Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanical properties of crepe paper and chickpaper Mechanické vlastnosti krepového a “chickpaper“ papierov

The paper deals with the evaluation of the mechanical properties of the crepe paper and chickpaper. The thickness of crepe paper was 300 m with the surface mass 150 g*m and chickpaper paper thickness was 100 m with the surface mass 40 g*m. Crepe paper and chickpaper are usually used for chicken breeding. Longitudinal and transversal tensile properties were studied. The tensile behavior was monitored on the motorized test stand ANDILOG STENTOR 1000 (Andilog Technologies, Vitrolles, France). There were measured the tensile properties as modulus of elasticity, maximal elongation, maximal tensile force, tensile strengths, tensile index and strain at break of the longitudinal and transversal samples by testing paper strips. Mean values of the maximal elongation b of longitudinal chickpaper samples were three times smaller than maximal elongation of transversal samples. Mean values of maximal tensile force Ft, tensile strength  b tensile indexand maximal strain at break T of longitudinal chickpaper samples were two times smaller than maximal quantities of transversal samples. Mean values of the tensile modulus of elasticity of longitudinal and transversal chickpaper samples were almost equal. Chickpaper mechanical properties in tension were different in the longitudinal and transversal direction of the original paper surface. Mean values of the maximal elongation b and maximal strain at break T of longitudinal crepe paper samples were sixty times smaller than maximal elongation and maximal strain at break of transversal samples. Mean values of maximal tensile force Ft, tensile strength andtensile index w of longitudinal crepe paper samples were fourth times smaller than maximal quantities of transversal samples. Mean values of the tensile modulus of elasticity of longitudinal samples of crepe paper were thirty eight times smaller than values of transversal samples.
The effects of loss and latency on user performance in unreal tournament 2003®

The growth in the popularity of interactive network games has increased the importance of a better understanding of the effects of packet loss and latency on user performance. While previous work on network games has studied user tolerance for high latencies and has studied the effects of latency on user performance in real-time strategy games, to the best of our knowledge, there has been no systematic study of the effects of loss and latency on user performance. In this paper we study user performance for Unreal Tournament 2003 (UT2003), a popular first person shooter game, under varying amounts of packet loss and latency. First, we deduced typical real world values of packet loss and latency experienced on the Internet by monitoring numerous operational UT2003 game servers. We then used these deduced values of loss and latency in a controlled networked environment that emulated various conditions of loss and latency, allowing us to monitor UT2003 at the network, application and user levels. We designed maps that isolated the fundamental first person shooter interaction components of movement and shooting, and conducted numerous user studies under controlled network conditions. We find that typical ranges of packet loss have no impact on user performance or on the quality of game play. The levels of latency typical for most UT2003 Internet servers, while sometimes unpleasant, do not significantly affect the outcome of the game. Since most first person shooter games typically consist of generic player actions similar to those that we tested, we believe that these results have broader implications.
Image enhancement method based on fractional wavelet transform

The characteristic of the subband coefficients of the discrete fractional wavelet transform (DFRWT) vary with the change of the order p should be utilized in this paper. The original image is decomposed by DFRWT in different p order near p=1, and the mode subband coefficients have changed slightly with the comparison of DWT(p=1). In order to make the result of image enhancement selective, the paper presents a new image enhancement technique based on DFRWT and adaptive threshold algorithm. Experimental results show that the proposed methods effectively promote the enhancement effect in details compared to the result of the order p=1, objective evaluation is of Contrast Improvement Index(CII) are obviously improved.
Interoperative Biopsy Site Relocalization in Endoluminal Surgery

Barrett's oesophagus, a premalignant condition of the oesophagus has been on a rise in the recent years. The standard diagnostic protocol for Barrett's involves obtaining biopsies at suspicious regions along the oesophagus. The localization and tracking of these biopsy sites “interoperatively” poses a significant challenge for providing targeted treatments and tracking disease progression. This paper proposes an approach to provide guided navigation and relocalization of the biopsy sites using an electromagnetic tracking system. The characteristic of our approach over existing ones is the integration of an electromagnetic sensor at the flexible endoscope tip, so that the endoscopic camera depth inside the oesophagus can be computed in real time, allowing to retrieve and display an image from a previous exploration at the same depth. We first describe our system setup and methodology for interoperative registration. We then propose three incremental experiments of our approach. First, on synthetic data with realistic noise model to analyze the error bounds of our system. The second on in vivo pig data using an optical tracking system to provide a pseudo ground truth. Accuracy results obtained were consistent with the synthetic experiments despite uncertainty introduced due to breathing motion, and remain inside acceptable error margin according to medical experts. Finally, a third experiment designed using data from pigs to simulate a real task of biopsy site relocalization, and evaluated by ten gastro-intestinal experts. It clearly demonstrated the benefit of our system toward assisted guidance by improving the biopsy site retrieval rate from 47.5% to 94%.
A two-stage learning method for protein-protein interaction prediction

