Miracle-dz/UniFAR-FTUs huggingface dataset:
The End of Slow Networks: It's Time for a Redesign

The next generation of high-performance networks with remote direct memory access (RDMA) capabilities requires a fundamental rethinking of the design of distributed in-memory DBMSs. These systems are commonly built under the assumption that the network is the primary bottleneck and should be avoided at all costs, but this assumption no longer holds. For instance, with InfiniBand FDR 4×, the bandwidth available to transfer data across the network is in the same ballpark as the bandwidth of one memory channel. Moreover, RDMA transfer latencies continue to rapidly improve as well. In this paper, we first argue that traditional distributed DBMS architectures cannot take full advantage of high-performance networks and suggest a new architecture to address this problem. Then, we discuss initial results from a prototype implementation of our proposed architecture for OLTP and OLAP, showing remarkable performance improvements over existing designs.
On geographic coordinates of PlanetLab Europe

PlanetLab is a global IP research network of computers located across the world. The primary aim of PlanetLab is to support evaluation and deployment of new IP communication technologies. Due to its global scale, PlanetLab is used as an experimental testbed for a number of geographically oriented research. Therefore, the geographical properties of the PlanetLab nodes are of significant importance for correct and trustworthy results of the experiments. In the paper, we analyse the geographical properties of PlanetLab Europe, which is the European part of the PlanetLab global network. It describes different methods used for estimating the location information correctness provided for the PlanetLab nodes. The results show that the nodes' location is commonly burdened with an error varying from units to hundreds of kilometres. We propose a location uncertainty index to assist the PlanetLab users with a proper selection of the nodes for their experiments. The location index allows a selection of the nodes for the experiments requiring diverse input location accuracy data. We used the proposed index to evaluate the location accuracy of the PlanetLab Europe nodes. We estimate that below 1 % of the active PlanetLab Europe nodes have a maximum location error less than 1 km, 42 % less than 5 km, 60 % less than 10 km, and 85 % less than 100 km.
HRF-Interacting Molecules

Histamine-releasing factor (HRF), also termed translationally controlled tumor protein (TCTP) and fortilin, is a highly conserved, multi-functional protein. This protein within a cell plays a critical role in the fundamental processes of cell-cycle progression, proliferation, survival, and malignant transformation. The same protein, despite the lack of signal sequence, is secreted through a nonclassical secretory pathway. The secreted protein usually termed HRF can activate IgE-primed basophils and mast cells, and works as a B cell growth factor and a chemoattractant for eosinophils. This structurally well-characterized protein interacts with many proteins to perform its intracellular and extracellular functions. This review summarizes recent studies of HRF/TCTP-interacting proteins as a major driving force to decipher its functions.
Large scale training methods for linear RankRLS

RankRLS is a recently proposed state-of-the-art method for learning ranking functions by minimizing a pairwise ranking error. The method can be trained by solving a system of linear equations. In this work, we investigate the use of conjugate gradient and regularization by iteration for linear RankRLS training on very large and high dimensional, but sparse data sets. Such data is typically encountered for example in applications where natural language based data is used. We show that even though a pairwise loss function is optimized when training RankRLS, the computational complexity of the proposed methods, when learning from data with utility scores, is O ( tms ), where t is the required number of iterations, m the number of training examples and s the average number of non-zero features per example. In addition, the complexity of learning from pairwise preferences is O ( tms + tl ), where l is the number of observed preferences in the training set. In the experiments, it is further conﬁrmed that restricting the number of conjugate gradient iterations has a regularizing eﬀect and that the number of iterations that provides optimal results is, in practice, a small constant. Thus, the use of regularization by iteration, while providing similar performance as the more well-known Tikhonov regularization, results in a tremendous reduction in the computational cost of training and parameter selection.
Inflation Targeting and Output Stabilisation

Inflation targeting has been criticised for being ‘inflation only’ targeting and hence, for ignoring output and employment. This paper argues that this criticism is misplaced. The inflation-targeting framework has sufficient flexibility to allow for the short-run trade-off between output and inflation. The extent to which it does so in part reflects some design features of the inflation-targeting framework, such as targeting bands and the policy horizon, that have been adopted in practice in the inflation-targeting countries. Medium-term price stability can be maintained while still allowing some degree of short-run inflation variability, thus providing scope for lower output variability.
Indexing Multi-Dimensional Data with Missing Values April 2005

