Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Recording of Cerebral Blood Oxygenation Changes during Human Brain Activation by Magnetic Resonance Imaging and Near-Infrared Spectroscopy

Changes in cerebral blood oxygenation due to functional activation of the primary sensorimotor cortex during a unilateral finger opposition task were simultaneously mapped by deoxyhemoglobin-sensitive magnetic resonance imaging (MRI) and monitored by near-infrared spectroscopy (NIRS). Activation foci along the contralateral central sulcus displayed task-associated increases in MRI signal intensity, indicating a concomitant decrease of the focal concentration of deoxyhemoglobin. This interpretation was confirmed by simultaneous reductions in deoxyhemoglobin measured optically. Since observation of the latter effect required exact spatial matching of the MRI-detected activation foci and position of the fiber optic bundles (“optodes”) used for transmitting and receiving light, it may be concluded that optical recordings of changes in deoxyhemoglobin during functional challenge probe only a restricted brain tissue region. While deoxyhemoglobin responses seen by NIRS were smaller for ipsi- than for contralateral finger movements, task-related increases in oxyhemoglobin were rather similar between both conditions and, thus, seem to be less specific. Furthermore, no consistent changes were obtained for total hemoglobin during task performance, possibly due to the short timing of the repetitive protocol. In general, results underline, in humans, the hitherto assumed signal physiology for functional brain mapping by oxygenation-sensitive MRI and allow assessment of both constraints and practicability of functional studies by NIRS.
Listeners' Response to Music as a Function of Personality Type

The purpose of this study was to examine listener response to music as a function of the personality variables measured by the Myers-Briggs Type Indicator (MBTI). Undergraduate nonmusic majors (N = 328) who were enrolled in music appreciation courses indicated characteristic responses to music on the Music Listener Response Scale (MLRS) (after Hedden, 1973). The MLRS contained statements that focused on responses to music that were classified as associative, cognitive, physical involvement, or enjoyment. Subjects responded to statements on a scale that ranged from “all of the time” to “none of the time. ” Pearson product-moment correlations were determined between the MIMS and the Myers-Briggs variables. Significant (p < .001) correlations were found between the MLRS and sensing-intuition for the entire sample and subgroups of male and female subjects. A significant (p < .01) correlation was found between judgment-perception and the MLRS for female subjects. MBTI scores were used to identify 16 subgroups according to the 16 possible combinations among the variables. A one-way analysis of variance (ANOVA) was performed for the MLRS composite with Myers-Briggs type as the independent variable having 16 levels. The overall F test was significant [F(15, 312) =3.06, p < . 05] and accounted for 13% of the variance in the MLRS.
Design and optimization of MEMS piezoelectric energy harvesters for improved efficiency

This paper proposes an automated design and optimization methodology for performance improvement of the MEMS unimorph piezoelectric harvesters based on Genetic Algorithm (GA), which can be conducted with minimum human efforts. In this regard, an analytic equation, which can predict harvested voltage in terms of geometric dimensions, is utilized as an objective function of GA. By using micro-fabrication process, we fabricated the optimized MEMS harvesters. The experimental results demonstrate that the prototyped energy harvester has larger generated voltage magnitude by a factor of 31% in comparison with un-optimized ones, along with device area reduction from 3.1 mm2 to 1.55 mm2. Furthermore, a new T-shaped unimorph MEMS harvester with higher conversion efficiency from mechanical vibration to electricity by offering both bending stress and torsion stress, is proposed and fabricated. Our measurement shows that the T-shaped harvester can generate higher voltage by a factor of 96% with reference to the optimized conventional unimorph piezoelectric harvester, while its occupied area is further reduced by 35% to 1.01 mm2.
Graph-Based Semi-Supervised Learning as a Generative Model

This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.
A new metric for space-time block codes with imperfect channel estimates

