Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Duplicate Removal

The distributed duplicate removal problem is concerned with the detection and subsequent elimination of all duplicate elements in a given multiset that is distributed over several computers connected by a network. Sanders et al. [48] outline a communication efficient algorithm solving this problem. It uses distributed compressed single shot Bloom filters to identify distinct elements using minimal communication effort. The filter however produces false positive results. Thus, all elements passing the filter are post processed using a traditional hash-based distributed duplicate removal algorithm in order to distinguish real duplicates from false positives. This thesis complements the theoretical analysis with an experimental evaluation. We transform the high-level description of the algorithm into an efficient implementation than runs on a shared-nothing system and exploits the shared memory parallelism capabilities of its nodes for all computationally intensive operations. The results of our analysis substantiate the benefits predicted by theory. Our implementation outperforms the best-suited traditional algorithm up to the point where the input data contains 50% duplicates. When executed on datasets that contain less than 10% duplicates, our implementation achieves a communication volume that is more than one order of magnitude smaller than that of its competitor. Zusammenfassung Ziel der verteilten Duplikaterkennung ist die Identifikation von Elementen, welche mehrfach in einer großen, über mehrere Rechenknoten verteilten Datenmenge vorkommen. Sanders et al. [48] präsentieren einen verteilten Algorithmus, welcher dieses Problem in einer besonders kommunikationseffizienten Art und Weise löst. In einer Vorverarbeitungsphase werden mit Hilfe eines verteilten, platz-effizienten Bloom Filters zunächst möglichst viele distinkte Elemente als solche identifiziert und somit die Gesamtmenge der noch zu betrachtenden Elemente stark reduziert. Da hierbei jedoch auch falsch positive Ergebnisse auftreten, müssen alle als potentiell nicht distinkt erkannten Elemente in einer zweiten Phase noch einmal überprüft werden. Hierzu wird ein klassischer Hash-basierter Algorithmus zur verteilten Duplikaterkennung angewendet. Die vorliegende Arbeit ergänzt die theoretische Analyse durch eine praktische Evaluation. Wir erarbeiten hierzu eine effiziente Implementierung für Shared-Nothing Systeme. Besonders rechenintensive Schritte des Algorithmus werden zusätzlich durch Shared-MemoryProgrammierung innerhalb eines Knotens parallelisiert. Die Ergebnisse unserer experimentellen Untersuchung untermauern die durch die Theorie vorhergesagten Vorteile des Algorithmus. Unsere Implementierung ist signifikant schneller als der am besten geeignete klassische Ansatz solange die Eingabedaten zu weniger als 50% aus Duplikaten bestehen. Wird der Algorithmus auf Datensätzen ausgeführt, die zu weniger als 10% aus Duplikaten bestehen, so ist das gesamte Kommunikationsvolumen zudem mehr als eine Größenordnung kleiner als das des klassischen Konkurrenten.
Therapeutic interaction detection for serious games in physical rehabilitation

Serious games gained popularity in recent years together with the use of modern input devices. Mainly marker-less motion tracking cameras play a special role in the automation of physical rehabilitation. These inexpensive cameras can provide accurate information about the movements and poses of the subject without complicated setup. However, these cameras are still not perfect and experience problems in particular poses, setups or when users are interacting. Interaction between a patient and the therapist is a crucial and inevitable aspect of the therapy and results in frustrations when using new technologies. In this paper we propose a method that can identify whether a therapist is interacting with a patient or not, in order to improve not only the therapy sessions but also the quality of the data collected during the gameplay or assessment, automated with the modern input sensors. We compare our measurement results with a marker based motion tracking system (Vicon) and additional scores to demonstrate the importance of identifying interactions between a therapist and patients.
Immune Escape Strategies of Malaria Parasites

Malaria is one of the most life-threatening infectious diseases worldwide. Immunity to malaria is slow and short-lived despite the repeated parasite exposure in endemic areas. Malaria parasites have evolved refined machinery to evade the immune system based on a range of genetic changes that include allelic variation, biomolecular exposure of proteins, and intracellular replication. All of these features increase the probability of survival in both mosquitoes and the vertebrate host. Plasmodium species escape from the first immunological trap in its invertebrate vector host, the Anopheles mosquitoes. The parasites have to pass through various immunological barriers within the mosquito such as anti-microbial molecules and the mosquito microbiota in order to achieve successful transmission to the vertebrate host. Within these hosts, Plasmodium species employ various immune evasion strategies during different life cycle stages. Parasite persistence against the vertebrate immune response depends on the balance among virulence factors, pathology, metabolic cost of the host immune response, and the parasites ability to evade the immune response. In this review we discuss the strategies that Plasmodium parasites use to avoid the vertebrate host immune system and how they promote successful infection and transmission.
CD27+ memory and CD27- effector CD8+ T cells are responsible for a decreased production of proinflammatory cytokines in HLA B27-positive subjects.