Advanced analytical studies are usually conducted on data with many dimensions. However, the large number of attributes associated with each data object naturally leads to situations where not all values are available. This paper presents a novel solution to the problem of retrieving multi-dimensional data with missing values based on region queries. The key aspect of the solution is that it e ectively makes use of available information. In addition, its worstcase behavior is not worse than that of the underling indexing technique, and it can support e ective prediction of query performance over incomplete data. While the solution is studied on a new indexing technique, it is a general solution that can be adopted by a broad range of multidimensional access methods. A comprehensive experimental study was conducted on real and synthetic data to monitor the eÆciency and e ectiveness of the technique. The study reveals that, when the incomplete data has sizable amount of available information, the technique outperforms sequential scan by a signi cant margin. Moreover, the indicators of both eÆciency and e ectiveness are comparable to those achieved on complete data.
AM Auto-Calibration of Hydrological Models Using High Performance Computing

Hydrological models have been increasing in complexity over the years. These models rely on their calibration to simulate real world conditions as close as possible. Calibration is a tedious and time-consuming process. An auto-calibration algorithm (SCE-UA) developed by Duan et al. [1992], has been successfully used in hydrological modeling area. This is a serial algorithm and as complexity of the models to be calibrated increases the computational cost, also significantly increases. In this study, a parallel version of the algorithm developed is used for testing of two simple hydrological models. The results show that parallel version of the algorithm can be successfully used to calibrate complex hydrological models.
Learning semantic scene models from observing activity in visual surveillance

This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a visual surveillance environment.
OCR Correction and Query Expansion for Retrieval on OCR Data -- CLARIT TREC-5 Confusion Track Report

In CLARIT TREC-5 confusion track experiments, they explored two techniques for improving retrieval performance over corrupted data : (1) OCR word error correction to improve OCR text accuracy, and (2) query expansion by adding query term variants found in the corrupted text. The OCR word correction technique is based on statistical word bigram modeling (Tong & Evans 1996). The variants of a query term are terms similar to the query term, as measured by the edit distance (Wagner 1974). While the official runs were based on the first approach, in the follow-up experiments they tested the second approach as well. In this report, they give a brief description of the OCR correction and query expansion techniques, and then discuss the results of the experiments
Development of a Bluetooth Access Point for One-Phone System

In this paper, we describe a development of a Bluetooth Access Point for the WAN connection of home network devices. Especially, users can access the PSTN at home instead of expensive digital cellular network through the AP, using the `one-phone', which is the Bluetooth enabled cellular phone. The simultaneous benchmark test about telephone service and LAN access service shows the perfect compatibility and reasonable performance of the access point. The one-phone service can become a convergence of wired and wireless communication through the AP.
Development of a collaborative learning space for student generated-question strategy

The potential of the student-generated question (SGQ) strategy on enhancing students' learning has been evidenced. However, the SGQ task may overload students, thus, its educational benefits may be reduced. Therefore, this study, grounded on the collaborative learning theory, proposed a design of collaborative SGQ to avoid students being overloaded by the SGQ task. Specifically, a web-based collaborative SGQ work-space was developed to engage students in the activity of composing questions collaboratively. The user experience testing was conducted using qualitative research approach with supporting quantitative data. The results supported its design and perceived benefits in enhancing the quality of the question. Recommendation on future research is provided.
Time-Bounded Reachability in Continuous-Time Markov Decision Processes ⋆

This paper solves the problem of computing the maximum and minimum probability to reach a set of goal states within a given time bound for locally uniform continuous-time Markov decision processes (CTMDPs). As this model allows for nondeterministic choices between exponentially delayed transitions, we define total time positional (TTP) schedulers which rely on the CTMDP’s current state and the total elapsed time when taking a decision. In this paper, TTP schedulers are proved to be sufficient to maximize timed reachability even w.r. t. fully timeand history-dependent schedulers; further, they allow us to derive a fixed point characterization of the optimal timed-reachability probability. The main contribution of this paper is a discretization technique which, for an a priori given error bound ε, induces a discrete-time MDP that approximates the optimal timed reachability probability in the underlying CTMDP up to ε.
Temporal data classification and rule extraction using a probabilistic decision tree

