Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcranial Magnetic Stimulation Studies of Face Processing

Neuropsychological patients exhibiting category-selective visual agnosias have provided unique insights into the cognitive functions of the human brain (Shallice, 1989; Moscovitch et al. 1997; Moro et al. 2008), and this has been especially true in the study of face processing (Bodamer, 1947; Farah, 2004). While cases of pure prosopagnosia resulting from cortical damage are extremely rare they still provide the strongest evidence that faces are processed in anatomically segregated neural networks in the human brain (Sergent and Signoret, 1992; McNeill and Warrington 1993; Rossion et al. 2003; Riddoch et al. 2008). Moreover, acquired prosopagnosics exhibiting selective deficits with specific aspects of face recognition (e.g. recognising facial identity or recognising facial expressions) provided evidence for the seminal cognitive model of face processing (Bruce and Young, 1986) as well as for subsequent cortical models of face processing (Haxby et al. 2000; Adolphs, 2002; Calder and Young, 2005). These models in turn provided cognitive frameworks with which to test how faces are recognised in the undamaged brain.
An analysis of university student academic self-entitlement: Levels of entitlement, academic year, and gender

Academic self-entitlement is a rapidly increasing phenomenon that is becoming a problem in universities today. Self-entitled students expect high grades for minimal effort and tend to be highly demanding; they exhibit strong emotions when outcomes fail to meet their expectations. This type of student behavior increases the burden placed upon unprepared faculty, emphasizes performance goals rather than learning goals, and threatens to place the core values of education at stake. This study investigates whether relationships exist between student gender, year in school, and academic self-entitlement. University students were assessed using the Academic Entitlement Scale (Achacoso, 2002). A two-way MANOVA revealed significant differences by gender, with males more self-entitled than females. There also were significant differences by academic year in school, with students less self-entitled as they progressed from early undergraduate years to graduate school. There were no significant interactions for the effects of academic year in school and gender.
Boundary Finding with Prior Shape and Smoothness Models

We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape, and combined models, respectively. Indeed, snakes, modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available.
Differential Expression Patterns of Pleurotus ostreatus Catalase Genes during Developmental Stages and under Heat Stress

Catalases are ubiquitous hydrogen peroxide-detoxifying enzymes. They participate in fungal growth and development, such as mycelial growth and cellular differentiation, and in protecting fungi from oxidative damage under stressful conditions. To investigate the potential functions of catalases in Pleurotus ostreatus, we obtained two catalase genes from a draft genome sequence of P. ostreatus, and cloned and characterized them (Po-cat1 and Po-cat2). Po-cat1 (group II) and Po-cat2 (group III) encoded putative peptides of 745 and 528 amino acids, respectively. Furthermore, the gene structures were variant between Po-cat1 and Po-cat2. Further research revealed that these two catalase genes have divergent expression patterns during different developmental stages. Po-cat1/Po-cat1 was at a barely detectable level in mycelia, accumulated gradually during reproductive growth, and was maximal in separated spores. But no catalase activity of Po-cat1 was detected by native-PAGE during any part of the developmental stages. In contrast, high Po-cat2/Po-cat2 expression and Po-cat2 activity found in mycelia were gradually lost during reproductive growth, and at a minimal level in separated spores. In addition, these two genes responded differentially under 32 °C and 40 °C heat stresses. Po-cat1 was up-regulated under both temperature conditions, while Po-cat2 was up-regulated at 32 °C but down-regulated at 40 °C. The accumulation of catalase proteins correlated with gene expression. These results indicate that the two divergent catalases in P. ostreatus may play different roles during development and under heat stress.
User Interaction Design Patterns for Information Retrieval Systems

The field of information retrieval systems (IRS), like web search engines, hypertext, database and also search facilities embedded in e.g. web shops and even desktop applications, has some special characteristics regarding the design of user interaction. On the first glance the interaction is very simple: Man asks, machine answers. When doing a conventional requirements analysis and design concept, you would draw two use cases and design two UI masks and you’re done. But at a closer look the design of usable, enjoyable and successful IRS is by far not trivial. Instead it seems that those factors that make up a good interaction design for an IRS are nearly as complex and subtle like human interpersonal communication itself [1]. Fortunately the field of information retrieval (IR) got a lot of attention since the early days of humancomputer interaction research. While more technically oriented scientists and designers engaged to optimize the hidden qualities of IRS, esp. the optimization of recall and precision, others concentrated on enhancing the user-system interaction. Of course there are shades and strong interrelationships between both directions and as a result, today, there are tons of scientific results on the design of IRS and their user interfaces and there are as well lots of successful solutions on desktop computers, information systems and in the WWW. The aim of this pattern language is to collect known good solutions for interaction design of IRS. It is mainly inspired by recent pattern languages on UI design like [2-4].
In Question Answering, Two Heads Are Better Than One

Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques. We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered, and a 32.8% improvement according to the average precision metric.
Detecting Grand Tours of Europe with Geo-Tags

In his recent, light-hearted article “The Grand Tour”, New Yorker journalist Evan Osnos writes of his time with a Chinese tour group in Europe [12]. Osnos describes a group of thirty eight closelychaperoned tourists undertaking a breakneck bus tour, covering five countries in ten days. He argues that Chinese tourists have formed a distinctively Chinese “grand tour” of Europe. This tour includes places like Trier, Germany, the birthplace of Karl Marx, described in a Chinese guide book as “the Mecca of the Chinese people”; it includes some willows on Cambridge University’s campus that were described in a famous Chinese poem as “young brides in the setting sun.” Furthermore, Osnos notes that for nearly all of their meals, the tourists went to Chinese restaurants; the tour guide advised that “in general, one should steer clear of the local food. ”
In an Uncertain World: Distributed Optimization in MIMO Systems with Imperfect Information

In this paper, we introduce a distributed algorithm that optimizes the Gaussian signal covariance matrices of multi-antenna users transmitting to a common multi-antenna receiver under imperfect and possibly delayed channel state information. The algorithm is based on an extension of exponential learning techniques to a semidefinite setting and it requires the same information as distributed water-filling methods. Unlike water-filling however, the proposed matrix exponential learning (MXL) algorithm converges to the system's optimum signal covariance profile under very mild conditions on the channel uncertainty statistics; moreover, the algorithm retains its convergence properties even in the presence of user update asynchronicities, random delays and/or ergodically changing channel conditions. In particular, by properly tuning the algorithm's learning rate (or step size), the algorithm converges within a few iterations, even for large numbers of users and/or antennas per user. Our theoretical analysis is complemented by numerical simulations which illustrate the algorithm's robustness and scalability in realistic network conditions.
Secure database-as-a-service with Cipherbase

Data confidentiality is one of the main concerns for users of public cloud services. The key problem is protecting sensitive data from being accessed by cloud administrators who have root privileges and can remotely inspect the memory and disk contents of the cloud servers. While encryption is the basic mechanism that can leveraged to provide data confidentiality, providing an efficient database-as-a-service that can run on encrypted data raises several interesting challenges. In this demonstration we outline the functionality of Cipherbase --- a full fledged SQL database system that supports the full generality of a database system while providing high data confidentiality. Cipherbase has a novel architecture that tightly integrates custom-designed trusted hardware for performing operations on encrypted data securely such that an administrator cannot get access to any plaintext corresponding to sensitive data.
IPS-2: The Second Generation of a Parallel Program Measurement System

IPS, a performance measurement system for parallel and distributed programs, is currently running on its second implementation. IPS's model of parallel programs uses knowledge about the semantics of a program's structure to provide two important features. First, IPS provides a large amount of performance data about the execution of a parallel program, and this information is organized so that access to it is easy and intuitive. Secondly, IPS provides performance analysis techniques that help to guide the programmer automatically to the location of program bottlenecks. The first implementation of IPS was a testbed for the basic design concepts, providing experience with a hierarchical program and measurement model, interactive program analysis, and automatic guidance techniques. It was built on the Charlotte distributed operating system. The second implementation, IPS-2, extends the basic system with new instrumentation techniques, an interactive and graphical user interface, and new automatic guidance analysis techniques. This implementation runs on 4.3BSD UNIX systems, on the VAX, DECstation, Sun 4, and Sequent Symmetry multiprocessor. >
Recent improvements in an approach to segment-based automatic language identification

In 1993, a segment-based system for Automatic Language Identi cation (ALI) was developed and introduced. The system incorporates phonetic, acoustic, and prosodic information within a probabilistic framework. The original system was trained and tested using the OGI MultiLanguage Telephone Speech Corpus and achieved an accuracy of 57.3% in identifying the language of test utterances from the OGI corpus. Recent improvements to the system have included the addition of channel normalization during preprocessing, the utilization of the recently transcribed utterances from the OGI corpus for phonetic recognition training, the use of mixture Gaussian density functions for the modeling of prosodic information, and the development of a hill-climbing optimization procedure for determining the scaling factors used when combining the scores from di erent models. The current system has achieved an accuracy of 79.7% in identifying the language of test utterances.
Reliability of Serum Metabolite Concentrations over a 4-Month Period Using a Targeted Metabolomic Approach

