Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in the Brain Waves of 3D and 2.5D Motion Picture Viewers

We measured brain waves of viewers watching the 2D, 2.5D, and 3D motion pictures, comparing them with one another. The relative intensity of {\alpha}-frequency band of 2.5D-viewer was lower than that of 2D-viewer, while that of 3D-viewer remained with similar intensity. This result implies visual neuro-processing of the 2.5D-viewer differs from that of the 3D-viewer.
Empowering network operating systems with memory management techniques

Similarly to computer operating systems which guarantee safe access to memory resources, Network Operating Systems shall grant SDN applications a reliable access to neatly organized flow table resources. This paper presents the architecture for a controller-agnostic Memory Management System and some of its functionalities that aim at improving flow table usage and preventing network misconfigurations. From the implementation perspective, this work discusses the applicability of the proposed system, a strategy to evaluate it and current open challenges.
MethHC: a database of DNA methylation and gene expression in human cancer

We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories. These data can help researchers to identify epigenetic patterns that are important for carcinogenesis. MethHC integrates data such as DNA methylation, mRNA expression, DNA methylation of microRNA gene and microRNA expression to identify correlations between DNA methylation and mRNA/microRNA expression from TCGA (The Cancer Genome Atlas), which includes 18 human cancers in more than 6000 samples, 6548 microarrays and 12 567 RNA sequencing data.
Survival Analysis on Information Technology Adoption of Chinese Enterprises

This paper takes three typical information systems (financial system, office automatic system, and enterprise resource planning system) as examples to study information technology (IT) adoption of Chinese enterprises by means of survival analysis. The dimension of time and dynamic characteristic of IT adoption are highly emphasized in this paper. The method of Kaplan-Meier analysis is used to compare the difference of survival time of IT adoption for heterogeneous enterprises, and the resulting survival functions reflect the adoption proportion at a particular time. Then Cox regression model is used to examine the impact of enterprise characteristics on IT adoption. The results demonstrate that their impact varies with the target technology. Further, this study indicates the direction and significance of the impact of enterprise characteristics on the three technologies respectively.
Paying to save: Reducing cost of colocation data center via rewards

Power-hungry data centers face an urgent pressure on reducing the energy cost. The existing efforts, despite being numerous, have primarily centered around owner-operated data centers (e.g., Google), leaving another critical data center segment - colocation data center (e.g., Equinix) which rents out physical space to multiple tenants for housing their own servers - much less explored. Colocations have a major barrier to achieve cost efficiency: server power management by individual tenants is uncoordinated. This paper proposes RECO (REward for COst reduction), which shifts tenants' power management from uncoordinated to coordinated, using financial reward as a lever. RECO pays (voluntarily participating) tenants for energy reduction such that the colocation operator's overall cost is minimized. RECO incorporates the time-varying operation environment (e.g., cooling efficiency, intermittent renewables), addresses the peak power demand charge, and also proactively learns tenants' unknown responses to the offered reward. RECO includes a new feedback-based online algorithm to optimize the reward without far future offline information. We evaluate RECO using both scaled-down prototype experiments and simulations. Our results show that RECO is "win-win" and can successfully reduce the colocation operator's overall cost, by up to 27% compared to the no-incentive baseline case. Further, tenants receive financial rewards (up to 15% of their colocation costs) for "free" without violating Service Level Agreements.
Pilot, Pivot and Advisory Boards: The Role of Governance Configurations in Innovation Commitment

This study examines how governance configurations comprised of board capital, CEO power and the presence of large shareholders are associated with innovation commitment in organizations. We take a configurational perspective, proposing that organizational innovation commitment is contingent upon how interdependent governance attributes associated with monitoring and resource provisioning can either enhance or constrain management’s discretion to invest in research and development (R&D). Using fuzzy-set qualitative comparative analysis (fsQCA), we identify complementarities which lead to three board archetypes that foster firm innovation commitment. ‘Pilot boards’ have both board capital breadth and depth allowing for active and close participation in innovation decision-making. ‘Pivot boards’ possess the depth of industry-specific expertise and linkages required for providing resources and oversight of powerful CEOs. And ‘advisory boards’ have less power but have outside directors who have breadth of expertise and relational capital that complements the oversight provided by powerful family owners so as to effectively advise management on innovation decisions. Our findings underscore that governance mechanisms work in tandem, not in isolation, to explain significant organizational outcomes, specifically those associated with innovation commitment.
Assessment of non-linear features for intrapartal fetal heart rate classification

