Miracle-dz/UniFAR-FTUs huggingface dataset:
Eukaryotic translation initiation factor 5A2 promotes metabolic reprogramming in hepatocellular carcinoma cells

Reprogramming of intracellular metabolism is common in liver cancer cells. Understanding the mechanisms of cell metabolic reprogramming may present a new basis for liver cancer treatment. In our previous study, we reported that a novel oncogene eukaryotic translation initiation factor 5A2 (EIF5A2) promotes tumorigenesis under hypoxic condition. Here, we aim to investigate the role of EIF5A2 in cell metabolic reprogramming during hepatocellular carcinoma (HCC) development. In this study, we reported that the messenger RNA (mRNA) level of EIF5A2 was upregulated in 59 of 105 (56.2%) HCC clinical samples (P = 0.015), and EIF5A2 overexpression was significantly associated with shorter survival time of patients with HCC (P = 0.021). Ectopic expression of EIF5A2 in HCC cell lines significantly promoted cell growth and accelerated glucose utilization and lipogenesis rates. The high rates of glucose uptake and lactate secretion conferred by EIF5A2 revealed an abnormal activity of aerobic glycolysis in HCC cells. Several key enzymes involved in glycolysis including glucose transporter type 1 and 2, hexokinase 2, phosphofructokinase liver type, glyceraldehyde 3-phosphate dehydrogenase, pyruvate kinase M2 isoform, phosphoglycerate mutase 1 and lactate dehydrogenase A were upregulated by overexpression of EIF5A2. Moreover, EIF5A2 showed positive correlations with FASN and ACSS2, two key enzymes involved in the fatty acid de novo biosynthetic pathway, at both protein and mRNA levels in HCC. These results indicated that EIF5A2 may regulate fatty acid de novo biosynthesis by increasing the uptake of acetate. In conclusion, our findings demonstrate that EIF5A2 has a critical role in HCC cell metabolic reprogramming and may serve as a prominent novel therapeutic target for liver cancer treatment.
Study of cell wall growth in Bacillus megaterium by high-resolution autoradiography

Growth of the cell wall of Bacillus megaterium was studied by pulse-labeling the cell wall of a DAP- Lys- mutant for a very short time with tritium-labeled diaminopimelic acid. The distribution of radioactivity along the cell wall was examined by high-resolution autoradiography on isolated cell walls and thin sections of bacteria. The results indicate that cell wall elongation occurs by diffuse intercalation of newly synthesized murein into the expanding cell wall during exponential growth, as well as during germination, and that the only zone of highly localized diaminopimelic acid incorporation is found at the cross wall during its synthesis. This zone contains about 30% of the radioactivity incorporated into the cell wall. Analysis of autoradiographs of thin sections of bacteria shows that the total radioactivity incorporated per bacterium doubles during the life cycle. This doubling occurs in the cylindrical part of the cell wall but not in the polar caps. This seems to indicate that elongation of the bacterium is not constant during the life cycle but increases with the length of the cell.
Assessing the Quality of Flow Measurements from OpenFlow Devices

Since its initial proposal in 2008, OpenFlow has evolved to become today’s main enabler of Software-Defined Networking. OpenFlow specifies operations for network forwarding devices and a communication protocol between data and control planes. Although not primarily designed as a traffic measurement tool, many works have proposed to use measured data from OpenFlow to support, e.g., traffic engineering or security in OpenFlow-enabled networks. These works, however, generally do not question or address the quality of actual measured data obtained from OpenFlow devices. Therefore, in this paper we assess the quality of measurements in real OpenFlow devices from multiple vendors. We demonstrate that inconsistencies and measurement artifacts can be found due to particularities of different OpenFlow implementations, making it impractical to deploy an OpenFlow measurement-based approach in a network consisting of devices from multiple vendors. In addition, we show that the accuracy of measured packet and byte counts and duration for flows vary among the tested devices, and in some cases counters are not even implemented for the sake of forwarding performance.
A robust and efficient dynamic identity‐based multi‐server authentication scheme using smart cards

