Miracle-dz/UniFAR-FTUs huggingface dataset:
Grille d’analyse de la performance des Réseaux Terntorialisés d’Organisations : le cas des places portuaires maritimes de commerce

Cette recherche vise à définir un cadre théorique de la performance dans les Réseaux Territorialisés d’Organisations (RTO), préalable à la conception d’outils de gestion spécifiques. Nous proposons une typologie modélisant des critères issus des outils de gestion de type tableaux de bord ou balanced scorecard d’une part, et des Facteurs Territoriaux de Performance (FTP) d’autre part. Sur la base d’une revue de littérature, nous appliquons cette typologie au cas des RTO portuaires, dont la vocation industrielle est la prestation de service logistique. Nous proposons une grille d’analyse de la performance de ces RTO et abordons les pistes de recherche dégagées. This research aims to establish a theoretical framework for performance in clusters, prior to the design of specific management tools. We propose a performance criteria typology, based on management tools on the one hand, and Territorial Factors on the other hand. Based on a literature review, we apply this typology to ports Clusters. We propose a performance analysis tool and define some future research possibilities.
Overlap regulation for additive overlapping clustering methods

Overlapping Clustering is an important technique in machine learning which aims to organize data into a set of non-disjoint groups rather than the disjoint one which is the case of conventional clustering methods. Several machine learning applications require that data object be assigned to one or several groups resulting in non-disjoint partitioning of data such as document clustering where each document can discuss one or many topics and then must be assigned to one or several groups. This paper presents a new partitional overlapping clustering method based on the additive model of overlaps. Compared to existing methods which build clusters with fixed size of overlaps, the proposed method gives users the ability to regulate this size. Experiments performed on simulated and real datasets show the performance of the proposed regulation principle to control the size of overlaps among groups.
On the stability of network distance estimation

Overlay network distance monitoring and estimation system can benefit many new applications and services, such as peer-to-peer overlay routing and location. However, there is a lack of such scalable system with small overhead, good usability, and good distance estimation accuracy and stability. Thus we propose a scalable overlay distance monitoring system, Internet Iso-bar, which clusters hosts based on the similarity of their perceived network distance, with no assumption about the underlying network topology. The centers of each cluster are then chosen as monitors to represent their clusters for probing and distance estimation. We compare it with other network distance estimation systems, such as Global Network Positioning (GNP) [1]. Internet Iso-bar is easy to implement and use, and has good scalability and small communication and computation cost for online monitoring. Preliminary evaluation on real Internet measurement data also shows that Internet Iso-bar has high prediction accuracy and stability. Finally, by adjusting the number of clusters, we can smoothly trade off the measurement and management cost for better distance estimation accuracy and stability.
One-to-Many Node Matching between Complex Networks

Revealing the corresponding identities of the same individual in different systems is a common task in various areas, e.g., criminals inter-network tracking, homologous proteins revealing, ancient words translating, and so on. With the reason that, recently, more and more complex systems are described by networks, this task can also be accomplished by solving a node matching problem among these networks. Revealing one-to-one matching between networks is for sure the best if we can, however, when the target networks are highly symmetric, or an individual has different identities (corresponds to several nodes) in the same network, the exact one-to-one node matching algorithms always lose their effects to obtain acceptable results. In such situations, one-to-many (or many-to-many) node matching algorithms may be more useful. In this paper, we propose two one-to-many node matching algorithms based on local mapping and ensembling, respectively. Although such algorithms may not tell us the exact correspondence of the identities in different systems, they can indeed help us to narrow down the inter-network searching range, and thus are of significance in practical applications. These results have been verified by the matching experiments on pairwise artificial networks and real-world networks.
Prosodic variability and segmental durations in Greek and Swedish

This contribution is an acoustic analysis of prosodic effects on segmental durations with reference to syllable structure, stress, focus and tempo in Greek and Swedish. Disyllabic nonsense words whose initial syllable had one, or two, or three consonant onsets were examined in stressed/unstressed, focused/unfocused and normal/fast tempo carrier sentence contexts in the corresponding languages. The results indicate that: (1) syllabic onset branching has an inverse effect on consonant duration in both Greek and Swedish, (2) stress has a bigger effect on the vowel than consonant duration in both Greek and Swedish, (3) focus has a major effect on both consonant and vowel durations in Swedish but not in Greek and (4) tempo effects both consonant and vowel durations in Greek as well as in Swedish
The Book Review Column 1

