Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability of one-class classification in mapping and detecting changes in bare peat surfaces in the tundra

ABSTRACT Arctic areas have experienced greening and changes in permafrost caused by climate change during recent decades. However, there has been a lack of automated methods in mapping changes in fine-scale patterns of permafrost landscapes. We mapped areal coverage of bare peat areas and changes in them in a peat plateau located in north-western Russia between 2007 and 2015. We utilized QuickBird and WorldView-3 satellite image data in an object-based setting. We compared four different one-class classifiers (one-class support vector machine, binary support vector machine, random forest, rotation forest) both in a fully supervised binary setting and with positive and unlabelled training data. There was notable variation in classification performance. The bare peat area F-score varied between 0.77 and 0.96 when evaluated by cross-validated training data and between 0.22 and 0.57 when evaluated by independent test data. Overall, random forest performed the most robustly but all classifiers performed well in some classifications. During the 8 year period, there was a 21%–26% decrease in the bare peat areal coverage. We conclude that (1) tested classifiers can be used in one-class settings and (2) there is a need to develop methods for tracking changes in single land cover types.
Chronic bacterial prostatitis and irritable bowel syndrome: effectiveness of treatment with rifaximin followed by the probiotic VSL#3

This study was undertaken to evaluate the influence of treatment with rifaximin followed by the probiotic VSL#3 versus no treatment on the progression of chronic prostatitis toward chronic microbial prostate-vesiculitis (PV) or prostate-vesiculo-epididymitis (PVE). A total of 106 selected infertile male patients with bacteriologically cured chronic bacterial prostatitis (CBP) and irritable bowel syndrome (IBS) were randomly prescribed rifaximin (200 mg, 2 tablets bid, for 7 days monthly for 12 months) and probiotic containing multiple strains VSL#3 (450 × 109 CFU per day) or no treatment. Ninety-five of them (89.6%) complied with the therapeutic plan and were included in this study. Group A = “6Tx/6-”: treatment for the initial 6 and no treatment for the following 6 months (n = 26); Group B = “12Tx”: 12 months of treatment (n = 22); Group C = “6-/6Tx”: no treatment for the initial 6 months and treatment in the last 6 months (n = 23); Group D = “12-”: no treatment (n = 24). The patients of Groups A = “6Tx/6-” and B = “12Tx” had the highest frequency of chronic prostatitis (88.5% and 86.4%, respectively). In contrast, group “12-”: patients had the lowest frequency of prostatitis (33.4%). The progression of prostatitis into PV in groups “6Tx/6-” (15.5%) and “6-/6Tx” (13.6%) was lower than that found in the patients of group “12-” (45.8%). Finally, no patient of groups “6Tx/6-” and “6-/6Tx” had PVE, whereas it was diagnosed in 20.8% of group “12-” patients. Long-term treatment with rifaximin and the probiotic VSL#3 is effective in lowering the progression of prostatitis into more complicated forms of male accessory gland infections in infertile patients with bacteriologically cured CBP plus IBS.
A sleep monitoring system based on audio, video and depth information for detecting sleep events

The purpose of this study is to develop a non-invasive sleep monitoring system to distinguish sleep disturbances based on multiple sensors. Unlike clinical sleep monitoring which records biological information such as EEG, EOG, and EMG, in this study, we aim to identify occurrences of events from a sleep environment. A device with an infrared depth sensor, a RGB camera, and a four-microphone array is used to detect three types of events: motion events, lighting events, and sound events. Given streams of depth signals and color images, we build two background models to detect movements and lighting effects, and audio signals are scored simultaneously. Moreover, we classify events by an epoch approach algorithm and provide a graphical sleep diagram for browsing corresponding video clips. Experimental results in sleep condition show the efficiency and reliability of our system, and it is convenient and cost-effective to be used in home context.
Storage hierarchy management for scientific computing