Fetal heart rate (fHR) is used to evaluate the fetal well-being during the delivery. It provides information of fetal status and allows doctors to detect ongoing hypoxia. The routine intrapartal evaluation is based on description of macroscopic morphological features of the fHR baseline. FHR contains more information than is used so far, therefore in this work we have focused on evaluation of nonlinear features for fHR signal description. Our data set consists of 189 recordings. Signals with umbilical artery pH less then 7.15 were considered pathological. From each record, 20-minute segment directly preceding the delivery, was chosen. Artifacts were removed from the data and all segments were resampled to 4 Hz sampling frequency. Fractal dimension of attractor, fractal dimension of waveform, entropy, and complexity were used as features. The particular methods used to compute the features were: correlation method for estimation of attractor dimension; Higuchi's, variance, and box counting method for estimation of waveform fractal dimension; approximate and sample method for estimation of entropy and also the Lempel Ziv complexity was computed. All features were evaluated using Mann-Whitney U test and those fulfilling the statistical significance with p<0.01 were used for further computations. Ten fold cross-validation classification using decision tree and SVM approach was carried out. Overall sensitivity and specificity of 70%, comparable to inter-observer variability, was acquired.
Enhancing the learning to rank using the virtual feature logistic regression with relevance feedback

Many information retrieval applications have to publish their outputs in the form of ranked lists, in which documents must be sorted in descending order according to their relevance to a given query. Many existing methods perform analysis on multidimensional features distilled from query-document pairs directly and don't take user's interactive feedback into account; hence, they incur a high computation overhead and a low retrieval performance due to inaccurate query expression. In this paper, we propose a Virtual Feature Logistic Regression (VFLR) method that conducts the logistic regression on a set of crucial but independent variables, called virtual features (VF), which are extracted by the principal component analysis (PCA) with the user's relevance feedback. We then predict the ranking score of each queried document to produce a ranked list. We systematically evaluate our method using the MQ2008 dataset. The experimental results validate that the VFLR method outperforms the state-of-the-art methods in terms of the Mean Average Precision (MAP), the Precision at position k (P@k), and the Normalized Discounted Cumulative Gain at position k (NDCG@k).
A Meeting Browser that Learns

We present a system for extracting useful information from multi-party meetings and presenting the results to users via a browser. Users can view automatically extracted discussion topics and action items, initially seeing high-level descriptions, but with the ability to click through to meeting audio and video. Users can also add value by defining and searching for new topics and editing, correcting, deleting, or confirming action items. These feedback actions are used as implicit supervision by the understanding agents, retraining classifier models for improved or user-tailored performance.
Subtasks of Unconstrained Face Recognition

Unconstrained face recognition remains a challenging computer vision problem despite recent exceptionally high results (∼ 95% accuracy) on the current gold standard evaluation dataset: Labeled Faces in the Wild (LFW) (Huang et al., 2008; Chen et al., 2013). We offer a decomposition of the unconstrained problem into subtasks based on the idea that invariance to identity-preserving transformations is the crux of recognition. Each of the subtasks in the Subtasks of Unconstrained Face Recognition (SUFR) challenge consists of a same-different face-matching problem on a set of 400 individual synthetic faces rendered so as to isolate a specific transformation or set of transformations. We characterized the performance of 9 different models (8 previously published) on each of the subtasks. One notable finding was that the HMAX-C2 feature was not nearly as clutter-resistant as had been suggested by previous publications (Leibo et al., 2010; Pinto et al., 2011). Next we considered LFW and argued that it is too easy of a task to continue to be regarded as a measure of progress on unconstrained face recognition. In particular, strong performance on LFW requires almost no invariance, yet it cannot be considered a fair approximation of the outcome of a detection→alignment pipeline since it does not contain the kinds of variability that realistic alignment systems produce when working on non-frontal faces. We offer a new, more difficult, natural image dataset: SUFR-in-the-Wild (SUFR-W), which we created using a protocol that was similar to LFW, but with a few differences designed to produce more need for transformation invariance. We present baseline results for eight different face recognition systems on the new dataset and argue that it is time to retire LFW and move on to more difficult evaluations for unconstrained face recognition.
Awareness Without Re-Exploitation: Empowering Approaches to Sharing the Message About Human Trafficking

ABSTRACT Human trafficking is a very real and devastating form of abuse and exploitation that affects women, men, and children from all walks of life. Within the last two decades, media personnel, multidisciplinary professionals, and concerned citizens alike have taken note of this social-justice issue. Due to such increased interest, an unprecedented number of people are engaged in awareness efforts that claim to combat human trafficking. While most involved in such efforts are well intentioned, few are intentional. Acts of awareness undertaken by allies are often harmful to the very victims and survivors they are declaring need to be “rescued. ” In the name of anti-trafficking awareness, survivors are frequently re-exploited. Rather than being recognized or compensated as experts and leaders in the anti-trafficking movement, survivors are taken advantage of in the pursuit of a story that has emotional pull. Thus, in response to the increasing rates of re-exploitation of survivors who are asked to share their stories with little thought to the context or effects of doing so, this article provides guidance on how to share the message of human trafficking in a manner that is empowering to survivors. Recommendations uniquely directed toward survivors, social service agencies, and media personnel are proposed.
Kinetic Data Structures for the Semi-Yao Graph and All Nearest Neighbors in R^d