Metabolomics is a promising tool for discovery of novel biomarkers of chronic disease risk in prospective epidemiologic studies. We investigated the between- and within-person variation of the concentrations of 163 serum metabolites over a period of 4 months to evaluate the metabolite reliability expressed by the intraclass-correlation coefficient (ICC: the ratio of between-person variance and total variance). The analyses were performed with the BIOCRATES AbsoluteIDQ™ targeted metabolomics technology, including acylcarnitines, amino acids, glycerophospholipids, sphingolipids and hexose in 100 healthy individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study who had provided two fasting blood samples 4 months apart. Overall, serum reliability of metabolites over a 4-month period was good. The median ICC of the 163 metabolites was 0.57. The highest ICC was observed for hydroxysphingomyelin C14:1 (ICC = 0.85) and the lowest was found for acylcarnitine C3:1 (ICC = 0). Reliability was high for hexose (ICC = 0.76), sphingolipids (median ICC = 0.66; range: 0.24–0.85), amino acids (median ICC = 0.58; range: 0.41–0.72) and glycerophospholipids (median ICC = 0.58; range: 0.03–0.81). Among acylcarnitines, reliability of short and medium chain saturated compounds was good to excellent (ICC range: 0.50–0.81). Serum reliability was lower for most hydroxyacylcarnitines and monounsaturated acylcarnitines (ICC range: 0.11–0.45 and 0.00–0.63, respectively). For most of the metabolites a single measurement may be sufficient for risk assessment in epidemiologic studies with healthy subjects.
Brahma Is Required for Proper Expression of the Floral Repressor FLC in Arabidopsis

Background BRAHMA (BRM) is a member of a family of ATPases of the SWI/SNF chromatin remodeling complexes from Arabidopsis. BRM has been previously shown to be crucial for vegetative and reproductive development. Methodology/Principal Findings Here we carry out a detailed analysis of the flowering phenotype of brm mutant plants which reveals that, in addition to repressing the flowering promoting genes CONSTANS (CO), FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CO1 (SOC1), BRM also represses expression of the general flowering repressor FLOWERING LOCUS C (FLC). Thus, in brm mutant plants FLC expression is elevated, and FLC chromatin exhibits increased levels of histone H3 lysine 4 tri-methylation and decreased levels of H3 lysine 27 tri-methylation, indicating that BRM imposes a repressive chromatin configuration at the FLC locus. However, brm mutants display a normal vernalization response, indicating that BRM is not involved in vernalization-mediated FLC repression. Analysis of double mutants suggests that BRM is partially redundant with the autonomous pathway. Analysis of genetic interactions between BRM and the histone H2A.Z deposition machinery demonstrates that brm mutations overcome a requirement of H2A.Z for FLC activation suggesting that in the absence of BRM, a constitutively open chromatin conformation renders H2A.Z dispensable. Conclusions/Significance BRM is critical for phase transition in Arabidopsis. Thus, BRM represses expression of the flowering promoting genes CO, FT and SOC1 and of the flowering repressor FLC. Our results indicate that BRM controls expression of FLC by creating a repressive chromatin configuration of the locus.
The Ticket to Work Program: Employment Networks’ Views on Serving Beneficiaries who are Blind or Visually Impaired

This article reports on a survey of the opinions of employment networks (ENs) about serving social security beneficiaries who are blind or visually impaired under the Ticket to Work program. Although most of the 267 ENs who participated in the survey expressed concerns about working with those who are blind or visually impaired, they did not seem to be biased against working with this population based solely on their disability type.
The effects of screen size and message content on attention and arousal Media Psychology

A three-dimensional spinning and throwing toy having a generally geodesic appearance formed of flexible elongated members connected in a manner to provide substantial space therebetween so that friction on the toy is insignificant when it is moved freely through the air. The toy may be adapted for use as (1) a beach bag, by separably connecting certain of the elongated members to provide access to the interior of the toy and (2) a float for playing in water, by making the elongated members inflatable.
Information extraction with neural networks

Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tune for new datasets. In this thesis, we propose the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical considerations, we explore transfer learning to take advantage of large annotated dataset to improve the performance on datasets with limited number of annotations. The ANN-based system is publicly released as an easy-to-use software package for general purpose named-entity recognition as well as de-identification. Finally, we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).
User mobility-aware decision making for mobile computation offloading

The last decade has seen a rapid growth in the use of mobile devices all over the world. With an increasing use of mobile devices, mobile applications are getting more diverse and complex, demanding more computational resources. However, mobile devices are typically resource-limited (i.e., a slower-speed CPU, a smaller memory) due to a variety of reasons. Mobile users will be capable of running applications with heavy computation if they can offload some of their computations to other places, such as desktop or server machines. However, mobile users are typically subject to dynamically changing network environments, particularly, due to user mobility. This makes it hard to make good offloading decisions in mobile environments. In general, user's mobility can provide some hints for upcoming changes to network environments. Motivated by this, we propose a mobility model of each individual user taking advantage of the regularity of his/her mobility pattern, and develop an offloading decision making technique based on the mobility model. We evaluate our technique through trace-based simulation with real log data traces from 14 Android users. Our evaluation result shows that the proposed technique can help mobile devices to boost its performance in terms of response time and energy consumption, when users are highly mobile.
Faster Compact On-Line Lempel-Ziv Factorization

We present a new on-line algorithm for computing the Lempel-Ziv factorization of a string that runs in $O(N\log N)$ time and uses only $O(N\log\sigma)$ bits of working space, where $N$ is the length of the string and $\sigma$ is the size of the alphabet. This is a notable improvement compared to the performance of previous on-line algorithms using the same order of working space but running in either $O(N\log^3N)$ time (Okanohara & Sadakane 2009) or $O(N\log^2N)$ time (Starikovskaya 2012). The key to our new algorithm is in the utilization of an elegant but less popular index structure called Directed Acyclic Word Graphs, or DAWGs (Blumer et al. 1985). We also present an opportunistic variant of our algorithm, which, given the run length encoding of size $m$ of a string of length $N$, computes the Lempel-Ziv factorization on-line, in $O\left(m \cdot \min \left\{\frac{(\log\log m)(\log \log N)}{\log\log\log N}, \sqrt{\frac{\log m}{\log \log m}} \right\}\right)$ time and $O(m\log N)$ bits of space, which is faster and more space efficient when the string is run-length compressible.
Make it reversible: Efficient embedding of non-reversible functions

Reversible computation became established as a promising concept due to its application in various areas like quantum computation, energy-aware circuits, and further areas. Unfortunately, most functions of interest are non-reversible. Therefore, a process called embedding has to be conducted to transform a non-reversible function into a reversible one — a coNP-hard problem. Existing solutions suffer from the resulting exponential complexity and, hence, are limited to rather small functions only. In this work, an approach is presented which tackles the problem in an entirely new fashion. We divide the embedding process into matrix operations, which can be conducted efficiently on a certain kind of decision diagram. Experiments show that improvements of several orders of magnitudes can be achieved using the proposed method. Moreover, for many benchmarks exact results can be obtained for the first time ever.
Deep bite: A treatment planning decision support scheme

Deep bite malocclusion is one of the most common malocclusions that have a multifactorial nature; with a multitude of underlying components, while various dental and facial parameters could affect the decision making process. This study aimed to customize a step-wise systematized diagnostic tool that could aid the clinician in the decision making process for deep bite cases. We used the data provided by 124 deep overbite cases to elucidate the most eminent underlying components of deep bite malocclusion, moreover a literature review was done that clarified the various contributing factors to the decision making of deep bite. Using this data a systematic diagnostic sheet is presented to aid the orthodontist in designing the treatment objectives of deep bite malocclusion.
Combining MEG and EEG Source Modeling in Epilepsy Evaluations

This article reviews the relative strengths and weaknesses of MEG and EEG source modeling for localization of epileptogenic foci. Proper interpretation of these dipole models requires an appreciation for the limitations of each technique and an understanding of the character of the cortical sources that can generate epileptiform transients identifiable in recordings of spontaneous cerebral activity. MEG is sensitive to smaller sources, is not altered by the skull and scalp, requires a simpler head model, and provides more accurate localization, but it is insensitive to radial sources. EEG requires larger sources, is attenuated and smeared by the skull/scalp, requires a more complicated head model, and provides less accurate localization; however, and most importantly, it is sensitive to all source orientations. In conclusion, the case is made that maximal clinical information is obtained when simultaneous MEG and EEG are both subjected to source modeling, either individually or in a combined fashion.