Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape-Based Registration of Kidneys Across Differently Contrasted CT Scans

We present a method to register kidneys from Computed Tomography (CT) scans with and without contrast enhancement. The method builds a patient-specific kidney shape model from the contrast enhanced image, and then matches it against automatically segmented candidate surfaces extracted from the pre-contrast image to find the alignment. Only the object of interest is used to drive the alignment, providing results that are robust to near-rigid relative motions of the kidney with respect to the surrounding tissues. Shape-based features are used, as opposed to intensity-based ones, and consequently the resulting registration is invariant to the inherent contrast variations. The contributions of this work are: a surface grouping and segmentation algorithm driven by smooth curvature constraints, and a framework to register image volumes under contrast variation, relative motion and local deformation with minimal user intervention. Encouraging experimental results with real patient images, all with various kinds and sizes of kidney lesions, validate the approach.
Activity and Cross-Reactivity of Antibodies Induced in Mice by Immunization with a Group B Meningococcal Conjugate

ABSTRACT The capsular polysaccharide of group BNeisseriameningitidis is composed of a linear homopolymer of α(2-8) N-acetyl neuraminic acid or polysialic acid (PSA) that is also carried by isoforms of the mammalian neural cell adhesion molecule (NCAM), which is especially expressed on brain cells during development. Here we analyzed the ability of antibodies induced by the candidate vaccineN-propionyl polysaccharide tetanus toxoid conjugate to recognize PSA-NCAM. We hyperimmunized mice to produce a pool of antisera and a series of immunoglobulin G monoclonal antibodies and evaluated their self-reactivity profile by using a battery of tests (immunoprecipitation, immunoblotting, and immunofluorescence detection on live cells and human tissue sections) chosen for their sensitivity and specificity to detect PSA-NCAM in various environments. We also searched for the effects of the vaccine-induced antibodies in two functional assays involving cell lysis or cell migration. Although they were highly bactericidal, all the antibodies tested showed very low or no recognition of PSA-NCAM, in contrast to PSA-specific monoclonal antibodies used as controls. Different patterns of cross-reactions were revealed by the tests used, likely due to affinity and specificity differences among the populations of induced antibodies. Furthermore, neither cell lysis nor perturbation of migration was observed in the presence of the tested antibodies. Importantly, we showed that whereas enzymatic removal of PSA groups from the surfaces of live cells perturbed their migration, blocking them with PSA-specific antibodies was not functionally detrimental. Taken together, our data indicated that this candidate vaccine induced antibodies that could not demonstrate an immunopathologic effect.
A Simple and Scalable Force Actuator

Abstract : One problem posed by robots that must perform in unstructured environments and interact with humans is that of achieving high fidelity force control as well as compliance in a compact form factor. Force control, as opposed to only position control, allows for safe, graceful contact behavior. Having spring elements placed in series between an actuator and its output provides this desired compliance. It also provides low mechanical impedance, precise force sensing capability, and good control bandwidth, as demonstrated by Series Elastic Actuators (SEA). There have been both linear and rotary versions of SEAs, each with their own advantages and shortcomings. However, neither type of these actuators is easily miniaturized for joint mounting; linear SEAs require ball screws while conventional rotary ones require custom-made torsional springs. The aim of this work was to integrate the positive features of an SEA into a compact, easy-to-build rotary actuator. This new actuator design involves two opposing linear springs coupled to a rotary shaft. A modified potentiometer relays position information linearly proportional to spring deflection and thus applied force. The mechanism is intentionally fabricated in layers to achieve precompression/extension of the springs while maintaining construction simplicity. The result is a completely encased and scalable module made of inexpensive off-the-shelf components.
Channel based sequential simulation

Sequential discrete event simulation is widely employed to study the behavior of many systems. Events are typically managed in a central event list which is implemented as a priority queue ordered by event timestamps. Most research to improve sequential simulation performance has focused on improving the priority queue implementations. Recent work has demonstrated that asynchronous conservative parallel discrete event simulation systems can achieve better sequential performance under some conditions, but worse performance under other conditions. This paper introduces a new sequential discrete event simulation algorithm that can exhibit some of the same performance advantages of asynchronous conservative parallel discrete event simulation algorithms and has complexity no more than that of central event list algorithms in the worst case.
LTE physical layer implementation using GPU based high performance computing