This paper presents a simple kinetic data structure for maintaining all the nearest neighbors of a set of $n$ moving points in $\mathbb{R}^d$, where the trajectory of each point is an algebraic function of at most constant degree $s$. The approach is based on maintaining the edges of the Semi-Yao graph, a sparse graph whose edge set includes the pairs of nearest neighbors as a subset. Our kinetic data structure (KDS) for maintaining all the nearest neighbors is deterministic. It processes $O(n^2\beta_{2s+2}^2(n)\log n)$ events with a total cost of $O(n^2\beta_{2s+2}(n)\log^{d+1} n)$. Here, $\beta_s(n)$ is an extremely slow-growing function. The best previous KDS for all the nearest neighbors in $ \mathbb{R}^d$ is by Agarwal, Kaplan, and Sharir (TALG 2008). It is a randomized result. Our structure and analysis are simpler than theirs. Also, we improve their result by a factor of $\log^d n$ in the number of events and by a $\log n$ factor in the total cost. This paper generalizes and improves the 2013 work of Rahmati, King and Whitesides (SoCG 2013) on maintaining the Semi-Yao graph in $\mathbb{R}^2$; its new technique provides the first KDS for the Semi-Yao graph in $\mathbb{R}^d$. Our KDS is local in the worst case, meaning that only a constant number of events is associated with any one point at any time. For maintaining all the nearest neighbors, neither our KDS nor the KDS by Agarwal~\etal~is local, and furthermore, each event in our KDS and in their KDS is handled in polylogarithmic time in an amortized sense. Finally, in this paper, we also give a KDS for maintenance of all the $(1+\epsilon)$-nearest neighbors which is local and each event can be handled in a polylogarithmic worst-case time.
Spectral Sculpting for OFDM based Cognitive Radio

In this article we describe the software-controllable spectral capabilities available in OFDM based UWB signals. Here we intend to show how MB-OFDM signal might adapt local regulations, remedy future interference situations prepare UWB for the opportunities of cognitive radio. Spectrum sculpting for interference control is more easier and more effective with OFDM based UWB used in cognitive radio than direct sequence Ultra wide band (DS-UWB). Simulation is performed by zeroing 6 and 8 tones out of 128 which results in the notch depth > 45dB and >70dB.
Effects of clozapine on ethanol withdrawal syndrome in rats.

AIMS Co-morbid substance use in schizophrenic patients is common, and an important factor affects the outcome of disease. On the other hand, drug dependence is a predictive factor for psychosis. Alcohol is one of the most frequently abused psychoactive substances and may contribute psychotic symptoms in several conditions, such as withdrawal syndrome. The present study was designed to investigate the effects of clozapine on ethanol withdrawal syndrome (EWS) in rats. METHODS Adult male Wistar rats were used in the study. Ethanol (7.2%, v/v) was given to rats by a liquid diet for 14 days. An isocaloric liquid diet without containing ethanol was also given to control rats. Clozapine (2.5, 5 and 10 mg/kg) and its vehicle (0.1% acetic acid) were injected to rats subcutaneously at the 1.5th and 5.5th hours of ethanol withdrawal. At 2nd, 4th and 6th hours of ethanol withdrawal, rats were observed for 5 min and withdrawal signs that included locomotor hyperactivity, agitation, tremor, tail stiffness, stereotyped behaviour and wet dog shakes were recorded or rated. Following the observations at 6th hour, subjects were tested for audiogenic seizures. RESULTS Clozapine significantly and dose-dependently inhibited the EWS-induced locomotor hyperactivity, wet dog shake, stereotyped behaviour, tremor and tail stiffness. However, it did not produce any significant effect on agitation and audiogenic seizures. Doses of clozapine used in the present study did not produce any significant change on locomotor activities of naïve rats. CONCLUSIONS Our results suggest that clozapine had some significant beneficial effects on EWS in rats. Thus, this drug may be helpful for controlling some withdrawal signs in ethanol-dependent patients.
Image-Based Fitting Diffuse and Specular Reflectance of Object

Traditional image texture mapping can increase abundant details for virtual object. As virtual lighting condition varies, textures lack of realistic representation with dynamically varying with lighting, due to pictures are captured in especial lighting condition. In computer graphics, it often needs to produce continuous and different illumination animation effect in a fixed viewpoint scene. In order to speed up the rendering, the paper proposes a projections-based method, which can interpolate the scene by using several images. This method uses two quadratic multinomials to respectively fit the diffuse reflection component and the specular reflection component for each pixel in different light sources, coefficients of diffuse reflection multinomial and specular reflection multinomial of each pixel can be figure out by least square method. The coefficients are stored for each pixel Illumination effects for arbitrary light-source position may be interpolated efficiently.
Ego depletion improves insight