In this paper, a new method for PPI (proteinprotein interaction) prediction is proposed. In PPI prediction, a reliable and sufficient number of training samples is not available, but a large number of unlabeled samples is in hand. In the proposed method, the denoising auto encoders are employed for learning robust features. The obtained robust features are used in order to train a classifier with a better performance. The experimental results demonstrate the capabilities of the proposed method. Protein-protein interaction; Denoising auto encoder;Robust features; Unlabelled data;
Advanced social work practice : an integrative, multilevel approach

Dr. Joan Dworkin has been a Professor in the Division of Social Work, California State University, Sacramento since 1994. She teaches advanced social work practice and social work practice with HIV and AIDS in the graduate program. Her primary specialty areas are mental health, public health, HIV and AIDS, professional education and divorce mediation, and she has experience in provision of clinical services in community mental health and in development of primary prevention programs. "Multilevel Social Work Practice: A Case Study Manual" features over 20 cases, addressing diverse and ethnic clients and groups, so that introductory practitioners can understand the close relationship between their clinical work, which is client-focused, and working for social justice, which most frequently is population-focused. Through the cases, content, and accompanying activities and resources for further research, social workers will develop the ability to fully address the concerns and problems of the people they work with and will be able to act on them simultaneously.
Reactive navigation of an intelligent robotic walking aid

This work presents the intelligent walking aid system Care-O-bot. Care-O-bot is the prototype of a multifunctional home care system, to be used by elderly people in order to live independently in their homes. In order to enable easy manipulation of the robot platform, the way to use it as a walking aid has been adapted to conventional walking aid systems. The robot drives in reaction to input forces given by the user, for example if the user "pushes" the robot forward, it will start moving in the required direction. As an improvement to conventional walking aid systems, intelligent behaviours, as for example autonomous obstacle avoidance and path planning are included.
Making queueing theory more palatable to SDN/OpenFlow-based network practitioners

Software-Defined Networking (SDN) is an emerging networking technology that has attracted intense interest from both the industry and research communities. Thus far, it is primarily applied to datacenters and research network environments. Despite meticulous effort in planning and equipment selection prior to deployment, there remain unknowns that can affect the network's performance after equipment has been deployed and is fully operational. Network administrators and planners would benefit from a tool that is able to monitor the load on various network entities and visualize this in real-time and, even better, predict likely performance changes arising from traffic variation; this allows them to make prompt decisions to prevent seemingly small hotspots from becoming major bottlenecks. In this paper, we present a network visualization and performance prediction tool that enables network planners to examine how their networks' performance will be affected as the traffic loads and network utilization changes. This is a first of its kind where performance prediction is based on queueing analytic models of the network configuration coupled with real-time measurements taken from the network devices.
Resonance as an effective mechanism of dynamical stability in large microcircuits of spiking neurons

Self-sustained activity of the cortical circuitry is a known property in the brain. It is manifested both during spontaneous activity and delay periods [2]. The spontaneous activity is characterized by a very stable low firing regime (1-5 Hz) in which neurons tend to maintain a constant, homeostatic firing level. To account for it, most modeling studies dealing with spontaneous self-sustained activity have assumed an external “background” current [1]. The main argument rendering the above assumption reasonable is that neurons in the cortex seem to spike in an apparent random fashion [8, 9]. However, it has also been shown, that completely deterministic networks of spiking neurons can produce pseudo-random activity patterns, also called “network effect” [3, 5]. Moreover, the assumption of an independent, external, random drive is not only questionable, but it also changes the statistics of neural firing in a way that is not dependent on the states of the network. Thus, we propose, that at least to some degree, a large cortical circuit must be able to maintain its activity independently of any external stimulation. There have been many attempts to build self-sustained networks of spiking neurons. Most studies have been conducted using integrate-and-fire neurons and have failed, since the network either tends to “die out” or to “explode”, firing in saturation. A precise balance of excitation/inhibition or special structured receptive fields [10] are required in order to avoid the above-mentioned effect. The inhibition/excitation balance is however very difficult to maintain since it depends on the dynamical patterns of activity which are continuously changing. We propose that resonating neurons can overcome the instability effects in such networks and effectively contribute to the maintenance of ongoing self-sustained dynamics.
Topics in SCHISMA dialogues