In single‐server architecture, one service is maintained by one server. If a user wants to employ multiple services from different servers, he/she needs to register with these servers and to memorize numerous pairs of identities and passwords corresponding to each server. In order to improve user convenience, many authentication schemes have been provided for multi‐server environment with the property of single registration. In 2013, Li et al. provided an efficient multi‐server authentication scheme, which they contended that it could resist several attacks. Nevertheless, we find that their scheme is sensitive to the forgery attack and has a design flaw. This paper presents a more secure dynamic identity‐based multi‐server authentication scheme in order to solve the problem in the scheme by Li et al. Analyses show that the proposed scheme can preclude several attacks and support the revocation of anonymity to handle the malicious behavior of a legal user. Furthermore, our proposed scheme has a lower computation and communication costs, which make it is more suitable for practical applications. Copyright © 2014 John Wiley & Sons, Ltd.
The Factors Influencing the Band Gap Bowing of III Nitride Alloys

In the paper, the factors influencing the band gap bowing of the bulk alloy with random distribution are analyzed and it is found that the bowing coefficient is compositional dependent. The reasons for the large dispersion of the bowing coefficient are also analyzed. In addition, we obtained the bowing coefficient (b=2.26+5.04x) for InAlN by fitting the experimental data.
Spatiotemporal segmentation and tracking of objects in color image sequences

In this paper a procedure is described for the segmentation and tracking of objects in color image sequences. For this purpose, we propose the novel procedure of K-Means with a connectivity constraint algorithm as a general segmentation algorithm combining several types of information including color, motion and compactness. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames is analyzed simultaneously and as a result the same region is present in consequent frames. Experimental results in real image sequences evaluate the performance of the algorithm.
Effect of nanorope waviness on the effective moduli of nanotube sheets

Using a micromechanics approach, we recently investigated the theoretical limits on achievable moduli in nanotube mats by stiffening of bonds. However, the waviness intrinsic to many manufacturing processes also clearly plays an important role in stiffness of these materials. To study the effect of waviness on mechanical properties, we modeled fiber segments as sinusoids, generated networks comprised of these fibers, and performed simulations of deformations of the networks. In contradiction of classical work by Kallmes and Corte [Tappi J. 43, 737 (1960)], we found the number of fiber crossings in these networks to be independent of fiber waviness, leading to identification of the number of fiber crossings as a necessary and sufficient parameter to specify network geometry, for either wavy or straight fibers. Our mechanical modeling results suggest that reducing the waviness of nanotube ropes would significantly improve Young’s moduli in these materials. However, reduction of waviness would not produce the improvements achievable with higher bond density; for random sheets, assuring connections among all intersecting ropes appears to be the most direct route toward improving the overall sheet properties. There remains a persistent discrepancy between statistically predicted bond densities and physical bond densities, based on moduli of these materials.
Effective Combination of Language and Vision Through Model Composition and the R-CCA Method

We address the problem of integrating textual and visual information in vector space models for word meaning representation. We first present the Residual CCA (R-CCA) method, that complements the standard CCA method by representing, for each modality, the difference between the original signal and the signal projected to the shared, max correlation, space. We then show that constructing visual and textual representations and then post-processing them through composition of common modeling motifs such as PCA, CCA, R-CCA and linear interpolation (a.k.a sequential modeling) yields high quality models. On five standard semantic benchmarks our sequential models outperform recent multimodal representation learning alternatives, including ones that rely on joint representation learning. For two of these benchmarks our R-CCA method is part of the Best configuration our algorithm yields.
A Recommendation System for Enhancing the Personalized Search Itineraries in the Public Transportation Domain

In traditional transport information systems, the users must explicitly provide the information related to both their profiles and travels to receive a personalized response. However, this requires, among others, an extra effort from user in term of search time. We aim to identify not only implicitly users’ information, but also to anticipate their need even if some data are missing through a recommender system based on collaborative filtering technique. In this work, the information related to users is represented using the ontology which proved far more adequate model for representing semantically data.
Recognition and re-synthesis of 3D human motion with personalized variations

This paper proposes a 3D view-invariant human action recognition method based on Hidden Markov Models. The natures of the actions, as well as the characteristics of the actors and different performance styles have been successfully recognized. The results have been compared to Nearest Neighbor and Similarity Search based recognition for further evaluation. Also the research addresses the problem of re-synthesis of motion. Transformation of moods, genders, and other characteristics of the actor have successfully been carried out, and the entire action has been re-synthesized for various purposes such as animation.
A Neurodynamical Approach to Visual Attention