ABSTRACT Initial acts of self-control can reduce effort and performance on subsequent tasks – a phenomenon known as ego depletion. Ego depletion is thought to undermine the capacity or willingness to engage executive control, an important determinant of success for many tasks. We examined whether ego depletion improves performance on a task that favours less executive control: insight problem solving. In two experiments, participants completed an ego-depletion manipulation or a non-depleting control condition followed by an insight problem-solving task (i.e., matchstick arithmetic). Participants in the depleting condition demonstrated greater insight problem-solving accuracy than those in the non-depleting control condition. Priming theories of willpower did not impact these results. Although ego depletion is widely regarded as a “state of impairment”, attendant decreases in executive control may foster insightful thinking.
Spatially Cost-Sensitive Active Learning

In active learning, one attempts to maximize classifier performance for a given number of labeled training points by allowing the active learning algorithm to choose which points should be labeled. Typically, when the active learner requests labels for the selected points, it assumes that all points require the same amount of effort to label and that the cost of labeling a point is independent of other selected points. In spatially distributed data such as hyperspectral imagery for land-cover classification, the act of labeling a point (i.e., determining the land-type) may involve physically traveling to a location and determining ground truth. In this case, both assumptions about label acquisition costs made by traditional active learning are broken, since costs will depend on physical locations and accessibility of all the visited points. This paper formulates and analyzes the novel problem of performing active learning on spatial data where label acquisition costs are proportional to distance traveled.
Unsupervised Morphological Segmentation with Recursive Neural Network

Motivated by (Socher et al., 2010; 2011)’s work in syntactic parsing of natural language sentences, where the input is a sequence of words, our goal is to learn similar hierarchical parse trees but for words instead, treating each character as a unit. By recursively grouping characters together, we aim to achieve unsupervised learning of not only the shallow morphological segmentation, i.e. breaking words into morphemes, but also the deep structure of word formations.
Solving the Incremental Satissability Problem

Given a set of clauses in propositional logic that have been found satissable, we wish to check whether satissability is preserved when the clause set is incremented with a new clause. We describe an eecient implementation of the Davis-Putnam-Loveland algorithm for checking satissability of the original set. We then show how to modify the algorithm for eecient solution of the incremental problem, which is NP-complete. We also report computational results. Suppose that a given proposition does not follow from a knowledge base encoded in propositional logic. This can be checked by adding the propo-sition's denial to the knowledge base and verifying that the resulting set of propositions is satissable. We wish to determine whether the proposition follows when the knowledge base is augmented by one or more new propositions. Rather than resolve the satissability problem from scratch, we propose to use information gained while solving the original problem, so as to speed the solution of the new problem. We therefore address the incremental satissability problem of proposi-tional logic: given that a set S of propositional clauses is satissable, check whether S fCg is satissable for a given clause C. Our investigation was occasioned by eeorts to solve logic circuit veriication problems 6], but it has application whenever one wishes to check again for logical inferences after enlarging a propositional knowledge base. It also plays a critical role in the solution of inference problems in rst-order predicate logic 4, 7].
Imaging the Disk around the Luminous Young Star LkHα 101 with Infrared Interferometry

The Herbig Ae/Be star LkHα 101 has been imaged at high angular resolution at a number of wavelengths in the near-infrared (from 1 to ~3 μm), using the Keck I Telescope, and also observed in the mid-infrared (11.15 μm), using the UC Berkeley Infrared Spatial Interferometer (ISI). The resolved circular disk with a central hole or cavity reported by Tuthill, Monnier, & Danchi is confirmed. This is consistent with an almost face-on view (inclination of ≲35°) onto a luminous pre-main-sequence or early main-sequence object surrounded by a massive circumstellar disk. With a multiple-epoch study spanning almost 4 years, relative motion of the binary companion has been detected, together with evidence of changes in the brightness distribution of the central disk/star. The resolution of the LkHα 101 disk by ISI mid-infrared interferometry constitutes the first such measurement of a young stellar object in this wavelength region. The angular size was found to increase only slowly from 1.6 to 11.15 μm, inconsistent with standard power-law temperature profiles usually encountered in the literature, supporting instead models with a hot inner cavity and relatively rapid transition to a cool or tenuous outer disk. The radius of the dust-free inner cavity is consistent with a model of sublimation of dust in equilibrium with the stellar radiation field. Measurements from interferometry have been combined with published photometry, enabling an investigation of the energetics and fundamental properties of this prototypical system.