In this paper, a new metric for the decision rule of the space-time block (STB) codes having imperfect estimate of the channel fading parameters is derived. Estimation errors of these fading parameters have high impact on the performance of the STB code as the decision rule is dependent on these fading parameters. By including the variance of the channel estimation error in the decoder metric derivation of the STB codes, we derive an exact probability distribution function (pdf) of the received signals conditioned on the estimated channel parameters and transmitted symbol sequences. A modified decision rule is easily found from this and compared with the state-of-the-art scheme proposed by Tarokh. Performance comparison shows that significant improvement is achieved in terms of error rate and system complexity, especially for quadrature amplitude modulation (QAM). The derived decision rule converges to the ideal case for no estimation error and to Tarokh's decision rule for high signal-to-noise ratio (SNR).
Toward Pan-Slavic NLP: Some Experiments with Language Adaptation

There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.
An efficient scheme for anonymous communication in IoT

Security and privacy are challenging task in resource constrained in Internet of things (IoT) and Wireless Sensor Networks (WSN). Hence, anonymity and trust are prominent for many common IoT and WSN applications, in which we conceal nodes identity and give confidential data to a trusted node. The majority of exiting IoT security solutions cannot achieve complete security needs and/or have large computation overheads. In this paper we design a secure generic model for IoT and WSN to ensure anonymity, data secrecy and trust between nodes. Our work is based on lightweight key agreement protocol, the Identity Based Encryption (IBE) and Pseudonym Based Encryption (PBC).
Ocean Eddy Dynamics in a Coupled Ocean-Atmosphere Model*

Abstract The role of mesoscale oceanic eddies is analyzed in a quasigeostrophic coupled ocean–atmosphere model operating at a large Reynolds number. The model dynamics are characterized by decadal variability that involves nonlinear adjustment of the ocean to coherent north–south shifts of the atmosphere. The oceanic eddy effects are diagnosed by the dynamical decomposition method adapted for nonstationary external forcing. The main effects of the eddies are an enhancement of the oceanic eastward jet separating the subpolar and subtropical gyres and a weakening of the gyres. The flow-enhancing effect is due to nonlinear rectification driven by fluctuations of the eddy forcing. This is a nonlocal process involving generation of the eddies by the flow instabilities in the western boundary current and the upstream part of the eastward jet. The eddies are advected by the mean current to the east, where they backscatter into the rectified enhancement of the eastward jet. The gyre-weakening effect, which is due.
Segmentation of Nuclear Medicine three-dimensional images using Anscombe transformation

In general, Nuclear Medicine images present poor signal to noise ratio mostly due to low counts and Poisson noise. The success of segmentation may be improved if relevant information of the input image is enhanced. Our aim was to verify segmentation improvement due to adequate filters. Basically, all images were submitted to Anscombe Transform, filtered by using Lee filter and inverse Anscombe Transform. Then, they were segmented by using Fuzzy Connected method and finally, compared to expected results. This study simulated real images obtained in Nuclear Medicine, such as the volume of the left ventricle of the heart (128×128×128 voxels), then Poisson quantic noise was incorporated in the simulation. For the assessment we used 30 three-dimensional images with different dimensions (volume, wall thickness) and different instances of Poisson noise, divided in three groups: Group I — 10 three-dimensional images of adult males, Group II — 10 three-dimensional images of adult females and Group III — 10 three-dimensional images of children. The analysis used the following parameters: True Positive (TP) rate and False Positive (FP) rate, and Maximum Distance (MaxDist) to the expected contour. Our results show consolidated results obtained from the validation of the image processing, by highlighting the usefulness of Anscombe/Lee filter, and showing the superiority of the segmentation values due to the proposed process.
COMPARISONS: PROFESSIONALLY-DIRECTED AND SELF-DIRECTED INTERNET GROUPS FOR WOMEN WITH BREAST CANCER

Psychological interventions for women coping with breast cancer (BC) have embraced the idea that the open expression of negative emotions is beneficial. This study asks if there is a difference in the amount of negative emotions expressed by members of professional-led compared to self-directed on-line support groups. To answer this question we examined the support group messages written by participants of 11 Internet delivered BC support groups. Seven were facilitated by professionals and four were self-directed. We used two text analysis software programs, LWIC and PCAD, to categorize the interaction. Groups conducted by professionals expressed significantly more negative emotions, anxiety, hostility, and depression and fewer positive emotions than self-directed groups. These differences suggest what leaders encourage, reinforce, model, and may influence members’ behaviors. Professionals encourage the further expression of such emotions, whereas lay leaders more often respond with support and reassurance to members who express painful emotions.
Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks

Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream. In particular, activity in early, intermediate, and late layers is closely related to activity in V1, V4, and the inferotemporal cortex (IT). This study further compares activity in late layers of object-classification CNNs to activity patterns reported in the IT electrophysiology literature. There are a number of close similarities, including the distributions of population response sparseness across stimuli, and the distribution of size tuning bandwidth. Statisics of scale invariance, responses to clutter and occlusion, and orientation tuning are less similar. Statistics of object selectivity are quite different. These results agree with recent studies that highlight strong parallels between object-categorization CNNs and the ventral stream, and also highlight differences that could perhaps be reduced in future CNNs.
On Causal Discovery from Time Series Data using FCI

We adapt the Fast Causal Inference (FCI) algorithm of Spirtes et al. (2000) to the problem of inferring causal relationships from time series data and evaluate our adaptation and the original FCI algorithm, comparing them to other methods including Granger causality. One advantage of FCI based approaches is the possibility of taking latent confounding variables into account, as opposed to methods based on Granger causality. From simulations we see, however, that while the FCI based approaches are in principle quite powerful for finding causal relationships in time series data, such methods are not very reliable for most practical sample sizes. We further apply the framework to microeconomic data on the dynamics of firm growth. By releasing the full computer code for the method we hope to facilitate the application of the procedure to other domains.
Latent semantic analysis of facial action codes for automatic facial expression recognition

For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. One possibility is to label facial expressions into emotion categories. Another approach is to label facial expressions independently from any interpretation attempts. This can be achieved via the facial action coding system (FACS). In this paper we present a novel approach that allows to automatically cluster FACScodes into meaningful categories. Our approach exploits the fact that FACScodes can be seen as documents containing terms -the action units (AUs) present in the codes-and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. The FACScode derived descriptions are computed by Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA). We show that, as a high-level description of facial actions, the newly derived codes constitute a competitive alternative to both basic emotion and FACScodes. We have used them to train different types of artificial neural networks
High Access and Low Use of Technologies in High School Classrooms: Explaining an Apparent Paradox

Most policy makers, corporate executives, practitioners, and parents assume that wiring schools, buying hardware and software, and distributing the equipment throughout will lead to abundant classroom use by teachers and students and improved teaching and learning. This article examines these assumptions in two high schools located in the heart of technological progress, Northern California’s Silicon Valley. Our qualitative methodology included interviews with teachers, students, and administrators, classroom observations, review of school documents, and surveys of both teachers and students in the two high schools. We found that access to equipment and software seldom led to widespread teacher and student use. Most teachers were occasional users or nonusers. When they used computers for classroom work, more often than not their use sustained rather than altered existing patterns of teaching practice. We offer two interrelated explanations for these challenges to the dominant assumptions that guide present technological policy making.
Autonomic and Behavioral Reactivity to an Acute Laboratory Stressor

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Logging impacts on avian species richness and composition differ across latitudes and foraging and breeding habitat preferences

Understanding the causes underlying changes in species diversity is a fundamental pursuit of ecology. Animal species richness and composition often change with decreased forest structural complexity associated with logging. Yet differences in latitude and forest type may strongly influence how species diversity responds to logging. We performed a meta‐analysis of logging effects on local species richness and composition of birds across the world and assessed responses by different guilds (nesting strata, foraging strata, diet, and body size). This approach allowed identification of species attributes that might underlie responses to this anthropogenic disturbance. We only examined studies that allowed forests to regrow naturally following logging, and accounted for logging intensity, spatial extent, successional regrowth after logging, and the change in species composition expected due to random assembly from regional species pools. Selective logging in the tropics and clearcut logging in temperate latitudes caused loss of species from nearly all forest strata (ground to canopy), leading to substantial declines in species richness (up to 27% of species). Few species were lost or gained following any intensity of logging in lower‐latitude temperate forests, but the relative abundances of these species changed substantially. Selective logging at higher‐temperate latitudes generally replaced late‐successional specialists with early‐successional specialists, leading to no net changes in species richness but large changes in species composition. Removing less basal area during logging mitigated the loss of avian species from all forests and, in some cases, increased diversity in temperate forests. This meta‐analysis provides insights into the important role of habitat specialization in determining differential responses of animal communities to logging across tropical and temperate latitudes.
Gray Oral Reading Tests: Some Reliability and Validity Data with Learning-Disabled Children

