-dimensional structure of the peptidoglycan subunit (in at least one conformation) are known, the architecture of the assembled sacculus is not. Four decades' worth of biochemical and electron microscopy experiments have resulted in two leading 3-D peptidoglycan models: “Layered” and “Scaffold”, in which the glycan strands are parallel and perpendicular to the cell surface, respectively. Here we resolved the basic architecture of purified, frozen-hydrated sacculi through electron cryotomography. In the Gram-negative sacculus, a single layer of glycans lie parallel to the cell surface, roughly perpendicular to the long axis of the cell, encircling the cell in a disorganized hoop-like fashion.
Fault-tolerant networked control systems under varying load

In this paper, a simulation study is made to test the fault tolerant ability of networked machines. This ability is introduced by reallocating loads in case of controller failure. Also, the increase in machine speed for higher production, is considered. The maximum speed of operation of individual machines and fault tolerant production-lines, is studied. All machine networks of this study are built on-top-of switched Gigabit Ethernet in star topology.
Challenges in conducting community-based trials of primary prevention of cardiovascular diseases in resource-constrained rural settings

Cardiovascular diseases account for almost half of all deaths from noncommunicable diseases, and almost 80% of these deaths occur in low- and middle-income countries such as India. The PrePAre (Primary pREvention strategies at the community level to Promote treatment Adherence to pREvent cardiovascular disease) trial was a primary prevention trial of community health workers aimed at improving adherence to prescribed pharmacological and nonpharmacological therapies in cardiovascular diseases. It was conducted at three geographically, culturally and linguistically diverse sites across India, comprising 28 villages and 5699 households. Planning and implementing large-scale community-based trials is filled with numerous challenges that must be tackled, while keeping in mind the local community dynamics. Some of the challenges are especially pronounced when the focus of the activities is on promoting health in communities where treating disease is considered a priority rather than maintaining health. This report examines the challenges that were encountered while performing the different phases of the trial, along with the solutions and strategies used to tackle those difficulties. We must strive to find feasible and cost-effective solutions to these challenges and thereby develop targeted strategies for primary prevention of cardiovascular diseases in resource-constrained rural settings.
Identification of Indic Scripts on Torn-Documents

Questioned Document Examination processes often encompass analysis of torn documents. To aid a forensic expert, automatic classification of content type in torn documents might be useful. This helps a forensic expert to sort out similar document fragments from a pile of torn documents. One parameter of similarity could be the script of the text. In this article we propose a method to identify the script in document fragments. Torn documents are normally characterized by text with arbitrary orientation. We use Zernike moment -- based feature that is rotation invariant together with Support Vector Machine (SVM) to classify the script type. Subsequently gradient features are used for comparative analysis of results between rotation dependent and rotation invariant feature type. We achieved an overall script-identification accuracy of 81.39% when dealing with 11 different scripts at character/connected-component level and 94.65% at word level.
Effect of "Sound Fonts" in an Aural Presentation

This paper deals with research for the design of "sound fonts" and development of an evaluation methodology suitable for use with non visual presentation based on the speech modality or on multimodality (speech and tactile). The work hypothesis of the study presented here relies on the fact that both structure and typographic attributes increase the comprehension process in visual presentation. Based on this constant, the Human Computer Interaction (HCI) question is to find alternative sounds or prosodic variants to display the typographic attributes -bold, italic-, for instance. This question takes part of the paradigm of the information accessibility problems.
Vibrotactile Displays for Improving Spatial Awareness

This paper discusses the effects of tactile cueing on performance in a fixed base simulated helicopter hover task. Sixteen active-duty military helicopter pilots participated in a repeated measures two minute stationary hover test. Participants performed the hover task under four treatment conditions; tactile cueing (on/off) and secondary arithmetic task (on/off). Following each 2-minute hover, participants reported subjective situation awareness using the five point China Lake Situation Awareness Scale (CLSA). Total time on target improved with tactile cueing with a significant task by display interaction. Performance on the arithmetic task remained consistent across conditions and no significant change was seen on CLSA scores between the tactile/no tactile conditions. Results demonstrate that tactile cues can be used to improve performance in spatial tasks, such as hovering a helicopter, especially in the presence of distracting secondary workload tasks. The lack of a significant difference in SA scores may be more related to the pilots' overall high confidence in their SA, despite their variation in performance, and highlights some of the difficulties encountered when attempting to measure SA.
Positive and negative affect after diagnosis of advanced cancer