BACKGROUND AND OBJECTIVE AS and other spondyloarthritides (SpA) are mostly chronic inflammatory rheumatic diseases characterised by a strong association with HLA B27. Recent data from our group have suggested that AS patients have a diminished secretion capacity of inflammatory cytokines, possibly associated with HLA B27. The aim of this study was to identify CD4+ and CD8+ T cell subsets responsible for the observed lower cytokine secretion capacity in HLA B27-positives. METHODS Highly purified (> 98%) CD4+ and CD8+ T cells of HLA B27-positive AS patients (n = 13), healthy HLA B27-positive (n = 7) and -negative controls (n = 9) were stimulated for 6h with PMA/ Ionomycin and, after fixation, stained for surface markers CD45RA and CD27 and cytokines TNFalpha, IFNgamma, IL-4 and IL-10. RESULTS CD27+ CD45RA- memory CD8+ T cells of HLA B27-positive subjects showed a significantly lower percentage of TNFalpha (median 71.4%) and IFNgamma production (median 69.7%) than HLA B27-negative controls (TNFalpha 85.1%; p < or = 0.027; IFNgamma 82.7%, p < or = 0.026). A similar result was also detected in CD27- CD45RA+ effector CD8+ T cells of which 43.2% produced TNFalpha and 66.3% IFNgamma in HLA B27-positive subjects, respectively, compared to 75.6% TNFalpha and 84.4% IFNgamma producing T cells in HLA B27-negatives (p < or = 0.045 and p < or = 0.062, respectively). For all CD4+ T cell subsets no significant differences between HLA B27-positive and HLA B27-negative donors were observed, regarding neither the frequency of IFNgamma, TNFalpha, IL-4 or IL-10 producers nor the coexpression of IFNgamma and IL-4 in memory subsets. CONCLUSIONS HLA B27-positive subjects are characterized by a low proinflammatory cytokine production in CD8+ effector and memory T cell subsets. This suggests an influence of HLA B27 on cytokine production in antigen-experienced CD8+ T cells.
How to Study: A Brief Guide

Introduction 1. Manage your time 2. Take notes in class & rewrite them at home 3. Study hard subjects first & study in a quiet place 4. Read texts actively & slowly, before & after class 5. Do your homework 6. Study for exams 7. Take Exams 8. Do research & write essays 9. Do I really have to do all this? 10. Are there other websites that give study hints? 11. HOW TO STUDY http://www.cse. buffalo.edu/~rapaport/howtostudy.html
A Framework for Aggregation Constraint Monitoring in Federated Databases

The aggregation problem arises whenever some collection of data has a classi cation strictly greater than that of the individual data forming the aggregate. This paper addresses such a problem in the context of federated databases, where data can be distributed and heterogeneous. Aggregation constraints are modelled as logic expressions of the FEderated Logic Language (FELL), and these specify the combinations of basic transactions that need to be checked against either single or multiple states of a federation. Transitional graphs are built from aggregation constraints to model all the required sub-computations for enforcing federated security policies. We also develop a monitoring technique, called Non-Linear Marking Technique (NLMT), to check the nodes of transitional graphs against the past and current states of a federation.
OpinAIS: An Artificial Immune System-based Framework for Opinion Mining

This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones.
Variation in antibiotic prescribing and its impact on recovery in patients with acute cough in primary care: prospective study in 13 countries

Objective To describe variation in antibiotic prescribing for acute cough in contrasting European settings and the impact on recovery. Design Cross sectional observational study with clinicians from 14 primary care research networks in 13 European countries who recorded symptoms on presentation and management. Patients followed up for 28 days with patient diaries. Setting Primary care. Participants Adults with a new or worsening cough or clinical presentation suggestive of lower respiratory tract infection. Main outcome measures Prescribing of antibiotics by clinicians and total symptom severity scores over time. Results 3402 patients were recruited (clinicians completed a case report form for 99% (3368) of participants and 80% (2714) returned a symptom diary). Mean symptom severity scores at presentation ranged from 19 (scale range 0 to 100) in networks based in Spain and Italy to 38 in the network based in Sweden. Antibiotic prescribing by networks ranged from 20% to nearly 90% (53% overall), with wide variation in classes of antibiotics prescribed. Amoxicillin was overall the most common antibiotic prescribed, but this ranged from 3% of antibiotics prescribed in the Norwegian network to 83% in the English network. While fluoroquinolones were not prescribed at all in three networks, they were prescribed for 18% in the Milan network. After adjustment for clinical presentation and demographics, considerable differences remained in antibiotic prescribing, ranging from Norway (odds ratio 0.18, 95% confidence interval 0.11 to 0.30) to Slovakia (11.2, 6.20 to 20.27) compared with the overall mean (proportion prescribed: 0.53). The rate of recovery was similar for patients who were and were not prescribed antibiotics (coefficient −0.01, P<0.01) once clinical presentation was taken into account. Conclusions Variation in clinical presentation does not explain the considerable variation in antibiotic prescribing for acute cough in Europe. Variation in antibiotic prescribing is not associated with clinically important differences in recovery. Trial registration Clinicaltrials.gov NCT00353951.
Interactive video asset retrieval using sketched queries