Scientific computation has always been one of the driving forces behind the design of computer systems. As a result, many advances in CPU architecture were first developed for high-speed supercomputer systems, keeping them among the fastest computers in the world. However, little research has been done in storing the vast quantities of data that scientists manipulate on these powerful computers. This thesis first characterizes scientists' usage of a multi-terabyte tertiary storage system attached to a high-speed computer. The analysis finds that the number of files and average file size have both increased by several orders of magnitude since 1980. The study also finds that integration of tertiary storage with secondary storage is critical. Many of the accesses to files stored on tape could have easily been avoided had scientists seen a unified view of the mass storage hierarchy instead of the two separate views of the system studied. This finding was a major motivation of the design of the RAMA file system. The remainder of the thesis describes the design and simulation of a massively parallel processor (MPP) file system that is simple, easy to use, and integrates well with tertiary storage. MPPs are increasingly commonly used for scientific computation, yet their file systems require great attention to detail to get acceptable performance. Worse, a program that performs well on one machine may perform poorly on a similar machine with a slightly different file system. RAMA solves this problem by pseudo-randomly distributing data to a disk attached to each processor, making performance independent of program usage patterns. It does this without sacrificing the high performance that scientific users demand, as shown by simulations comparing the performance of RAMA and a striped file system on both real and synthetic benchmarks. Additionally, RAMA can be easily integrated with tertiary storage systems, providing a unified view of the file system spanning both disk and tape systems. RAMA's ease of use and simplicity of design make it an ideal choice for the massively parallel computers used by the scientific community.
Diagnosing the Impacts of Northern Hemisphere Surface Albedo Biases on Simulated Climate

Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.
Identification and Validation of Quantitative PCR Reference Genes Suitable for Normalizing Expression in Normal and Dystrophic Cell Culture Models of Myogenesis

The coordinated differentiation of myoblasts to mature muscle is essential for muscle development and repair, and study of the myogenic program in health and disease is critical to the understanding and treatment of muscle pathologies. Use of quantitative RT-PCR to analyse gene expression in cell culture models of muscle differentiation can be highly informative, but data must be normalized to one or more suitable reference genes. Myogenesis is highly dynamic, thus identification of genes with stable expression throughout this process is challenging. Establishing a common set of reference genes suitable for measuring expression in both healthy and disease models would be of considerable advantage. We measured expression of 11 candidate normalization genes (Cdc40, Htatsf1, Ap3d1, Csnk2a2, Fbxw2, Fbxo38, Pak1ip1, Zfp91, GAPDH, ActB, 18S) in three cell culture models of myogenesis (C2C12 , H2K2B4, and the dystrophic line H2KSF1). Strong and weak normalization candidates were identified using the software packages Bestkeeper, geNorm and Normfinder, then validated against several known myogenic markers (MyoD, myogenin, MEF2C, dystrophin). Our data show that Csnk2a2 and Ap3d1 are suitable for normalizing gene expression during differentiation in both healthy and dystrophic cell-culture models, and that the commonly-used reference standards 18S, ActB and GAPDH are exceptionally poor candidates.
Pictorial Human Spaces: How Well Do Humans Perceive a 3D Articulated Pose?

Human motion analysis in images and video is a central computer vision problem. Yet, there are no studies that reveal how humans perceive other people in images and how accurate they are. In this paper we aim to unveil some of the processing-as well as the levels of accuracy-involved in the 3D perception of people from images by assessing the human performance. Our contributions are: (1) the construction of an experimental apparatus that relates perception and measurement, in particular the visual and kinematic performance with respect to 3D ground truth when the human subject is presented an image of a person in a given pose, (2) the creation of a dataset containing images, articulated 2D and 3D pose ground truth, as well as synchronized eye movement recordings of human subjects, shown a variety of human body configurations, both easy and difficult, as well as their 're-enacted' 3D poses, (3) quantitative analysis revealing the human performance in 3D pose re-enactment tasks, the degree of stability in the visual fixation patterns of human subjects, and the way it correlates with different poses. We also discuss the implications of our findings for the construction of visual human sensing systems.
Integrating Omnidirectional Perception and Drive in the Body of a Soccer Robot

The aim of this paper is to provide an overview of the robots designed at the University of Padua. Most of the robot were designed to work in the Middle-Size League of the RoboCup Competitions. In this paper we detail the insights we had designing so many robots and the new way of looking at omnidirectional vision we developed. Omnidirectional vision is not only a powerful sensor, but linked to a careful body design and an omnidirectional drive can realise a much more reactive machine, able to sense and act in real time, without any time delay due to limits of the sensory horizon or to constraints of the motion drive.
General Human Traits Oriented Generic Elastic Model for 3D Face Reconstruction