For the Gray Oral Reading Test grade-level data on 186 disabled (reading-retarded) children showed reasonable long-term stability. WISC-R scores did not predict their reading scores. Correlations between the Gray and Gates-MacGinitie Reading Test grade-levels were moderate, though Gray scores were significantly lower than Gates scores.
Robust local subspace learning by linear fuzzy clustering with Alternative c-Means criterion

Alternative c-Means model is a method for robustifying cluster estimation, in which a modified distance measure instead of the conventional Euclidean distance is used based on the robust M-estimation concept. In this paper, Fuzzy c-Varieties (FCV), which learns local subspace (i.e., FCV achieves local principal component analysis), is extended to a robustified version by using Alternative c-Means criterion. In order to replace the least square measure with alternative c-Means criterion, the clustering criteria of distances between data samples and linear prototypes are calculated by the lower rank approximation concept. Because the proposed method can extract local principal components in a robust way based on an iterative optimization scheme with additional typicality weights in a pseudo-M-estimation procedure, robust subspace learning can be performed in local area and achieves the lower dimensional visualization by using local principal components. In numerical experiments, the robust feature of the proposed model and the local lower visualization using Iris data set are demonstrated.
Critical action learning, policy learning and small firms: An inquiry

This article presents a conceptual and empirical synthesis of ‘critical action learning’ (CAL) and ‘policy learning’ (PL). We pursue this undertaking by reflecting upon a five-year inquiry into an initiative that aimed to provide business support to an action learning set comprising eight African-Caribbean entrepreneurs. CAL and PL share commitments that are riven with tensions, which we explore in our inquiry in relation to three themes that arose during the course of the investigation. The findings demonstrate how a synthesis of CAL and PL can: enrich CAL by recognizing the centrality of emotional and power relations; provide a vehicle to examine the tensions and dynamics that attend policy implementation; illustrate the merits of an experiential approach to evaluation. We contribute to debates on criticality in action learning and the need for more grounded approaches to the evaluation of initiatives directed at small firms.
WeCAPP - The Wendelstein Calar Alto Pixellensing Project I Tracing Dark and Bright Matter in M31

We present WeCAPP, a long term monitoring project searching for microlensing events towards M 31. Since 1997 the bulge of M 31 was monitored in two different wavebands with the Wendelstein 0.8 m telescope. In 1999 we extended our observations to the Calar Alto 1.23 m telescope. Observing simultaneously at these two sites we obtained a time coverage of 53 % during the observability of M 31. To check thousands of frames for variability of unresolved sources, we used the optimal image subtraction method (OIS) by Alard&Lupton (1998) This enabled us to minimize the residuals in the difference image analysis (DIA) and to detect variable sources with amplitudes at the photon noise level. Thus we can detect microlensing events with corresponding amplifications A>10 of red clump giants with M_I = 0.
Geriatric Depression Scale

Abstract The Long Form and the Short Form of the Geriatric Depression Scale (GDS) were compared in a VA nursing home population. The study had two phases. In the first phase, 86 geriatric male veterans were administered the Long Form of the GDS at intake. The Long Form was rescored on the Short Form and a scatterplot was constructed. The Short Form of the GDS consistently identified 94% of the participants using the Long Form as the standard. In the second phase of the study, 31 veterans were administered both the Long Form and the Short Form of the GDS in alternating order during their intake or annual screening assessment. A scatterplot showed the Short Form to consistently identify 100% of the participants using the Long Form as the standard.
Wireless Sensor Network Localization in Harsh Environments Using SDP Relaxation

The accuracy of localization in wireless sensor networks depends on noise level and the presence of nonline of sight (NLOS) connections. In this letter, we propose a novel semidefinite programming (SDP) method to improve the accuracy and to reduce the required time for wireless sensor network localization in harsh environments. In fact, we perturb Edge-based SDP (ESDP) relaxation and add some constraints to the optimization problem in order to make the proposed localization method robust against large amounts of error in distance measurements. Simulation results confirm that our proposed method outperforms others when the majority of connections are NLOS, noise level is high and it is not possible to distinguish between NLOS and line of sight (LOS) connections.