Temporal data classification is an evolving area in machine learning and data mining in which time is included in learning procedure. In some real domains, observations are recorded on a time basis, so that there is a time sequence among the observation records. In this study, to make use of this temporal sequence, a procedure called temporalisation is applied to merge consecutive records. The learning algorithm is an entropy-based decision tree integrated with temporal decision tree concept. Furthermore, a probabilistic approach based on Bayes' theorem is applied to enhance prediction accuracy. The proposed temporal classifier is evaluated with three real datasets. It achieves better prediction results than an ordinary decision tree and produces temporal decision rules or temporal relationships.
Unsupervised learning of supervoxel embeddings for video Segmentation

We present an algorithm for learning a feature representation for video segmentation. Standard video segmentation algorithms utilize similarity measurements in order to group related pixels. The contribution of our paper is an unsupervised method for learning the feature representation used for this similarity. The feature representation is defined over video supervoxels. An embedding framework learns a feature mapping for supervoxels in an unsupervised fashion such that supervoxels with similar context have similar embeddings. Based on the learned representation, we can merge similar supervoxels into spatio-temporal segments. Experimental results demonstrate the effectiveness of this learned supervoxel embedding on standard benchmark data.
Gesture based dialogue management using behavior network for flexibility of human robot interaction

The usage of robots becomes more sophisticated, direct communication by means of human language is required to increase the efficiency of their performance. However, the dialogue systems that reply to the user with a set of predefined answers tend to be static. In this paper, we propose a gesture based dialogue system using behavior network for flexibility of human robot interaction. Gestures take an important part of interactions. By using gestures in dialogues, it could support a flexible and realistic interaction with humans. We confirm the usability of gestures through several scenarios and SUS subject test.
A Generic Concept for the Implementation of Medical Image Retrieval Systems

This paper presents a technical framework to support the development and installation of system for content-based image retrieval in medical applications (IRMA). A strict separation of feature extraction, feature storage, feature comparison, and the user interfaces is suggested. This allows to reuse implemented components in different retrieval algorithms, which improves software quality, shortens the development cycle for applications, and allows to introduce standardized end-user interfaces. Based on the proposed framework, the IRMA engine has been established, which is currently used to evaluate content-based retrieval methods on a collection of 20,000 medical and 135,000 non-medical images.
On the Use of Median String for Multi-source Translation

State-of-the-art approaches to multi-source translation involve a multimodal-like process which applies an individual translation system to each source language. Then, the translations of the individual systems are combined to obtain a consensus output. We propose to use the (generalised) median string as the consensus output of the individual translation systems. Different approximations to the median string are studied as well as different approaches to improve the median string performance when dealing with natural language strings. The proposed approaches were evaluated on the Europarl corpus, achieving significant improvements in translation quality.
Bare-hand 3D gesture input to interactive systems

Although the 2D desktop metaphor has been the dominating paradigm of user interfaces for over two decades, 3D models of interaction are becoming more feasible due to advances in computer output hardware and software technology. However, conventional input devices such as a mouse or track-pad generally restrict direct manipulation interaction to a 2D paradigm. More sophisticated 3D input devices such data-gloves have been available for some time, but these tend to be expensive or restrictive in their use. In this paper we describe a simple and inexpensive single camera-based video input system which allows 3D interaction with existing computer application using bare hands.
Total Variation-Based Image Noise Reduction With Generalized Fidelity Function

In this letter, we analyze the relationship between the change in the intensity value and the scale of an image feature, when a generalized function is used as the fidelity term in the total variation-based noise removal scheme. Based on the analysis, we propose a designing method of the fidelity function that results in any desired monotonic relationship between the intensity change and the scale. As an example, we designed a fidelity function that results in a larger contrast between the intensity change of a small scaled feature and that of a large scaled one than the original total variation-based noise removal scheme that uses the norm as the fidelity function.
Moving Region Segmentation From Compressed Video Using Global Motion Estimation and Markov Random Fields

In this paper, we propose an unsupervised segmentation algorithm for extracting moving regions from compressed video using global motion estimation (GME) and Markov random field (MRF) classification. First, motion vectors (MVs) are compensated from global motion and quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its advantages over state-of-the-art methods.
Temporal salient graph for sports event detection