In recent years Graphics Processing Unit (GPU) has evolved as a high performance data processing technology allowing users to compute large blocks of parallel data using an array of low complexity processors. This paper proposes the implementation of compute intensive portions of 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) physical layer using GPU. LTE employs Orthogonal Frequency Division Multiple Access (OFDMA) in downlink and Single Carrier Frequency Division Multiple Access (SC-FDMA) in uplink. Both these demand computationally complex Inverse Fast Fourier Transform (IFFT) and Fast Fourier Transform (FFT) processing at the transmitter and the receiver. The computational requirements at the base station increases significantly with the increase in number of users. This paper presents a simulation model utilizing the massively parallel architecture of GPU to reduce computation time of IFFT and FFT operations. Simulation results demonstrate that GPU provides a framework for fast data processing in this application.
Holistic recommender systems for software engineering

Software maintenance is a relevant and expensive phase of the software development process. Developers have to deal with legacy and undocumented code that hinders the comprehension of the software system at hand. Enhancing program comprehension by means of recommender systems in the Integrated Development Environment (IDE) is a solution to assist developers in these tasks. The recommender systems proposed so far generally share common weaknesses: they are not proactive, they consider a single type of data-source, and in case of multiple data-source, relevant items are suggested together without considering interactions among them. We envision a future where recommender systems follow a holistic approach: They provide knowledge regarding a programming context by considering information beyond the one provided by single elements in the context of the software development. The recommender system should consider different elements such as development artifact (e.g., bug reports, mailing lists), and online resources (e.g., blogs, Q&A web sites, API documentation), developers activities, repository history etc. The provided information should be novel and emerge from the semantic links created by the analysis of the interactions among these elements.
New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering

Collaborative filtering (CF) systems help address information overload, by using the preferences of users in a community to make personal recommendations for other users. The widespread use of these systems has exposed some well‐known limitations, such as sparsity, scalability, and cold‐start, which can lead to poor recommendations. During the last years, a great number of works have focused on the improvement of CF, but they do not solve all its problems efficiently. In this article, we present a new approach that applies semantic similarity fusion as well as biclustering to alleviate the aforementioned problems. The experimental results verify the effectiveness and efficiency of our approach over the benchmark CF methods.
Efficiency of the monofunctionalized C60 fullerenes as membrane targeting agents studied by all-atom molecular dynamics simulations

Abstract Transmembrane translocation of C60 fullerenes functionalized by the single amino-derivative in neutral and charged forms was studies by extensive all-atom molecular dynamics simulations. It is shown that these complexes exhibit very strong affinity to the membrane core, but their spontaneous translocation through the membrane is not possible at practical time scale. In contrast, free amino derivatives translocate through the membrane much easier than their complexes with fullerenes, but do not have pronounced affinity to the membrane interior. Our results suggest that monofunctionalized C60 could be extremely efficient membrane targeting agents, which facilitate accumulation of the water-soluble compounds in the hydrophobic core of lipid bilayer.
Production And Quality Evaluation Of Biscuit Incorporated With Fish Fillet Protein Concentrate

This research was aimed to evaluate the physico-chemical and nutritional qualities of biscuit produced from sturgeon fillet protein concentrate (SFPC), based on gross chemical composition, effects of SFPC incorporation, and storage stability for six months. SFPC was used in production of biscuits by replacing low gluten wheat flour by 5%, 7% and 10% to ensure the quality and acceptability of the biscuit. The obtained results for biscuits fortified with SFPC revealed that the nutritional and proximate composition of biscuits were significantly (P ≤ 0.05) improved: moisture (4.75 ± 0.08-4.76 ± 0.11), protein (14.63 ± 0.12-19.52 ± 0.12), fat (16.20 ± 0.06-16.50 ± 0.17), ash (1.53 ± 0.04-1.66 ± 0.12). Total amino acids were: 6.93, 13.15, 13.93, and 17.20; and essential amino acids: 2.43, 5.29, 5.87, and 7.48 gram/100 gram, for 0%, 5%, 7% and 10% SFPC, respectively. Leucine was the major amino acid in 5% and 7% SFP, while Leucine, Phenylalanine and lysine were the major amino acid in 10% SFPC. The obtained results also showed the produced fish biscuit and SFPC had a good physico-chemical quality. In addition that fish biscuit protein characterized with a good nutritional protein quality as it composed of all essential amino acids. Microbial and physico- chemical results revealed that the shelf life of biscuit supplemented with SFPC were more than 6 months. The obtained results also showed the supplementation of SFPC into the wheat flour mix ratio used for making biscuits at level up to 10% improved the most nutritional content, rheological properties of prepared biscuits dough batches and physical, and sensory quality characteristics of the final biscuit products. Therefore, the present results recommended that it should be directed towards the utilization of SFPC products in food products fortification, especially cereal products up to the concentration of 7-10% depending upon the fortified food product.
Edge correlations in spatial networks