We present a new algorithm for searching video repositories using free-hand sketches. Our queries express both appearance (color, shape) and motion attributes, as well as semantic properties (object labels) enabling hybrid queries to be specified. Unlike existing sketch based video retrieval (SBVR) systems that enable hybrid queries of this form, we do not adopt a model fitting/optimization approach to match at query-time. Rather, we create an efficiently searchable index via a novel space-time descriptor that encapsulates all these properties. The real-time performance yielded by our indexing approach enables interactive refinement of search results within a relevance feedback (RF) framework; a unique contribution to SBVR. We evaluate our system over 700 sports footage clips exhibiting a variety of clutter and motion conditions, demonstrating significant accuracy and speed gains over the state of the art.
COMPASSIONATE WRATH: TRANSPERSONAL APPROACHES TO ANGER

In this essay, two transpersonally-oriented approaches to anger are closely examined, and a radical reconsideration of angel' is recommended. The first approach (Mindfully Held Anger) refers to practices in which anger is consciously contained, not affectively expressed, and transegoically attended to, with a key intention being neither to suppress anger nor act it out. The second approach (Heart-Anger) refers to practices in which openly expressed anger and compassion mindfully coexist. Working with anger in transpersonal contexts is, it is concluded, not only possible, but necessaryif anger is truly to be a positive force in our lives. There is widespread disagreement in various literatures (psychological, psychothera­ peutic, philosophical, religious, and anthropological) regarding (a) the nature, value, and use of anger; (b) the relationship between anger and aggression, anger and reason, anger and intimacy, anger and spirituality; and (c) how to "work" practically with anger in various contexts (Masters, 1999). In this article, two transpersonally oriented approaches to working with anger are examined, and a radical reconsideration of anger is recommended. The two more conventional approaches to working with anger can be described as "anger-in" and "anger-out. " Advocates of both approaches continue to debate each other, citing and often making moral real estate out of the dangers of either letting anger out or keeping it in (as though it were but an endogenous entity). However, there is much more to working with anger than merely adopting a position along the anger-in/anger-out continuum. One can, for example, approach anger not just physiologically and psychologically, but also spiritually. In fact, working with anger in transpersonal contexts is, as will be argued, not only possible, but necessary, if anger is truly to be a positive force in our lives.
Screening for hypothyroidism in schools for children with learning difficulties in Southeastern Nigeria.

Iodine deficiency disorder is a public health pro- blem in NCT of Delhi. The present study was conducted in of to assess the current iodine nutrition status among school age children (6–11 years). Thirty clusters (schools) were selected by utilizing the population proportionate to size (PPS) cluster sampling methodol- ogy. A total of 1393 school age children were included. Urine samples were collected and tested for urinary iodine excretion (UIE). The Median UIE was found to be 200 m g/L. The salt samples collected from study subjects revealed that 87% of salt samples had stipulated level of iodine of 15ppm and more. In order to eliminate IDD, there is a need to sustain the supply of iodized salt and monitor its iodine con-tent regularly.
Sequence families sets constructed from quadratic congruence codes for use in secure spread spectrum watermarking for multimedia

Digital watermarking for multimedia information has been widely adopted as a measure to detect copyright infringements. In many watermarking schemes, often only one watermarking signature is used in the embedding of hidden information. This paper introduces new multiple sequence families with good auto- and cross-correlation properties, which are ideal in the application of digital watermarking. It is also envisaged that the good cross-correlation property of these multiple families of sequences allows for the use of multiple signatures in a watermarking scheme. Multiple signatures increases the robustness of the watermarking scheme and also retrieving for independent signatures is straightforward due to the low cross-correlation property of the the member sequence.
Time varying covariance structures in financial markets

