Miracle-dz/UniFAR-FTUs huggingface dataset:
Crypto for Tiny Objects

This work presents the first known implementation of elliptic curve cryptography for sensor networks, motivated by those networks’ need for an efficient, secure mechanism for shared cryptographic keys’ distribution and redistribution among nodes. Through instrumentation of UC Berkeley’s TinyOS, this work demonstrates that secret-key cryptography is already viable on the MICA2 mote. Through analyses of another’s implementation of modular exponentiation and of its own implementation of elliptic curves, this work concludes that public-key infrastructure may also be tractable in 4 kilobytes of primary memory on this 8-bit, 7.3828-MHz device.
The research platform for building medical diagnostic services

This paper describes research and development of a software platform for processing medical diagnostic data. This work is carried out by the IT-park of PetrSU in collaboration with the Faculty of Medicine. The aim of the Platform development is to present universal software tools for research done on the basis of the initial information on the diseases, and the mathematical methods of data processing. The paper describes the software platform general architecture, as well as the methods of diagnostic rules creation using the iteration model and the poly-procedural approach. The offered methods have sufficient generality and can be applied in research of a wide range of diseases.
Regulation of novelty seeking by midbrain dopamine D2/D3 signaling and ghrelin is altered in obesity

To investigate the relationship of novelty seeking traits (NS) with midbrain dopamine (DA) receptors and acyl ghrelin levels (AG) in normal weight (NW) and obese females.NS predict addictive behaviors and are hypothesized to contribute to eating behaviors. In healthy, NS are negatively associated with DA receptors in the substantia nigra (SN). The influence of obesity on the regulation of NS by DA signaling and AG was hypothesized.
Regression Based Integration of Demand Forecasting and Inventory Decision

In the traditional inventory decision-making, demand forecasting and inventory control decisions are made independently, so it is difficult to ensure that the inventory cost is minimized. To make the demand forecasting and inventory control be consistent, this paper proposes a cost regression model, in which the demand forecasting is combined with the inventory decisions, aiming to minimize the inventory cost rather than the forecast error. A computational example is presented, to show the difference of the integrated decision-making model and the decentralized decision-making model.
Nudity detection based on image zoning

Automatic nudity detection strategies play an important role in solutions focusing on controlling access to inappropriate content. These strategies usually apply filter or similar approaches in order to detect nudity in digital images. In this paper we propose a strategy for nudity detection based on applying image zoning. Moreover, we perform feature extraction using color and texture information, as well as feature selection, focusing on pointing out the most relevant features. SVM is used for image classification. Experimental results demonstrate that an effective improvement in terms of accuracy is attained by using the zoning strategy. In addition, the highest classification rates are obtained by combining two color-based and two texture-based features, which shows that it is possible to reduce the number of features while keeping high classification rates.
An Incremental Bayesian Model for Learning Syntactic Categories

We present an incremental Bayesian model for the unsupervised learning of syntactic categories from raw text. The model draws information from the distributional cues of words within an utterance, while explicitly bootstrapping its development on its own partially-learned knowledge of syntactic categories. Testing our model on actual child-directed data, we demonstrate that it is robust to noise, learns reasonable categories, manages lexical ambiguity, and in general shows learning behaviours similar to those observed in children.
Kestrel: A Programmable Array for Sequence Analysis

Kestrel is a programmable linear array processor designed for sequence analysis. Among other features, Kestrel includes an 8-bit word, a single-cycle add-and-minimize instruction, a multiplier and efficient communication using shared registers. This paper describes Kestrel's functional units in detail, and examines each of their effects on system performance. With functional prototype chips completed, we will assemble a full single-board Kestrel array, with 512 processing elements on eight chips, in early 1998.
Two-Phase Biomedical NE Recognition based on SVMs

Using SVMs for named entity recognition, we are often confronted with the multi-class problem. Larger as the number of classes is, more severe the multi-class problem is. Especially, one-vs-rest method is apt to drop the performance by generating severe unbalanced class distribution. In this study, to tackle the problem, we take a two-phase named entity recognition method based on SVMs and dictionary; at the first phase, we try to identify each entity by a SVM classifier and post-process the identified entities by a simple dictionary look-up; at the second phase, we try to classify the semantic class of the identified entity by SVMs. By dividing the task into two subtasks, i.e. the entity identification and the semantic classification, the unbalanced class distribution problem can be alleviated. Furthermore, we can select the features relevant to each task and take an alternative classification method according to the task. The experimental results on the GENIA corpus show that the proposed method is effective not only in the reduction of training cost but also in performance improvement: the identification performance is about 79.9(Fβ = 1), the semantic classification accuracy is about 66.5(Fβ = 1).
Differential localization of GABAA receptor subunits in relation to rat striatopallidal and pallidopallidal synapses

