Miracle-dz/UniFAR-FTUs huggingface dataset:
Perirhinal and parahippocampal cortices of the macaque monkey: Cortical afferents

Neuropsychological studies have recently demonstrated that the macaque monkey perirhinal (areas 35 and 36) and parahippocampal (areas TH and TF) cortices contribute importantly to normal memory function. Unfortunately, neuroanatomical information concerning the cytoarchitectonic organization and extrinsic connectivity of these cortical regions is meager. We investigated the organization of cortical inputs to the macaque monkey perirhinal and parahippocampal cortices by placing discrete injections of the retrograde tracers fast blue, diamidino yellow, and wheat germ agglutinin conjugated to horseradish peroxidase throughout these areas. We found that the macaque monkey perirhinal and parahippocampal cortices receive different complements of cortical inputs. The major cortical inputs to the perirhinal cortex arise from the unimodal visual areas TE and rostral TEO and from area TF of the parahippocampal cortex. The perirhinal cortex also receives projections from the dysgranular and granular subdivisions of the insular cortex and from area 13 of the orbitofrontal cortex. In contrast, area TF of the parahippocampal cortex receives its strongest input from more caudal visual areas V4, TEO, and caudal TE, as well as prominent inputs from polymodal association cortices, including the retrosplenial cortex and the dorsal bank of the superior temporal sulcus. Area TF also receives projections from areas 7a and LIP of the posterior parietal lobe, insular cortex, and areas 46, 13, 45, and 9 of the frontal lobe. As with area TF, area TH receives substantial projections from the retrosplenial cortex as well as moderate projections from the dorsal bank of the superior temporal sulcus; unlike area TF, area TH receives almost no innervation from areas TE and TEO. It does, however, receive relatively strong inputs from auditory association areas TE and TEO. It does, however, receive relatively strong inputs from auditory association areas on the convexity of the superior temporal gyrus. © 1994 Wiley‐Liss, Inc.
Low complexity multifocus image fusion in discrete cosine transform domain

This paper presents a low complex, highly energy efficient sensor image fusion scheme explicitly designed for wireless visual sensor systems equipped with resource constrained, battery powered image sensors and employed in surveillance, hazardous environment like battlefields etc. Here an energy efficient simple method for fusion of multifocus images based on higher valued AC coefficients calculated in discrete cosine transform domain is presented. The proposed method overcomes the computation and energy limitation of low power devices and is investigated in terms of image quality and computation energy. Simulations are performed using Atmel ATmega128 processor of Mica 2 mote, to measure the resultant energy savings and the simulation results demonstrate that the proposed algorithm is extremely fast and consumes only around 1% of energy consumed by conventional discrete cosine transform based fusion schemes. Also the simplicity of our proposed method makes it more appropriate for real-time applications.
Fast timing analysis for hardware-software co-synthesis

At the current time, an iterative approach seems to be best suited for hardware/software partitioning in hardware/software co-synthesis with time constraints. To check the timing constraints, the iteration loop contains a timing analysis. Only computation time-intensive RT-level simulation provides sufficient timing precision for complex processor architectures. We present a hardware/software timing analysis, which comes close to the precision of an RT-level simulation in a fraction of the computation time and, thus, removes a bottleneck from iterative hardware/software co-synthesis. We present some results for our co-synthesis system COSYMA. <<ETX>>
Context-aware mobile music recommendation for daily activities

Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.
Dissipation Function and ViBe Based Smoke Detection in Video

A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected the transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke&#xa1;&#xaf;s motion characteristics as well as detecting other moving targets. Since smoke has semi-transparent parts, the things which are covered by these parts can be recovered by the atmospheric scatter model. The similarity between the recovered parts and the original background parts in the same position is calculated by Normalized Cross Correlation (NCC) and the original background&#xa1;&#xaf;s value is selected from the frame which is nearest to the current frame. The parts with high similarity are considered as smoke parts.
Resource Allocation for Selection-Based Cooperative OFDM Networks