Reliable estimates of variances and covariances are crucial for portfolio management and risk controlling. This paper investigates alternative methods to estimate time varying variance-covariance matrices: ordinary estimates and exponentially weighted moving averages in comparison to Markov switching models. Different criteria are used to assess the quality of estimators. Bootstrapped confidence intervals for variances and correlations allow to compare the reliability of these estimates. Most importantly, we investigate the quality of these estimators directly in the context of portfolio applications and risk measurement. Therefore, we evaluate the performance of these estimation concepts on the basis of their suitability to select portfolios according to the minimum-variance criterion. This benchmark allows to infer the estimators' ability to capture diversification benefits without diluting this evaluation by any concept aiming to forecast returns. As an additional benchmark we evaluate the validity of forecasted confidence intervals.
Answering questions based on gradually learned knowledge from the web using lightweight semantics

In this paper, we describe an approach to answering natural language questions that is based on knowledge acquired from the Web by a current search engine. Our system gradually builds a lightweight semantic knowledge base that is used to select the answer. Explicit feedback from the interested fellow helps readjust weights of the triplets in a learning-like process. Questions are not restricted to be of specific kinds. Already in an early stage of experimenting, our system was able to bring notable improvement in comparison to its underlying search engine.
Brain MRI segmentation for tumor detection using cohesion based self merging algorithm

In this paper, we present segmentation of brain MRI for the purpose of determining the exact location of brain tumor using CSM based partitional K means clustering algorithm. CSM has attracted much attention as it gives efficient result as a self merging algorithm compared to other merging processes and the effect of noise is also less and the probability of obtaining the exact location of tumor is more. Our approach is much simpler and computationally less complex and computation time is very less.
Regulation of BDNF and trkB mRNA in rat brain by chronic electroconvulsive seizure and antidepressant drug treatments

The influence of chronic electroconvulsive seizure (ECS) or antidepressant drug treatments on expression of brain-derived neurotrophic factor (BDNF) and its receptor, trkB, was examined by in situ hybridization and Northern blot. In frontal cortex, acute ECS increased BDNF mRNA approximately twofold, an effect significantly augmented by a prior course of chronic ECS treatment (10 d). In the hippocampus, the influence of chronic ECS varied between the major subfields. In the dentate gyrus granule cell layer, chronic ECS decreased the acute induction of BDNF and trkB mRNA by approximately 50%, but prolonged their expression: levels remained elevated two- to threefold 18 hr later after the last chronic ECS treatment, but returned to control 18 hr after acute ECS. In CA3 and CA1 pyramidal cell layers, chronic ECS significantly elevated the acute induction of BDNF, and tended to prolong the expression of BDNF and trkB mRNA. A similar effect was observed in layer 2 of the piriform cortex, where chronic ECS significantly increased the acute induction and prolonged the expression of BDNF and trkB mRNA. Chronic (21 d), but not acute (1 d), administration of several different antidepressant drugs, including tranylcypromine, sertraline, desipramine, or mianserin, significantly increased BDNF mRNA and all but mianserin increased trkB mRNA in hippocampus. In contrast, chronic administration of nonantidepressant psychotropic drugs, including morphine, cocaine, or haloperidol, did not increase levels of BDNF mRNA. Furthermore, chronic administration of ECS or antidepressant drugs completely blocked the down-regulation of BDNF mRNA in the hippocampus in response to restraint stress. The enhanced induction and prolonged expression of BDNF in response to chronic ECS and antidepressant drug treatments could promote neuronal survival, and protect neurons from the damaging effects of stress.
Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle‐friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules—place recognition, landmark recognition, and road lane detection—supplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid‐occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real‐time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route.
Human-robot Collaboration for Bin-picking Tasks to Support Low-volume Assemblies

—In this paper, we present a framework to create hybrid cells that enable safe and efﬁcient human-robot collaboration (HRC) during industrial assembly tasks. We present our approach in the context of bin-picking, which is the ﬁrst task performed before products are assembled in certain low-volume production scenarios. We consider a representative one-robot one-human model in which a human and a robot asynchronously work toward assembling a product. The model exploits complimentary strengths of either agents: Whereas the robot performs bin-picking and subsequently assembles each picked-up part to build the product, the human assists the robot in critical situations by (1) resolving any perception and/or grasping problems encountered during bin-picking and (2) performing dexterous ﬁne manipulation tasks required during assembly. We explicate the design details of our overall framework comprising three modules: plan generation, system state monitoring, and contingency handling. We use illustrative examples to show different regimes where human-robot collaboration can occur while carrying out the bin-picking task.