As a central integrator of basal ganglia function, the external segment of the globus pallidus (GP) plays a critical role in the control of voluntary movement. The GP is composed of a network of inhibitory GABA‐containing projection neurons which receive GABAergic input from axons of the striatum (Str) and local collaterals of GP neurons. Here, using electrophysiological techniques and immunofluorescent labeling we have investigated the differential cellular distribution of α1, α2 and α3 GABAA receptor subunits in relation to striatopallidal (Str‐GP) and pallidopallidal (GP‐GP) synapses. Electrophysiological investigations showed that zolpidem (100 nm; selective for the α1 subunit) increased the amplitude and the decay time of both Str‐GP and GP‐GP IPSCs, indicating the presence of the α1 subunits at both synapses. However, the application of drugs selective for the α2, α3 and α5 subunits (zolpidem at 400 nm, L‐838,417 and TP003) revealed differential effects on amplitude and decay time of IPSCs, suggesting the nonuniform distribution of non‐α1 subunits. Immunofluorescence revealed widespread distribution of the α1 subunit at both soma and dendrites, while double‐ and triple‐immunofluorescent labeling for parvalbumin, enkephalin, gephyrin and the γ2 subunit indicated strong immunoreactivity for GABAAα3 subunits in perisomatic synapses, a region mainly targeted by local axon collaterals. In contrast, immunoreactivity for synaptic GABAAα2 subunits was observed in dendritic compartments where striatal synapses are preferentially located. Due to the kinetic properties which each GABAAα subunit confers, this distribution is likely to contribute differentially to both physiological and pathological patterns of activity.
Identification of CIITA Regulated Genetic Module Dedicated for Antigen Presentation

The class II trans-activator CIITA is a transcriptional co-activator required for the expression of Major Histocompatibility Complex (MHC) genes. Although the latter function is well established, the global target-gene specificity of CIITA had not been defined. We therefore generated a comprehensive list of its target genes by performing genome-wide scans employing four different approaches designed to identify promoters that are occupied by CIITA in two key antigen presenting cells, B cells and dendritic cells. Surprisingly, in addition to MHC genes, only nine new targets were identified and validated by extensive functional and expression analysis. Seven of these genes are known or likely to function in processes contributing to MHC-mediated antigen presentation. The remaining two are of unknown function. CIITA is thus uniquely dedicated for genes implicated in antigen presentation. The finding that CIITA regulates such a highly focused gene expression module sets it apart from all other transcription factors, for which large-scale binding-site mapping has indicated that they exert pleiotropic functions and regulate large numbers of genes.
Pancreatic Duodenal Homeobox (PDX-1) in Health and Disease

Insulin is expressed exclusively in the adult beta-cells of the islets of Langerhans. Pancreatic Duodenum Homeobox-1 (PDX-1) is a major regulator of transcription in these cells. It transactivates the insulin gene by binding to a specific DNA motif in its promoter region. Glucose, the main physiological regulator of insulin secretion, also regulates insulin gene transcription through PDX-1. While acute exposure to high glucose concentrations causes an increase in PDX-1 binding, and consequently in insulin mRNA levels, chronic hyperglycemia (toxic to the beta-cell) leads to a decrease in PDX-1 and insulin levels. PDX-1 is absolutely required for pancreas development. In view of the selective expression in adult beta-cells, pancreatic agenesis in both the pdx-1 null mouse and a human carrying a homozygous mutation of PDX-1 was an unexpected and remarkable finding. The homozygous clinical phenotype was neonatal diabetes mellitus (DM) and exocrine insufficiency. Heterozygosity for PDX-1 mutations was found in some individuals with a newly characterized subtype of maturity-onset diabetes of the young (MODY4) and in others with type 2 DM. This review underlines the unique role of PDX-1 in maintaining adult beta-cell-specific functions in normal and disease-related states.
Controlling Social Stress in Virtual Reality Environments

Virtual reality exposure therapy has been proposed as a viable alternative in the treatment of anxiety disorders, including social anxiety disorder. Therapists could benefit from extensive control of anxiety eliciting stimuli during virtual exposure. Two stimuli controls are studied in this study: the social dialogue situation, and the dialogue feedback responses (negative or positive) between a human and a virtual character. In the first study, 16 participants were exposed in three virtual reality scenarios: a neutral virtual world, blind date scenario, and job interview scenario. Results showed a significant difference between the three virtual scenarios in the level of self-reported anxiety and heart rate. In the second study, 24 participants were exposed to a job interview scenario in a virtual environment where the ratio between negative and positive dialogue feedback responses of a virtual character was systematically varied on-the-fly. Results yielded that within a dialogue the more positive dialogue feedback resulted in less self-reported anxiety, lower heart rate, and longer answers, while more negative dialogue feedback of the virtual character resulted in the opposite. The correlations between on the one hand the dialogue stressor ratio and on the other hand the means of SUD score, heart rate and audio length in the eight dialogue conditions showed a strong relationship: r(6) = 0.91, p = 0.002; r(6) = 0.76, p = 0.028 and r(6) = −0.94, p = 0.001 respectively. Furthermore, more anticipatory anxiety reported before exposure was found to coincide with more self-reported anxiety, and shorter answers during the virtual exposure. These results demonstrate that social dialogues in a virtual environment can be effectively manipulated for therapeutic purposes.
Long-term use of secondary prevention medications for heart failure in Western Australia: a protocol for a population-based cohort study