This paper considers resource allocation to achieve max-min fairness in a selection-based orthogonal frequency division multiplexing network wherein source nodes are assisted by fixed decode-and-forward relays. Crucial design questions such as whether to relay, r elay assignment and power allocation form a combinatorial problem with exponential solution complexity. The first set of problems assume perfect source-relay channels and that sources distribute power equally across subcarriers. The solutions based on these simplifications help illustrate our general methodol ogy and also why these solutions provide tight bounds. We then formulate the general problem of transmission strategy selection, relay assignment, and power allocation at the sources and relays considering all communication channels, i.e., imperfect sourcerelay channels. In both sets of problems, transmissions over subcarriers are assumed to be independent. Given the attendant problems of synchronization and the implementation using a FFT/IFFT pair, resource allocation at the subcarrier level appears impractical. We , therefore, consider resource allocation at the level of the entire OFDM block. While optimal resource allocation requires an exhaustive search, we develop tight bounds with lower complexity. Finally, we propose a decentralized block-based relaying scheme. Simulation results using the COST-231 channel model show that this scheme yields close-tooptimal performance while offering many computational benefits.
Scalable Communication-aware Task Mapping Algorithms for Interconnected Multicore Systems

Communication-aware task mapping algorithms, which map parallel tasks onto processing nodes according to the communication patterns of applications, are essential to reduce the communication time in modern high performance computing. In this paper, we design algorithms specifically for interconnected multicore systems, whose architectural property, namely small number of cores per node, large number of nodes, and large performance gap between the communication within a multicore and among multicores, had brought new challenges and opportunities to the mapping problem. Let k be the number of cores per multicore and n be the number of tasks. We consider the practical case that k ≪ n for k = 2, 4, and 6. The designed algorithms are optimal for the mapping measurement, called Maximum Interconnective Message Size (MIMS), and of time complexity merely O(m log m) for m communication pairs. Thus, they are highly scalable for large applications. We had experimented the algorithms on the IBM Blue Gene/P system for two synthetic benchmarks and two applications. The results show good communication performance improvement.
An exploration of implications for the development of Pilates instructor system through identification of instructors’ difficulties

This study is aimed at exploring ideas for the development of Pilates instructor qualification system by identifying a range of difficulties Pilates instructors are experiencing. Open-ended questionnaires and semi-structured interviews were conducted to collect data before they were analyzed with inductive content analysis method. In consideration of the difficulties Pilates instructors experience during three qualification stages (before-during-after qualification education), three key categories were incorporated in the collected data: (1) lack of information on Pilates qualification system, (2) difficulties in understanding the human anatomy related with the diverse movements taught in Pilates classes and its application to Pilates practice, (3) need for professional development through retraining. Based on these findings, a need for rethinking the monitoring and evaluation process for Pilates qualification system and Pilates education in Korea was identified. In addition, we need to summarize and offer information on a range of Pilates qualifications. And the quality of Pilates instructor education program should be improved as well by proving them a range of teaching methods including microteaching, discussion-based lessons as well as reading and writing sessions and other necessary teaching media.
“Have You Ever Seen This Face?” – Individual Differences and Event-Related Potentials during Deception

Deception studies emphasize on the importance of event-related potentials (ERP) for a reliable differentiation of the underlying neuro-cognitive processes. The stimulus-locked parietal P3 amplitude has been shown to reflect stimulus salience but also attentional control available for stimulus processing. Known stimuli requiring truthful responses (targets) and known stimuli requiring deceptive responses (probes) were hypothesized to be more salient than unknown stimuli. Thus, a larger P3 was predicted for known truthful and deceptive stimuli than for unknown stimuli. The Medial Frontal Negativity (MFN) represents the amount of required cognitive control and was expected to be more negative to known truthful and deceptive stimuli than to unknown stimuli. Moreover, we expected higher sensitivity to injustice (SI-perpetrator) and aversiveness (Trait-BIS) to result in more intense neural processes during deception. N = 102 participants performed a deception task with three picture types: probes requiring deceptive responses, targets requiring truthful responses to known stimuli, and irrelevants being associated with truthful responses to unknown stimuli. Repeated-measures ANOVA and fixed-links modeling suggested a more positive parietal P3 and a more negative frontal MFN to deceptive vs. irrelevant stimuli. Trait-BIS and SI-perpetrator predicted an increase of the P3 and a decrease of the MFN from irrelevants to probes. This suggested an intensification of stimulus salience and cognitive control across picture types in individuals scoring either higher on Trait-BIS or higher on SI-perpetrator. In contrast, individuals with both higher Trait-BIS and higher SI-perpetrator scores showed a less negative probe-MFN suggesting that this subgroup invests less cognitive control to probes. By extending prior research we demonstrate that personality modulates stimulus salience and control processes during deception.
Cooperative source number estimation for cognitive radio networks