The psychophysical evidence for "selective attention" originates mainly from visual search experiments. In this work, we formulate a hierarchical system of interconnected modules consisting in populations of neurons for modeling the underlying mechanisms involved in selective visual attention. We demonstrate that our neural system for visual search works across the visual field in parallel but due to the different intrinsic dynamics can show the two experimentally observed modes of visual attention, namely: the serial and the parallel search mode. In other words, neither explicit model of a focus of attention nor saliencies maps are used. The focus of attention appears as an emergent property of the dynamic behavior of the system. The neural population dynamics are handled in the framework of the mean-field approximation. Consequently, the whole process can be expressed as a system of coupled differential equations.
Structure and Tribotechnical Properties of Al-Sn Alloys Prepared by the Method of Liquid-Phase Sintering

The structure and tribotechnical properties of sintered Al-Sn alloys were studied. It was pointed that the sintering of pure metals powders allows save the skeleton structure of aluminium matrix at the enlarging concentration of tin up to 50% that twice higher its content in commercial alloys this system. It was established that the high Sn content in sintered alloys leads to their well wear resistance under the dry friction conditions, but it not has an influence on the value of friction coefficient.
Discriminative Scale Space Tracking

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.
A Multilevel Analysis of the Effect of Taxation Incentives on Innovation Performance

This study investigates how government taxation incentives stimulate R&D expenditures from the organizational field level and accordingly improve innovation performance. Our results, based on an examination of 54 Chinese High-Tech industrial parks in the period of 2002-2008, suggest that government taxation affects industrial parks' R&D expenditure in a U-shaped way over time. Although government taxation incentives have a direct impact on R&D expenditure, the R&D expenditure growth patterns are different for industrial parks with high tax incentives and low tax incentives. Moreover, instead of finding that R&D expenditure has a direct influence on innovation performance as indicated by the previous literature, our study shows that the effect is time-lagged. This paper augments the current literature by providing empirical evidence and a theoretical understanding of regional innovation motivation.
Mobility assisted device-to-device communications underlaying cellular networks

With the explosive growth of mobile data demands for local services, device-to-device (D2D) communication is proposed as a vital technology for next generation cellular network to enhance the transmission capacity. With the popularity of content-based mobile applications, popular content downloading can be optimized by allowing D2D communications to share the content with each other. Such improvements depend on effective and efficient solutions for the D2D technical problems of mode selection and resource allocation. Existing works dealing with these technical problems usually stand in the viewpoint of some network snapshots with fixed topology of static mobile users to optimization the instantaneous resource utilization. In this paper, from an angle of dynamic networks, we consider the mode selection and resource allocation problems for D2D communication underlaying cellular network with opportunistic change of users' location by fully exploiting their mobility patterns to achieve global optimization. By formulating the above process-oriented optimization problems, we introduce a utility function and contention graph based approaches, termed MAD2D (Mobility assisted D2D), to heuristically solve them effectively. Through extensive simulations, we demonstrate MAD2D greatly enhanced the system performance compared with the state-of-the-art solutions.
Exploring Word Embeddings and Character N-Grams for Author Clustering

We presented our system for PAN 2016 Author Clustering task. Our software used simple character n-grams to represent the document collection. We then ran K-Means clustering optimized using the Silhouette Coefficient. Our system yields competitive results and required only a short runtime. Character n-grams can capture a wide range of information, making them effective for authorship attribution. We also present a comparison study of two different features: character n-grams and word embeddings.
IMPLICIT SCHEMES AND PARALLEL COMPUTING IN UNSTRUCTURED GRID CFD

The development of implicit schemes for obtaining steady state solutions to the Euler and Navier-Stokes equations on unstructured grids is outlined. Applications are presented that compare the convergence characteristics of various implicit methods. Next, the development of explicit and implicit schemes to compute unsteady flows on unstructured grids is discussed. Next, the issues involved in parallelizing finite volume schemes on unstructured meshes in an MIMD (multiple instruction/multiple data stream) fashion are outlined. Techniques for partitioning unstructured grids among processors and for extracting parallelism in explicit and implicit solvers are discussed. Finally, some dynamic load balancing ideas, which are useful in adaptive transient computations, are presented.
Searching for good and diverse game levels