Introduction Heart failure (HF) is a chronic, debilitating and progressive disease associated with high morbidity and mortality. Evidence-based medications (EBMs) are the cornerstone of management of patients with HF. In Australia, these EBMs are subsidised by the Commonwealth Government under the Pharmaceutical Benefits Scheme. Suboptimal dispensing and non-adherence to these EBMs have been observed in patients with HF. Our study will investigate trends in dispensing patterns, as well as adherence and persistence of EBMs for HF. We will also identify factors influencing these patterns and their impact on long-term clinical outcomes. Methods and analysis This whole population-based cohort study will use longitudinal data for people aged 65–84 years who were hospitalised for HF in Western Australia between 2003 and 2008. Linked state-wide and national data will provide patient-level information on medication dispensing, medical visits, hospitalisations and death. Drug dispensing trends will be described, drug adherence and persistence estimated and the association with all-cause/cardiovascular death and hospitalisations reported. Ethics and dissemination This project has received approvals from the Western Australian Department of Health Human Research Ethics Committee and the Western Australian Aboriginal Health Ethics Committee. Results will be published in relevant cardiology journals and presented at national and international conferences.
Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

Probabilistic RoadMaps (PRMs) have been successful for many high-dimensional motion planning problems. However, they encounter difficulties when mapping narrow passages. While many PRM sampling methods have been proposed to increase the proportion of samples within narrow passages, such difficult planning areas still pose many challenges. We introduce a novel algorithm, Spark PRM, that sparks the growth of Rapidly-expanding Random Trees (RRTs) from narrow passage samples generated by a PRM. The RRT rapidly generates further narrow passage samples, ideally until the passage is fully mapped. After reaching a terminating condition, the tree stops growing and is added to the roadmap. Spark PRM is a general method that can be applied to all PRM variants. We study the benefits of Spark PRM with a variety of sampling strategies in a wide array of environments. We show significant speedups in computation time over RRT, Sampling-based Roadmap of Trees (SRT), and various PRM variants.
Predicted Liquid Water Saturation in Unstructured Pore Networks Based on PEMFC GDL Porosity Profiles

An unstructured, two-dimensional pore network model is employed to describe the effect of through-plane porosity profiles on liquid water saturation within the gas diffusion layer (GDL) of the polymer electrolyte membrane fuel cell. Random fibre placements are based on the porosity profiles of six commercially available GDL materials recently obtained through x-ray computed tomography experiments. The pore space is characterized with a Voronoi diagram, and invasion percolation-based simulations are performed. It is shown that water tends to accumulate in regions of relatively high porosity due to the lower associated capillary pressures. It is predicted that GDLs tailored to have smooth porosity profiles will have fewer pockets of high saturation levels within the bulk of the material.Copyright © 2010 by ASME
Prediction performance of support vector machines on input vector normalization methods

Support vector machines (SVM) based on the statistical learning theory is currently one of the most popular and efficient approaches for pattern recognition problem, because of their remarkable performance in terms of prediction accuracy. It is, however, required to choose a proper normalization method for input vectors in order to improve the system performance. Various normalization methods for SVMs have been studied in this research and the results showed that the normalization methods could affect the prediction performance. The results could be useful for determining a proper normalization method to achieve the best performance in SVMs.
Outage performance of multi-hop hybrid FSO/RF communication systems

In this paper, five scenarios are considered for the realization of a multi-hop hybrid free-space optical/radio frequency (FSO/RF) communication system with decode-and-forward (DF) relaying, and analytical outage probability expressions are derived. The scenarios have a different number of FSO and RF links. The aim is to identify an optimum scenario to effectively improve the end-to-end reliability of the multi-hop hybrid FSO/RF system. Outage performance is investigated for the scenarios under various weather conditions through numerical examples. It is identified that for a given link distance the end-to-end outage probability is minimized when using a hybrid FSO/RF link between every two successive nodes of the multi-hop system, and this requires the maximum number of FSO and RF links to be deployed.
A New Machine Translation Decoder Based on Artificial Immune System