In this paper, we show that many real-world spatial networks—including demographic migration networks, social networks and human brain networks—possess ‘spatial edge correlations’ that profoundly affect the network’s underlying structure but which go largely undetected by standard network analysis methods. Even methods designed to characterize the spatial attributes like distribution of edge lengths in a network or the costliness of edge lengths (i.e. long-distance edges tend to be more costly in terms of efficiency, energy, price, etc. than shorter edges) miss the presence of spatial edge correlations. We introduce two network measures designed to detect and quantify these edge correlations, and discuss their implications. We show that even highly simplified models that explicitly take account of these edge correlations can achieve dramatic (potentially order-of-magnitude) improvements over standard network models. We demonstrate the utility of this approach by explaining certain anomalies seen in newly available, highresolution human brain networks. The methods introduced here are very general, with potentially broad applicability to spatial networks across diverse branches of network science.
The Fetal Liver Counterpart of Adult Common Lymphoid Progenitors Gives Rise to All Lymphoid Lineages, CD45+CD4+CD3− Cells, As Well As Macrophages1

We identified an IL-7Rα+Sca-1lowc-Kitlow population in E14 fetal liver, which is the phenotypical analog of common lymphoid progenitors (CLP) in adult bone marrow. After transfer into newborn mice, the IL-7Rα+Sca-1lowc-Kitlow population rapidly differentiated into CD45+CD4+CD3− cells, which are candidate cells for initiating lymph node and Peyer’s patch formation. In addition, this population also gave rise to B, T, NK, and CD8α+ and CD8α− dendritic cells. The fetal liver precursors expressed a significantly lower level of the myeloid-suppressing transcription factor Pax-5, than adult CLP, and retained differentiation activity for macrophages in vitro. We propose that the transition from fetal liver IL-7Rα+Sca-1lowc-Kitlow cells to adult CLP involves a regulated restriction of their developmental potential, controlled, at least in part, by Pax-5 expression.
CLTCP: An Adaptive TCP Congestion Control Algorithm Based on Congestion Level

The Transmission Control Protocol (TCP) has been profusely used by diverse applications such as FTP, email, and HTTP. In recent decades, numerous TCP variants have been developed to fit the fast increasing of network capacities and improve the performance of TCP algorithms in various scenarios, particularly in the high-bandwidth-delay-product (high-BDP) and lossy networks. Although the performance improvements are different for different TCP under different networking conditions, implementing a new congestion control algorithm that is suitable for a wide range of network conditions is still a challenge. In this letter, we propose a novel scheme, i.e., the congestion-level-based TCP (CLTCP), which could perform effectively in both high-BDP and lossy networks. Different from the TCP-FIT, the CLTCP does not use the delay variation but the congestion level as a signal to control the number of virtual parallel flows in a TCP connection. This could avoid the influence of the delay measurement error in the TCP-FIT. Extensive experiments in NS-2 show that the performance of the CLTCP is significantly improved, as compared to other state-of-the-art algorithms, while maintaining good fairness.
Optical Code Division Multiple Access Network Transmission With M-ary Chip Symbols

This paper uses M-ary chip symbols to increase the spectral efficiency of optical code division multiple access (OCDMA) even though, when compared to its binary counterpart, M-ary OCDMA can have significantly increased multi-user interference (MUI). The presented numerical channel capacity results show that appropriate non-uniform M-ary signaling allows the reduction of MUI and the achievement of improved OCDMA spectral efficiency. Using insights from the OCDMA capacity results, we design a coded M-ary OCDMA transmission scheme that can achieve very high spectral efficiencies. The proposed time/wavelength OCDMA scheme relies on intensity modulation, appropriate non-uniform signaling, M-ary trellis-coded modulation and error-correcting codes. The corresponding receiver is based on direct detection of optical signals, soft-decision single-user demodulation and error-control decoding. The presented simulation results illustrate that the designed OCDMA scheme can support hundreds of active users at the target bit error rate of 10-9 and is robust to impairments encountered in optical transmission (shot noise, thermal noise). The achieved spectral efficiency of up to 1.34 bits per OCDMA chip is significantly better than the best binary OCDMA result reported in the literature.
DESIGN OF A CLOSED LOOP CONTROL OF THE BOOST CONVERTER (AVERAGE MODEL)

This paper addresses the design of closed loop control of the boost converter based on specific criteria given. The closed loop boost converter is used to convert a low level DC input voltage from a DC power supply and use to control the average model. The average model is useful when investigating the dynamic behavior of the converter when it is subject to changes in operating parameters. The design methodology will comprise of hand calculation that involves theory and simulation. The simulation carried out in Pspice software. The Performance analysis, which covers the closed loop control of the average model on related waveforms of output voltage, current and power are discussed and achieved.
What Makes APIs Hard to Learn? The Answers of Developers