In this paper, we consider a cognitive radio (CR) network operating on a spectrum reserved for licensed primary user (PU) by employing spatial spectrum sharing (SS). When the secondary users (SUs) are equipped with multiple antennas, they can perform SS without interfering with the PU by orthogonalizing the secondary transmission with the interference channel . In order to avoid interference and to maximize the capacity of the secondary transmissions, it is essential that the SUs determine the dimension of the orthogonal transmission space. In CR networks where no cooperation is provided by the PU, this information can be obtained by estimating the number of primary transmit streams at the secondary terminals. We study this issue when the secondary network performs cooperative estimation by using the minimum description length (MDL) algorithm to obtain the independent estimates and propose a decision fusion rule which takes into account the estimation bias of MDL. We will show that the proposed method can outperform conventional decision fusion rule and that it provides additional protection for the PU in the case of estimation error.
Higher-order constraint simplification in dependent type theory

Higher-order unification is undecidable, but has fragments which admit practical algorithms, which are used extensively in logical frameworks. For example, it is decidable whether unification problems in the pattern fragment are solvable, and they enjoy unique most general unifiers when they are. Often we wish to treat more general problems which are nonetheless solvable by incrementally reasoning about the parts of them that fall in the pattern fragment after more progress has been made --- to this end constraint simplification algorithms have been proposed, which work on the so-called dynamic pattern fragment. However, their theory turns out to be surprisingly subtle. The constraint simplification algorithm implemented in Twelf, for instance, is not terminating, despite the sketch of a proof of its termination in the literature. We describe and prove correct a new, terminating constraint simplification algorithm for a dynamic pattern fragment of higher-order unification in a dependent type system, and discuss its implementation.
Microdissected Region-specific Gene Expression Analysis with Methacarn-fixed, Paraffin-embedded Tissues by Real-time RT-PCR

We have previously shown methacarn to be a versatile fixative for analysis of proteins, DNA, and RNA in paraffin-embedded tissues (PETs). In this study we analyzed its suitability for quantitative mRNA expression analysis of microdissected PET specimens using a real-time RT-PCR technique. Fidelity of expression in the methacarn-fixed PET sections, with reference to dose-dependent induction of cytochrome P450 2B1 in the phenobarbital-treated rat liver, was high in comparison with the unfixed frozen tissue case, even after hematoxylin staining. RNA yield from methacarn-fixed PET sections was equivalent to that in unfixed cryosections and was also not significantly affected by hematoxylin staining. Correlations between the expression levels of target genes and input amounts of extracted RNA in the range of 1–1000 pg were very high (correlation coefficients >0.98), the regression curves being similar to those with unfixed cryosections. Although cell numbers should be optimized for each target gene/tissue, ≥200 cells were necessary for accurate measurement in 10-μm-thick rat liver sections judging from the variation of measured value in small microdissected areas. These results indicate high performance with methacarn, close to that of unfixed tissues, regarding quantitative expression analysis of mRNAs in microdissected PET-specimens. (J Histochem Cytochem 52:903–913, 2004)
SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins

SDPpred (Specificity Determining Position prediction) is a tool for prediction of residues in protein sequences that determine the proteins' functional specificity. It is designed for analysis of protein families whose members have biochemically similar but not identical interaction partners (e.g. different substrates for a family of transporters). SDPpred predicts residues that could be responsible for the proteins' choice of their correct interaction partners. The input of SDPpred is a multiple alignment of a protein family divided into a number of specificity groups, within which the interaction partner is believed to be the same. SDPpred does not require information about the secondary or three-dimensional structure of proteins. It produces a set of the alignment positions (specificity determining positions) that determine differences in functional specificity. SDPpred is available at http://math.genebee. msu.ru/~psn/.
Effects of Hypnotic Analgesia and Virtual Reality on the Reduction of Experimental Pain Among High and Low Hypnotizables