This paper focuses on decoding as main part of statistical machine translation. Decoding is considering as a NP-complete algorithm that requires intelligent heuristics to and optimum solutions. In order to solve this problem, we proposed a decoder named DAIS based on the meta-heuristic of artificial immune system. The evaluation is performed on two different corpora. The obtained translations show that the proposed approach obtains encouraging results by comparing them to those of the most known decoders in the field
A Compendium for Mycoplasma pneumoniae

Historically, atypical pneumonia was a term used to describe an unusual presentation of pneumonia. Currently, it is used to describe the multitude of symptoms juxtaposing the classic symptoms found in cases of pneumococcal pneumonia. Specifically, atypical pneumonia is a syndrome resulting from a relatively common group of pathogens including Chlamydophila sp. , and Mycoplasma pneumoniae. The incidence of M. pneumoniae pneumonia in adults is less than the burden experienced by children. Transmission rates among families indicate children may act as a reservoir and maintain contagiousness over a long period of time ranging from months to years. In adults, M. pneumoniae typically produces a mild, “walking” pneumonia and is considered to be one of the causes of persistent cough in patients. M. pneumoniae has also been shown to trigger the exacerbation of other lung diseases. It has been repeatedly detected in patients with bronchitis, asthma, chronic obstructive pulmonary disorder, and cystic fibrosis. Recent advances in technology allow for the rapid diagnosis of M. pneumoniae through the use of polymerase chain reaction or rapid antigen tests. With this, more effort has been afforded to identify the causative etiologic agent in all cases of pneumonia. However, previous practices, including the overprescribing of macrolide treatment in China and Japan, have created increased incidence of macrolide-resistant M. pneumoniae. Reports from these countries indicate that >85% of M. pneumoniae pneumonia pediatric cases are macrolide-resistant. Despite its extensively studied past, the smallest bacterial species still inspires some of the largest questions. The developments in microbiology, diagnostic features and techniques, epidemiology, treatment and vaccines, and upper respiratory conditions associated with M. pneumoniae in adult populations are included within this review.
Multiorgan Metastasis of Human HER-2+ Breast Cancer in Rag2−/−;Il2rg−/− Mice and Treatment with PI3K Inhibitor

In vivo studies of the metastatic process are severely hampered by the fact that most human tumor cell lines derived from highly metastatic tumors fail to consistently metastasize in immunodeficient mice like nude mice. We describe a model system based on a highly immunodeficient double knockout mouse, Rag2−/−;Il2rg−/−, which lacks T, B and NK cell activity. In this model human metastatic HER-2+ breast cancer cells displayed their full multiorgan metastatic potential, without the need for selections or additional manipulations of the system. Human HER-2+ breast cancer cell lines MDA-MB-453 and BT-474 injected into Rag2−/−;Il2rg−/− mice faithfully reproduced human cancer dissemination, with multiple metastatic sites that included lungs, bones, brain, liver, ovaries, and others. Multiorgan metastatic spread was obtained both from local tumors, growing orthotopically or subcutaneously, and from cells injected intravenously. The problem of brain recurrencies is acutely felt in HER-2+ breast cancer, because monoclonal antibodies against HER-2 penetrate poorly the blood-brain barrier. We studied whether a novel oral small molecule inhibitor of downstream PI3K, selected for its penetration of the blood-brain barrier, could affect multiorgan metastatic spread in Rag2−/−; Il2rg−/− mice. NVP-BKM120 effectively controlled metastatic growth in multiple organs, and resulted in a significant proportion of mice free from brain and bone metastases. Human HER-2+ human breast cancer cells in Rag2−/−;Il2rg−/− mice faithfully reproduced the multiorgan metastatic pattern observed in patients, thus allowing the investigation of metastatic mechanisms and the preclinical study of novel antimetastatic agents.
Three-dimensional surface profilometry using structured liquid crystal grating

This paper describes a device for measuring the three dimensional surface profile using a grating projection method. A phase shifting technique without any mechanical moving is expected for profile analysis. A grating that is a key component in this technique is made using an active controlled liquid crystal (LC). This LC grating has the performances of more than 8 bits of gray levels and its grating period is 50 micro-meters per line without any colored filters. Surface profiles of some samples are measured for the demonstration of the system.
System for Automated Interactive Lighting (SAIL)

Successful lighting in video games is more than a physically accurate illumination model. Aesthetics and function are of equal or greater importance. Lighting designers may deviate from physical accuracy to help a player identify an important object or to more powerfully evoke a desired emotion. Under the assumption that fulfilling the pipeline needs of interactive lighting design requires more than solving the computer rendering equation, we set out to develop a System for Automated Interactive Lighting (SAIL). The goal for SAIL was to develop an adaptive system that maintains lighting design goals (aesthetic and functional) in the context of unpredictable, interactive experiences. This paper presents SAIL and the results of a qualitative evaluation of SAIL's contributions. We describe the algorithms of SAIL, where it succeeds, and where it fails. We conclude with a plan for future work.