An important part of dialogue management in a dialogue system is topic management. The system has to keep track of topic aspects (current topic, context, obligations, .) in order to be able to resolve ambiguities and to respond in a cooperative and sensible way. In the Schisma project, we are developing a system that takes part in a conversation about theatre performances and which is able to make reservations for these performances. In this paper we discuss several techniques for topic management that we developed and tested .
A probabilistic approach to coordinated multi-robot indoor surveillance

In this paper we discuss the problem of monitoring and searching an indoor environment for an intruder with a group of mobile robots. We present a graph-based algorithm to coordinate a group of robots which takes the limitations and uncertainties of sensors into account and is able to find good coordination plans efficiently even for large environments. We analyze and compare the approach against other coordination strategies based on a new probabilistic framework that allows to evaluate the performance of any coordination strategy based on a probabilistic sensor model and a worst case behavior model for intruders. Using this framework we demonstrate the capabilities of the planning algorithm in several simulation experiments.
Effects of Digitally Enhanced Learning Tasks on Cognitive Functioning

Introduction In recent years there has been an increase in commercial cognitive training programs that claim that their program improves memory, attention, processing speed or problem-solving skills. These programs are based on the assumption that improving working memory can lead to gains in related areas of cognition. This implication that working memory can improve other areas of cognition stems from the finding that working memory is a central component of general cognition. The increased access via portable electronic devices allows these digitally enhanced learning tasks to reach a greater amount of people. However, the literature does not have a clear answer as to whether these digitally enhanced learning tasks that train working memory actually improve other areas of cognitive functioning. Working memory (WM) is defined as a storage system that is responsible for retaining small amounts of information over brief intervals of time. A cognitive function that is related to working memory is fluid intelligence, a persons ability to reason with novel information. A persons ability to reason with novel information can be largely attributed to WM capacity and vice versa. Another cognitive function that is related to working memory is attention control. People that have a high WM capacity are less likely to have their attention drawn from a distraction than their low WM counterparts. In 2014, Au et al., conducted a meta-meta-analysis across 20 different studies of working memory literature and found a small but statistically significant net effect of n-back training on fluid intelligence outcome measures. However, this finding's generalizability is restricted due to the limited age range of the population. However, in 2012, Shipstead, Redick, and Engle conducted a literature review in which they concluded that the literature did not provide sufficient evidence of the efficacy of WM training impacting other areas of cognition. Therefore, the current body of literature regarding the relationship between electronically enhanced learning tasks that train working memory and its impact on other areas of cognition is inconsistent at best. Hypothesis If an individual is exposed to a digitally enhanced learning task, then they will show an increase in cognitive functioning compared to an individual who is exposed to other learning task forms. Independent variables Digitally enhanced learning tasks: Digitally enhanced learning tasks, also, known as cognitive training programs has begun to proliferate a variety of programs that state that their program will lead to transfer or gains in cognitive function. A …
A 0.9 V 96 $\mu$W Fully Operational Digital Hearing Aid Chip

A 0.9 V 96 muW fully operational low-power digital hearing aid chip is proposed and implemented. An internal status controller is introduced to achieve full operation of the adaptive-SNR analog front end. Dedicated DSP with an additional volume control parameter eliminates any internal overflow and enables the hearing aid to be customized for each individual user. When the input audio band is split into a low band and a high band, the audio signal can be processed coarsely. In addition, fine processing of the high-band signal can be obtained with a low-power automatic gain control (AGC) comprising a digital comparator and a subtraction unit. A heterogeneous Sigma-Delta DAC reduces the power consumption of the interpolation filter without degrading performance by allowing different frequencies between the input signal and the sampling clock of the Sigma-Delta modulator. Compared with a conventional Sigma-Delta DAC, the heterogeneous Sigma-Delta DAC reduces the power dissipation by 40.4% and the area occupation by 40.5%, and it has a reported error rate of only 0.16%. The fabricated chip achieves a 79 dB peak SNR with 4.1 muVrms of input-referred noise voltage. The core area is 2.8 mm x 1.1 mm in a 0.18 mum standard CMOS process.
Using a Heterogeneous Linguistic Network for Word Sense Induction and Disambiguation

Linguistic Networks are structures that allow us to model the characteristics of human language through a graph-like schema. This kind of modelization has proven to be useful while dealing with natural language processing tasks. In this paper, we first present and discuss the state of the art of recent semantic relatedness methods from a network-centric point of view. That is, we are interested in the types of networks used to solve practical semantic tasks. In order to address some of the short-comings in the studied approaches, we propose a hybrid linguistic structure that takes into account lexical and syntactical language information. We show our model’s practicality with a proof of concept: we set to solve word sense disambiguation and induction while using the presented network schema. Our modelization aims to shed light into ways of combining distinct types of linguistic information in order to take advantage of each of its components’ unique characteristics.
Regional Time Stepping for SPH