We propose a Simplified Generic Elastic Model (S-GEM) which intends to construct a 3D face from a given 2D face image by making use of a set of general human traits viz., Gender, Ethnicity and Age (GEA). Different from the original GEM model which employs and deforms the mean depth value of 3D sample faces according to a specific 2D input face image, we hypothesise that the variations inherent on the depth information for individuals are significantly mitigated by narrowing down the target information via a selection of specific GEA traits. This is achieved by representing the unknown 3D facial feature points of a 2D input as a Gaussian Mixture Model (GMM) of that of the samples of its own GEA type. It is then further incorporated into a Bayesian framework whereby the 3D face reconstruction is posed as estimating the PCA coefficients of a statistical 3D face model, given the observation of 2D feature points, however, with their respective depth as hidden variables. By making the reasonable assumption that the support area of each component of GMM is small enough, the proposed method is reduced to choose the depth values of the features points of a sample face that is nearest to the 2D input face. Thus the 3D reconstruction is obtained with depth-value augmented feature points rather than the 2D ones in normal PCA statistical model based reconstruction. The proposed method has been tested with the USF 3D face database as well as the FRGC dataset. The experimental results show that the proposed S-GEM has achieved improved reconstruction accuracy, consistency, and the robustness over the conventional PCA based and the GEM (mean-face feature points) reconstruction, and also yields enhanced visual improvements on certain facial features.
An empirical evaluation of classifier combination schemes for predicting user navigational behavior

In this paper we present a simple classification system for predicting user behavior when browsing a Web site devoted to informing about university degrees. More than building a very accurate classifier, we want to study which kind of combination scheme performs better in front of a complexity constraint. A set of marks embedded in the Web pages being visited by each user is used as the input for a classification system which decides whether the user will be interested in accessing other related parts of the Web site or not. We compare two different classification systems: the first one is built using decision trees for the whole data set, with the aim of studying user profiles and variable importance, while the second one combines simple classifiers based on small decision trees using a combination of the voting and cascading paradigms, in order to make predictions which evolve during the period of time the Web site is collecting data. Results show that it is possible to extract useful information for studying user profiles and for predicting user behavior using small decision trees.
International Conference on Computational Science , ICCS 2013 A Small-World Network Immune from Random Failures and Resilient to Targeted Attacks

A network architecture with bimodal degree distribution robust for both random failures and targeted attacks has been proposed. This paper studies the robustness of ”n-Star” network which is proposed by the author as a new Small-World network by comparing with the bimodal degree distribution network and a scale-free network. As a result, the n-Star network is found to be not only more robust than the bimodal degree distribution network and the scale-free network, but also has the good characteristics maintaining the property of Small-World even under the above failures and attacks. Furthermore, even if the n-Star network were more severely damaged and the network doesn’t work well at all with some nodes alive, the damaged network could be recovered using the alive nodes by connecting the links among the nodes in a self-organizing manner of communication. We show a resilient algorithm to automatically recover from the damaged network, which can hold the property of Small-World for the n-Star network.
Data Structures for Eecient Broker Implementation

With the profusion of text databases on the Internet, it is becoming increasingly hard to nd the most useful databases for a given query. To attack this problem, several existing and proposed systems employ brokers to direct user queries, using a local database of summary information about the available databases. This summary information must e ectively distinguish relevant databases, and must be compact while allowing e cient access. We o er evidence that one broker, GlOSS, can be e ective at locating databases of interest even in a system of hundreds of databases, and examine the performance of accessing the GlOSS summaries for two promising storage methods: the grid le and partitioned hashing. We show that both methods can be tuned to provide good performance for a particular workload (within a broad range of workloads), and discuss the tradeo s between the two data structures. As a side e ect of our work, we show that grid les are more broadly applicable than previously thought; in particular, we show that by varying the policies used to construct the grid le we can provide good performance for a wide range of workloads even when storing highly skewed data.
Temporal lobe epilepsy and affective disorders: the role of the subgenual anterior cingulate cortex