A graphic representation is proposed to facilitate the analysis of temporal saliency in a video document. This representation visualizes temporal saliency at multiple resolutions and introduces efficient image operations to the computationally intensive task of saliency analysis. Algorithms for graphic-based saliency fusion and attended area detection are presented. We evaluate the effectiveness by the application of general highlight detection in football videos. The experimental collection includes six full games from FIFA World Cup and European Champion.
The Use of the Cytokines EMAP-II, IL-19 and IL-10 as Biomarkers to Determine Prognosis of Non – Hodgkin’s Lymphoma

Objectives: Establishing diagnostic and prognostic factors are very important in the management of Non-Hodgkin Lymphoma (NHL). Our aim was to evaluate the clinical significance of serum EMAP-II, IL-19, and IL–10 in NHL patients for assessing treatment response and prognosis of patients. Methods: Serum EMAP-II, IL-19, and IL-10 levels were measured in the serum of 64 NHL patients before and after treatment with CHOP-based chemotherapy by enzyme-linked immunosorbent assay. Correlations of marker levels to the laboratory, clinicopathological and immunophenotyping markers were performed. Results: Serum levels of EMAP-II and IL-10 were higher before therapy and decreased significantly thereafter (P<0.001). High EMAP-II and IL-10 were correlated with serum ALT and blood urea respectively (P=0.043, P=0.020). Significantly higher levels of IL-19 were demonstrated in patients with relapse (P<0.001). A significant association was found between serum IL-19 and AST, CD23 and B-cl2 (P=0.032, P=0.015, P=0.024 respectively). There was a significant correlation between EMAP-II and IL-10 in NHL patients (r=0.827, P<0.001). Conclusion: EMAP-II, IL-19, and IL-10 can all serve as useful diagnostic markers in NHL patients. They assessed response to therapy. IL-19 proved to be the most sensitive predictor of advanced disease and poor prognosis.
A Downward Trend of the Ratio of Influenza RNA Copy Number to Infectious Viral Titer in Hospitalized Influenza A-Infected Patients

Background. Efficacy endpoints in influenza clinical trials may include clinical symptoms and virological measurements, although virology cannot serve as the primary endpoint. We investigated the relationship between influenza A RNA copy number and quantity of infectious viruses in hospitalized influenza patients. Methods. One hundred fifty influenza-infected, hospitalized patients were included in this prospective cohort study spanning the 2012–2013 influenza season. Daily nasopharyngeal samples were collected during hospitalization, and influenza A RNA copy number and infectious viral titer were monitored. Results. The decay rate for 50% tissue culture infectious dose (TCID50) was 0.51 ± 0.14 log10 TCID50/mL per day, whereas the RNA copy number decreased at a rate of 0.41 ± 0.04 log10 copies/mL per day (n = 433). The log ratio of the RNA copy number to the infectious viral titer within patient changes significantly with −0.25 ± 0.09 units per day (P = .0069). For a 12-day observation period, the decay corresponds to a decline of this ratio of 3 log influenza RNA copies. Conclusions. Influenza RNA copy number in nasal swabs is co-linear with culture, although the rate of decay of cell culture-based viral titers was faster than that observed with molecular methods. The study documented a clear decreasing log ratio of the RNA copy number to the infectious viral titer of the patients over time.
4 How to Verify Your Python Conversations

In large-scale distributed systems, each application is realised through interactions among distributed components. To guarantee safe communication (no deadlocks and communication mismatches) we need programming languages and tools that structure, manage, and policy-check these interactions. Multiparty session types (MSPT), a typing discipline for structured interactions between communicating processes, offers a promising approach. To date, however, session types applications have been limited to static verification, which is not always feasible and is often restrictive in terms of programming API and specifying policies. This chapter investigates the design and implementation of a runtime verification framework, ensuring conformance between programs and specifications. Specifications are written in Scribble, a protocol description language formally founded on MPST. The central idea of the approach is a runtime monitor, which takes a form of a communicating finite state machine, automatically generated from Scribble specifications, and a communication runtime stipulating a message format. We demonstrate Scribble-based runtime verification in manifold ways. First, we present a Python library, facilitated with session primitives and verification runtime. Second, we show examples from a large cyber-infrastructure project for oceanography, which uses the library as a communication medium. Third, we examine communication patterns, featuring advanced Scribble primitives for verification of exception handling behaviours.