In procedural content generation, one is often interested in generating a large number of artifacts that are not only of high quality but also diverse, in terms of gameplay, visual impression or some other criterion. We investigate several search-based approaches to creating good and diverse game content, in particular approaches based on evolution strategies with or without diversity preservation mechanisms, novelty search and random search. The content domain is game levels, more precisely map sketches for strategy games, which are meant to be used as suggestions in the Sentient Sketchbook design tool. Several diversity metrics are possible for this type of content: we investigate tile-based, objective-based and visual impression distance. We find that evolution with diversity preservation mechanisms can produce both good and diverse content, but only when using appropriate distance measures. Reversely, we can draw conclusions about the suitability of these distance measures for the domain from the comparison of diversity preserving versus blind restart evolutionary algorithms.
Approximating multi-dimensional aggregate range queries over real attributes

Finding approximate answers to multi-dimensional range queries over real valued attributes has significant applications in data exploration and database query optimization. In this paper we consider the following problem: given a table of d attributes whose domain is the real numbers, and a query that specifies a range in each dimension, find a good approximation of the number of records in the table that satisfy the query. We present a new histogram technique that is designed to approximate the density of multi-dimensional datasets with real attributes. Our technique finds buckets of variable size, and allows the buckets to overlap. Overlapping buckets allow more efficient approximation of the density. The size of the cells is based on the local density of the data. This technique leads to a faster and more compact approximation of the data distribution. We also show how to generalize kernel density estimators, and how to apply them on the multi-dimensional query approximation problem. Finally, we compare the accuracy of the proposed techniques with existing techniques using real and synthetic datasets.
Management of Distributed Big Data for Social Networks

In the current era of Big Data, high volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. Due to the well-known 5V's of these Big Data, many traditional data management approaches may not be suitable for handling the Big Data. Over the past few years, several applications and systems have developed to use cluster, cloud or grid computing to manage Big Data so as to support data science, Big Data analytics, as well as knowledge discovery and data mining. In this paper, we focus on distributed Big Data management. Specifically, we present our method for Big Data representation and management of distributed Big Data from social networks. We represent such big graph data in distributed settings so as to support big data mining of frequently occurring patterns from social networks.
Acquiring word meanings with questions, answers and a dynamic environment - generalizing with neural networks

The majority of all words in natural language refer to some state or change of states in our environment. In this paper, we show how a three-layer neural network, which receives its input from two sources words and a simulated environment, can be trained to answer short questions relating to the state of the simulated environment. The network has initially no knowledge about the internal relationships between the words or how they are related to the simulated environment this knowledge has to be acquired during the training. We also show that the trained network is able to generalize well (i.e. to answer questions correctly) when it is confronted with a question and an environmental state that was not used in the training phase.
Modeling and nonlinear control of antagonistically actuating pneumatic artificial muscles

This paper discusses modeling and nonlinear control of a joint antagonistically actuated by two pneumatic, artificial muscles. A single model of the whole system is obtained by a combined physical and phenomenological modeling approach. The combined model for the joint, the muscles and the proportional valves results in a nonlinear, affine-in-control system description. The model is used to derive control laws for an input/output linearization approach to linearize the plant. Modeling and parametrization errors are covered via an outer control loop consisting of a state-feedback which is extended by an additional feedback of error integral. Extensive experimental results show the quality of the model and the performance of the respective control laws.
A multimodal-input multimedia-output guidance system: MMGS

Wehave built a multimodal-input multimedia-outputguid-ance system called MMGS. The input of a user can be a combination of speech and hand-written gestures. The system, on the other hand, outputs a response that combines speech, three-dimensional graphics, and/or other information. This system can interact cooperatively with the user by resolving ellipses/anaphora and various ambiguities such as those caused by speech recognition errors. It is currently implemented on a SGI workstation and achieves nearly real-time processing.
Parallel Shortest-Path Queries in Planar Graphs

We develop several parallel algorithms for shortest distance queries in planar graphs that use graph partitioning in the preprocessing phase to precompute and store distances between selected pairs of vertices. In the query phase, given a pair of arbitrary vertices v and w, the stored information is used to find the distance between v and w fast. The algorithms are implemented and tested on a high performance cluster with upto 256 16-core CPUs and their performances are analyzed and compared.