Objective Reduced deactivation within the default mode network (DMN) is common in individuals with primary affective disorders relative to healthy volunteers (HVs). It is unknown whether similar network abnormalities are present in temporal lobe epilepsy (TLE) patients with a history of affective psychopathology. Methods 17 TLE patients with a lifetime affective diagnosis, 31 TLE patients with no formal psychiatric history and 30 HVs were included. We used a visuo-spatial ‘n-back’ paradigm to compare working memory (WM) network activation between these groups. Post hoc analyses included voxel-based morphometry and diffusion tensor imaging. The Beck Depression Inventory-Fast Screen and Beck Anxiety Inventory were completed on the day of scanning. Findings Each group activated the fronto-parietal WM networks and deactivated the typical DMN in response to increasing task demands. Group comparison revealed that TLE patients with lifetime affective morbidity showed significantly greater deactivation in subgenual anterior cingulate cortex (sACC) than either the TLE-only or the HVs (p<0.001). This effect persisted after covarying for current psychotropic medication and severity of current depressive/anxiety symptoms (all p<0.001). Correlational analysis revealed that this finding was not driven by differences in task performance. There were no significant differences in grey matter volume or structural connectivity between the TLE groups. Conclusions Our results provide novel evidence suggesting that affective psychopathology in TLE has a neurobiological correlate, and in this context the sACC performs differently compared with network activity in primary affective disorders.
Intraoperative neurocytology of primary central nervous system neoplasia: A simplified and practical diagnostic approach

Intraoperative consultations may pose considerable diagnostic challenge to the neuropathologist in diagnosing primary and metastatic neoplasms of the central nervous system (CNS). Cytological preparations in the form of squash, touch, imprint or smears are few of the available modalities in addition to the frozen section (FS). Although the latter is superior in providing both histologic patterns and cytomorphologic details yet smears are of vital importance when tissue available is limited (stereotactic biopsy), scrutinisation of intercellular matrix (astrocytoma versus oligodendroglioma) and evaluation of discohesive cells (lymphoma, pituitary adenoma) and in inflammatory lesions. This review is intended to emphasize the value, applicability and limitations of neurocytology aiming to expedite the intraoperative smear-based diagnoses of CNS neoplasia as per the World Health Organization (WHO) classification. We recommend that whenever possible, both smears and FS should be examined concomitantly and in a correlative manner. In the unlikely event of a mismatch between the findings on smear and FS, intraoperative diagnosis is primarily based on FS, if adequate tissue is available. However, each case must be evaluated on its own merit and in difficult cases relevant differential diagnoses should be offered to facilitate surgical decisions and optimally triage patient management.
Hybrid Local Feature Selection In DNA Analysis Based Cancer Classification

Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data and increasing learning accuracy. The development of microarray dataset technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer. The challenging task in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data because extracted genes from microarray dataset have many unwanted datas not related to cancer. In this project we attempt explore a novel hybrid wrapper and filter feature selection algorithm for classification problem using a memetic framework i.e., a combination of genetic algorithm (GA) and local search (LS) has been proposed. The LS is performed using correlation based filter methods are discritize, ranking and redundancy elimination with symmetrical uncertainty (SU) measure . using this hybrid method we can able find cancer related gene, From the larger amount of gene datas . using that smaller dataset doctors can able to find the affected gene and provide better treatment. The efficiency and the effectiveness of the method are demonstrated through extensive comparisons with other methods using real-world datasets of high dimentionality
Complete Genome Sequence of Agrobacterium tumefaciens Ach5

ABSTRACT Agrobacterium tumefaciens is a phytopathogenic bacterium that causes crown gall disease. The strain Ach5 was isolated from yarrow (Achillea ptarmica L.) and is the wild-type progenitor of other derived strains widely used for plant transformation. Here, we report the complete genome sequence of this bacterium.
Controlling Wild Bodies Using Linear Temporal Logic

There is substantial interest in controlling a group of bodies from specifications of tasks given in a high-level, human-like language. This paper proposes a methodology that creates low-level hybrid controllers that guarantee that a group of bodies execute a high-level specified task without dynamical system modeling, precise state estimation or state feedback. We do this by exploiting the wild motions of very simple bodies in an environment connected by gates which serve as the system inputs, as opposed to motors on the bodies. We present experiments using inexpensive hardware demonstrating the practical feasibility of our approach to solving tasks such as navigation, patrolling, and coverage.
Data Hiding: Current Status and Key Issues

Image steganography is a covert communication method that uses an image as the cover to hide the truth from potential attackers that some secret message hidden in the image is being transported. When we appreciate the astonishing beauty of a world famous picture in its digital form on the computer, it is hard to imagine that the picture might actually be working as a messenger, carrying some invisible important secret message along with it. In other words, steganography is a collection of cryptographic techniques that provide protection to the secret message by offering it the appearance of an image. In this survey paper, our focus is on the development and current status of steganographic techniques for grayscale images. We shall separately introduce schemes with high hiding capacities and schemes with high imperceptibility. The advantages and disadvantages of those schemes will be closely analyzed, offering directions for our future research efforts.