Anxiety and depression are studied thoroughly in patients with advanced cancer. However, little is known about the nature of mood disorders in this stage of the disease. We studied positive and negative affect in patients who have had a diagnosis of advanced cancer, and examined how these are related to anxiety and depression, and to other patient and care factors. One hundred and five patients filled out a written questionnaire and were interviewed personally. The PANAS positive affect scores were lower than those in the general population, but the negative affect scores were fairly similar. We found a rather low prevalence of depression (13%) and anxiety (8%) as measured by the HADS. The emotional problems patients mentioned most frequently were anxiety about metastases (26%), the unpredictability of the future (18%) and anxiety about physical suffering (15%). Both positive and negative affect were most strongly related to patient's sense of meaning and peace. We conclude that distinguishing positive and negative affect enhances the understanding of psychological distress of patients with advanced cancer, that seems to be mainly caused by low levels of positive affect. Several theories are discussed to explain this finding, that may contribute to efforts to improve care for these patients. Copyright © 2004 John Wiley & Sons, Ltd.
Concept for coring from a low-mass rover

Future Mars missions such as the Mars Sample Return (MSR) mission may benefit from core sample acquisition from a low-mass rover where the rover cannot be assumed to be stationary during a coring operation. Manipulation from Mars rovers is currently done under the assumption that the rover acts as a stationary, stable platform for the arm. An MSR mission scenario with a low-mass rover has been developed and the technology needs have been investigated. Models for alternative types of coring tools and tool-environment interaction have been developed and input along with wheel-soil interaction models into the Stanford Simulation & Active Interfaces (SAI) simulation environment to enable simulation of coring operations from a rover. Coring tests using commercial coring tools indicate that the quality of the core is a critical criterion in the system design. Current results of the models, simulation, and coring tests are provided
Dielectric properties of hydrogen-incorporated chemical vapor deposited diamond thin films

Diamond thin films with a broad range of microstructures from a ultrananocrystalline diamond (UNCD) form developed at Argonne National Laboratory to a microcrystalline diamond (MCD) form have been grown with different hydrogen percentages in the Ar∕CH4 gas mixture used in the microwave plasma enhanced chemical vapor deposition (CVD) process. The dielectric properties of the CVD diamond thin films have been studied using impedance and dc measurements on metal-diamond-metal test structures. Close correlations have been observed between the hydrogen content in the bulk of the diamond films, measured by elastic recoil detection (ERD), and their electrical conductivity and capacitance-frequency (C-f) behaviors. Addition of hydrogen gas in the Ar∕CH4 gas mixture used to grow the diamond films appears to have two main effects depending on the film microstructure, namely, (a) in the UNCD films, hydrogen incorporates into the atomically abrupt grain boundaries satisfying sp2 carbon dangling bonds, resulting in inc.
Register Transfer Level Workflow for Application and Evaluation of Soft Error Mitigation Techniques

Digital circuits exposed to environments with high levels of radiation, such as those found in High Energy Physics experiments, are prone to Single Event Upsets. These upsets impact the reliability of the circuit. In order to mitigate the effects of the upsets, several well-known techniques for use with register transfer level (RTL) circuit descriptions have been proposed over the years. They typically have a large impact on circuit size and power consumption. Therefore, they are often applied only to the more critical modules of the system. Additionally, the manual implementation of those techniques has a significant cost in terms of time and design effort, involving both RTL changes and tailoring of the synthesis flow to avoid optimizing away the additional hardware. This paper describes an automated workflow that reduces the time for implementing SEU mitigation techniques, avoids the errors caused by manual alteration of the RTL descriptions, and enables the designer to explore different alternatives quickly. The paper describes the application of the workflow to three digital circuits and discusses the data obtained from the implementation of the different mitigation techniques.
Parallel independent component analysis using an optimized neurovascular coupling for concurrent EEG-fMRI sources

The complexity of the human brain and the limitation of any one imaging approach motivates the need for multimodal measurements to better understand cerebral processing. A very natural goal is to integrate electrophysiological and hemodynamic activity. Among them, concurrent EEG-fMRI studies have shown great promise for understanding intrinsic brain properties yet analyzing such data presents a significant methodological challenge. Here, we propose a multivariate parallel ICA decomposition incorporating dynamic neurovascular coupling for concurrent EEG-fMRI recordings. The goal of our algorithm is to fuse multimodal EEG-fMRI information and detect/interpret the relationship between electrophysiological and hemodynamic sources via a temporal neurovascular connection enhancement. We analyze the performance of the algorithm on a valid simulation based on real EEG and fMRI components (sources) from our previous works and a neurovascular coupling built from an extended ‘balloon model’. The results from our simulations yield an accurate source tracking and linkage for concurrent EEG-fMRI, and provide a novel and efficient way to combine EEG and hemodynamic responses.