Most software projects reuse components exposed through Application Programming Interfaces (APIs), which provide developers with access to implemented functionality. APIs have grown large and diverse, raising the question of how usable they really are. This article reports on a study of the obstacles professional developers at Microsoft faced when learning how to use APIs. The study was grounded in developers' experience, collected through a survey and interviews. The resulting data showed that resources for learning an API were critically important, and shed light on three API learning obstacles: The need discover the design and rationale of the API on a need-to-know basis, the challenge of finding credible usage API examples at the right level of complexity, and the challenge of understanding inexplicable API behavior. The article describes each of these challenges in detail, and discusses associated implications for API users and designers.
Assembling biological boolean networks using manually curated databases and prediction algorithms

Despite the large quantity of information available, thorough researches in various biological databases are still needed in order to reconstruct and understand the steps that lead to known or new phenomena. By using protein-protein interaction networks and algorithms to extract relevant interconnections among proteins of interest, it is possible to assemble subnetworks from global interactomes. Using these extracted networks it is possible to use algorithms to predict signal directions while activation and inhibition effects can be predicted using RNA interference screenings. The result of this approach is the automatic generation of boolean networks. This way of modelling dynamical systems allows the discovery of steady states and the prediction of stimuli response.
Using Google distance to weight approximate ontology matches

Discovering mappings between concept hierarchies is widely regarded as one of the hardest and most urgent problems facing the Semantic Web. The problem is even harder in domains where concepts are inherently vague and ill-defined, and cannot be given a crisp definition. A notion of approximate concept mapping is required in such domains, but until now, no such notion is vailable. The first contribution of this paper is a definition for approximate mappings between concepts. Roughly, a mapping between two concepts is decomposed into a number of submappings, and a sloppiness value determines the fraction of these submappings that can be ignored when establishing the mapping. A potential problem of such a definition is that with an increasing sloppiness value, it will gradually allow mappings between any two arbitrary concepts. To improve on this trivial behaviour, we need to design a heuristic weighting which minimises the sloppiness required to conclude desirable matches, but at the same time maximises the sloppiness required to conclude undesirable matches. The second contribution of this paper is to show that a Google based similarity measure has exactly these desirable properties. We establish these results by experimental validation in the domain of musical genres. We show that this domain does suffer from ill-defined concepts. We take two real-life genre hierarchies from the Web, we compute approximate mappings between them at varying levels of sloppiness, and we validate our results against a handcrafted Gold Standard. Our method makes use of the huge amount of knowledge that is implicit in the current Web, and exploits this knowledge as a heuristic for establishing approximate mappings between ill-defined concepts.
Physical Structure Segmentation with Projection Profile for Mathematic Formulae and Graphics in Academic Paper Images

Image segmentation is an essential topic in a document processing system, and also is an important initial task for higher level image processing such as recognition or object tracking since recent years. In this paper we propose a method for segmentation and extraction of the regions of mathematical formulae and graphics in an academic paper. First, an academic paper image is transformed into a binary image by Ohtsu's method. Second, the boundary blank spaces of the binary image are removed and then the image is recursively segmented and extracted by vertical and horizontal projection profiles. We apply the proposed method to 150 academic paper images, and show that the method is adaptable, robust and effective for segmentation.
On-board vision-based 3D relative localization system for multiple quadrotors

This work proposes a novel relative localization system, based on active markers and an on-board camera, for tracking multiple quadrotors in a limited field of view. The system extracts the 3D poses of the markers including one that, by pulsating at a predefined frequency, provides an unique platform ID. We discuss how the camera field of view can be explored in presence of multiple targets, and what are the conditions on the system visibility that lead to the establishment of bidirectional sensing between robots with similar sensing capabilities. A visibility analysis is conducted to show that the developed relative localization system meets such requirements, and a closed-loop experiment is used to validate its performance under these conditions. Finally, its performance is compared with other results from the literature, and a metric is established with the intent of mapping different design solutions, facilitating design choices in presence of different requirements.
Adaptive Distributed Randomized TDMA Scheduling For Clustered Wireless Sensor Networks

This paper presents an adaptive distributed TDMA slot assignment algorithm, called A-DRAND, which is an improved version of DRAM) in clustered wireless sensor networks where cluster heads need more slots and will be alternated afterwards by other cluster members for energy balance reason. It utilizes cluster info to allocate slots discriminately for different kind of sensor node and adapts its operation depending on the requirements of the application. The overhead is reduced significantly by reserving a special slot number range for slot reassignment. Simulation results show that running time and transmission cost of slot reassignment is only at most 1/m of that of all-participating assignment, where m is the proportion of cluster heads and other sensors with changing slots demanding in the network.