This paper presents novel and efficient strategies to spatially adapt the amount of computational effort applied based on the local dynamics of a free surface flow, for classic weakly compressible SPH (WCSPH). Using a convenient and readily parallelizable block-based approach, different regions of the fluid are assigned differing time steps and solved at different rates to minimize computational cost. We demonstrate that our approach can achieve about two times speed-up over the standard method even in highly dynamic scenes.
Dramatic Repositioning of c-Myb to Different Promoters during the Cell Cycle Observed by Combining Cell Sorting with Chromatin Immunoprecipitation

The c-Myb transcription factor is a critical regulator of proliferation and stem cell differentiation, and mutated alleles of c-Myb are oncogenic, but little is known about changes in c-Myb activity during the cell cycle. To map the association of c-Myb with specific target genes during the cell cycle, we developed a novel Fix-Sort-ChIP approach, in which asynchronously growing cells were fixed with formaldehyde, stained with Hoechst 33342 and separated into different cell cycle fractions by flow sorting, then processed for chromatin immunoprecipitation (ChIP) assays. We found that c-Myb actively repositions, binding to some genes only in specific cell cycle phases. In addition, the specificity of c-Myb is dramatically different in small subpopulations of cells, for example cells in the G2/M phase of the cell cycle, than in the bulk population. The repositioning of c-Myb during the cell cycle is not due to changes in its expression and also occurs with ectopically expressed, epitope-tagged versions of c-Myb. The repositioning occurs in established cell lines, in primary human CD34+ hematopoietic progenitors and in primary human acute myeloid leukemia cells. The combination of fixation, sorting and ChIP analysis sheds new light on the dynamic nature of gene regulation during the cell cycle and provides a new type of tool for the analysis of gene regulation in small subsets of cells, such as cells in a specific phase of the cell cycle.
Reducing motion blurring associated with temporal summation in low light scenes for image quality enhancement

In order to see under low light conditions nocturnal insects rely on neural strategies based on combinations of spatial and temporal summations. Though these summation techniques when modelled are effective in improving the quality of low light images, using the temporal summation in scenes where image velocity is high only come at a cost of motion blurring in the output scenes. Most recent research has been towards reducing motion blurring in scenes where motion is caused by moving objects rather than effectively reducing motion blurring in scenes where motion is caused by moving cameras. This makes it impossible to implement the night vision algorithm in moving robots or cars that operate under low light conditions. In this paper we present a generic new method that can replace the normal temporal summation in scenes where motion is detected. The proposed method is both suitable for motion caused by moving objects as well as moving cameras. The effectiveness of this new generic method is shown with relevant supporting experiments.
Differential tissue targeting of autoimmunity manifestations by autoantigen-associated Y RNAs.

OBJECTIVE To assess the Y RNAs, a family of homologous RNAs that bind to the Ro autoantigen, for the ability to contribute to autoimmune disease by activating RNA-responsive Toll-like receptors (TLRs). METHODS Using cell lines expressing or stably transfected with TLR-3, TLR-7, or TLR-8, we determined the patterns of RNA-specific TLR activation by in vitro transcripts of all of the known murine and human Y RNAs. Next, 8-10-week-old female mice were exposed to a single 50-microg subcutaneous injection of mouse Y1 or mouse Y3 RNA, and the effects were observed. RESULTS Y RNA family members differed in their TLR reactivities. Both human and mouse Y3 RNAs, but not other human or mouse Y RNAs, prominently induced TLR-3 activation. Although most human and mouse Y RNAs activated TLR-7 efficiently, mouse Y3 RNA and human Y5 RNA did not. Single subcutaneous injections of mice with either mouse Y1 RNA or mouse Y3 RNA induced or inhibited lymphoid infiltrates in different target organs based on the Y RNA and TLR status of the mouse used. Mouse Y1 RNA induced kidney lesions in TLR-3-intact mice but not in TLR-3-knockout mice. In contrast, mouse Y3 RNA treatment was associated with nephritis in TLR-3-knockout mice but not in TLR-3-intact mice. Sialoadenitis developed in untreated TLR-3-/- mice and in TLR-3-/- mice treated with mouse Y3 RNA, but sialoadenitis was not present in TLR--/-) mice treated with mouse Y1 RNA. CONCLUSION Y RNAs can induce innate immune responses and influence clinical manifestations of autoimmunity, suggesting that they are relevant to syndromes of anti-Ro autoimmunity. Distinct patterns of tissue targeting can be seen after exposure to different Y RNAs, in a pattern that correlates with the innate immune signals they induce. Thus, the balance of innate immune signals induced by exposure to endogenous Y RNAs may help determine the nature of the clinical syndrome in anti-Ro autoimmunity.