Abstract This research compared a no-treatment control condition and 3 experimentally induced pain treatment conditions: (a) virtual reality distraction (VRD), (b) hypnotic analgesia (HA), and (c) HA + VRD in relieving finger-pressure pain. After receiving baseline pain stimulus, each participant received hypnosis or no hypnosis, followed by VRD or no VRD during another pain stimulus. The data analysis indicated that, overall, all 3 treatments were more effective compared to the control group, irrespective of whether it involved hypnotic analgesia, virtual reality distraction, or both (hypnosis and virtual reality). Nevertheless, the participants responded differently to the pain treatment, depending on the hypnotizability level. High hypnotizables reported hypnotic analgesia, but low hypnotizables did not show hypnotic analgesia. VR distraction reduced pain regardless of hypnotizability.
Comparison Enhances Size Sensitivity: Neural Correlates of Outcome Magnitude Processing

Magnitude is a critical feature of outcomes. In the present study, two event-related potential (ERP) experiments were implemented to explore the neural substrates of outcome magnitude processing. In Experiment 1, we used an adapted gambling paradigm where physical area symbols were set to represent potential relative outcome magnitudes in order to exclude the possibility that the participants would be ignorant of the magnitudes. The context was manipulated as total monetary amount: ¥4 and ¥40. In these two contexts, the relative outcome magnitudes were ¥1 versus ¥3, and ¥10 versus ¥30, respectively. Experiment 2, which provided two area symbols with similar outcome magnitudes, was conducted to exclude the possible interpretation of physical area symbol for magnitude effect of feedback–related negativity (FRN) in Experiment 1. Our results showed that FRN responded to the relative outcome magnitude but not to the context or area symbol, with larger amplitudes for relatively small outcomes. A larger FRN effect (the difference between losses and wins) was found for relatively large outcomes than relatively small outcomes. Relatively large outcomes evoked greater positive ERP waves (P300) than relatively small outcomes. Furthermore, relatively large outcomes in a high amount context elicited a larger P300 than those in a low amount context. The current study indicated that FRN is sensitive to variations in magnitude. Moreover, relative magnitude was integrated in both the early and late stages of feedback processing, while the monetary amount context was processed only in the late stage of feedback processing.
X R D S @bullet W I N T E R 2 0 14 @bullet V O L . 2 1 @bullet N O . 2 Challenges in Personal Health Tracking: the Data Isn't Enough

Increasingly, personal health data can be tracked and integrated from numerous streams quickly and easily, but our feedback lingers in the land of " show the user a graph and hope. " How can we help people make sense of personal health data? 'em a graph and hope " approach: Surely people will notice the correlations between their different data streams and draw appropriate conclusions if we just show them the data! Never-mind that one third of Americans have low graphical literacy [1] and would be unlikely to make valid inferences from supposedly straightforward graphical data. Asking people to make statistical judgments from graphical data—such as identifying correlations—without providing scaffolding too often leads P ersonal informatics—and particularly personal health tracking—is booming. Apple and Samsung have both announced apps for aggregating personal health data, such as step counts or sleep quality, into one unified interface. This is the first step in achieving a long-standing vision in personal informatics. Giving people one place to see visualizations of all of their health data. A lot of the low-level technical challenges for this vision are being solved: We can sense myriad aspects of health—from step count, to sleep quality, to stress, to respiratory function, to heart rate variability. As users and developers, we can also access this data more easily than before. While it is sometimes cumbersome to get all of that data out of each provider's silo—your Wi-Fi bathroom scale may not have the same API or data format as your pedometer— projects like S Health are breaking down these barriers by encouraging developers to support these APIs or be left behind. What we are left with is the question of what to do with all that data. It remains the case that most health apps take some variant of the " show to bewilderment, or worse, the drawing of spurious conclusions. People— even trained statisticians—are particularly good at seeing patterns in data where there are none. Some of the core challenges for personal health informatics, then, lie in finding and presenting health data to people in useful and meaningful ways—it isn't enough to bring the data into one place. This leads us to ask questions like: What data do we actually need? How can we summarize it
Online mining in unstructured financial information: An empirical study in bulletin news

The Internet produces massive financial unstructured textual information every day. How to utilize these unstructured data effectively is a challenging topic. In the background of A share T+0 and stock option promoting in the China security market, we present a model to recognize the risk and investment opportunity according to the massive online financial textual information. Since the key word vector is in extremely high dimension space and critical in influencing the performance of our forecast models, a manifold learning method is firstly applied to reduce its dimension while keeping essential features. By utilizing financial event study, we secondly apply support vector machines to predict the news type and sentiment value. The model can achieve the intelligent and instant match between textual news and the reactions of stock market. Our results provide prompt supports for financial practitioners to make investment decisions no matter they are in long or in short positions in the market.