Editorial about Book Prices: In the first draft of this column I had the following comment about Beck’s book on Combinatorial Games: The books is expensive and not online. I assume that the author is more interested in getting the material out to people who want to read it rather than in making money. Yet the book has a high price tag and is not online. The entire math-book-publishing business needs to be rethought. The problem is not hypothetical. I mentor many high school and college students on projects. This book has a wealth of ideas for projects; however, I am not going to ask my students to shell out $65.00 for it. Hence I have not had any students work on this material. When I emailed a link of the first draft to all authors-of-books, authors-of-reviews, and publishingcontacts Lauren Cowles of Cambridge Press (Beck’s publisher) responded that (1) I didn’t have prices listed for the other books (true at the time but since corrected), and (2) Beck’s book is not bad in terms of price-per-page. (3) Most books are not online, including the other ones in the column. I interpret her argument as don’t single out Beck’s book since most books are overpriced and not online. I agree with her point entirely. It would be easy for me to blame the industry; however, I do not know what the economics of this industry is. I can however suggest what WE as a community can do:
Saturation throughput analysis of WAVE networks in Doppler spread scenarios

IEEE 802.11p, also known as wireless access in vehicular environment (WAVE), extends the applications of IEEE 802.11 to a fast fading vehicular communication environment. In WAVE systems, Doppler effect should not be ignored because of the high velocity of vehicles. Hence, in this paper the authors study the characteristics of physical layer WAVE system in the presence of the Doppler spectrum, such as symbol error rate performance and inter-subcarrier interference power. The throughput performance expression of a WAVE system is derived theoretically, which is the function of the frame size, number of the nodes, transmission probability, frame error rate and Doppler spread. Based on the obtained expressions, the optimal frame size, transmission probability and the number of nodes supportable in the WAVE system are derived to evaluate the maximum throughput performance. Finally, to validate the analytical results, simulations have been conducted to show the effectiveness of the proposed scheme.
Effects of Repeated Bouts of Segmental Vibration Therapy on Balance in Parkinsons Disease

Background: One of the cardinal features of Parkinson’s disease is postural instability. This instability is believed to be due to abnormalities in processing afferent information from the vestibular, somatosensory and visual systems. Whole body vibration has been shown to improve balance in older adults. The goals of this study were to determine how sensory information affects balance and to examine if multiple sessions of segmental vibration therapy improved balance in individuals with Parkinson’s disease. Methods: Balance in healthy older adults and in individuals with Parkinson’s disease was assessed using the modified Clinical Test of Sensory Integration and Balance. After initial assessment, individuals with Parkinson’s disease were randomized into a control or segmental vibration therapy group. The segmental vibration therapy group completed twelve sessions, over four weeks, and was re-tested after that period. Results: Subjects with Parkinson’s disease showed the highest overall level of sway in the eyes closed soft surface condition, when compared to healthy older adults. However, repeated bouts of segmental vibration did not result in significant improvement in the sway scores. Conclusion: Although these individuals showed significant balance deficits, segmental vibration therapy did not promote improvements in balance, as measured with the modified Clinical Test of Sensory Integration and Balance test.
Radiotherapy and chemotherapy in pancreatic cancer. Topical issues and future perspectives.

Pancreatic cancer is one of the top five causes of cancer death in the Western world and long term survival remains poor with less than 5% of patients alive 5 years after diagnosis [1]. In 1996, the age-adjusted incidence and mortality rates were 12.6 and 12.4 per 100,000 inhabitants for men, and 7.4 and 7.2 per 100,000 inhabitants for women, respectively. The high mortality rate of pancreatic cancer is due to the high incidence of metastatic disease at the time of diagnosis and the lack of adequate systemic therapies. At present, surgery offers the only means of cure. Unfortunately, only 5-25% of patients present with resectable tumors. Patients who undergo radical resection for localized pancreatic cancer have a long-term survival rate of approximately 20% and a median survival time of 11 to 20 months. A much higher percentage (40% to 45%) of patients present with metastatic disease, which carries a shorter median survival of 3 to 6 months [2]. Patients with locally advanced carcinoma of the pancreas constitute an intermediate group. These patients have pancreatic tumors which are defined as surgically unresectable, but have no evidence of distant metastases. A tumor without metastatic disease is considered unresectable if it has one of the following features: extensive peripancreatic lymph node involvement; encasement or occlusion of the superior mesenteric vein or portal vein confluence; direct involvement of the superior mesenteric artery, celiac axis, inferior vena cava or aorta. Combined treatment with radiation and chemotherapy increases median survival for patients with locally advanced cancer of approximately 9 to 13 months, but rarely results in long term survival [2].
Power allocation under quality of service constraints for uplink multi-user MIMO systems

In this contribution, we derive the optimal power allocation policy in the case of an uplink multiuser system under quality of service constraints. Interestingly, using asymptotic results (in the number of users and dimensions) of random matrix theory and under the assumption of Minimum Mean Square Error Successive Interference Cancellation (MMSE-SIC) at the base station, the results show that the users requested rate can be satisfied based only on the knowledge of the statistical nature of the environment and not the channel realization as it is commonly assumed. Moreover, a framework is provided to determine the decoding order of users which minimizes the overall transmitted power. The results are then validated through simulations.
Distributed Real-Time Power Balancing in Renewable-Integrated Power Grids With Storage and Flexible Loads

The large-scale integration of renewable generation directly affects the reliability of power grids. We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple renewable generators (RGs) each co-located with storage, and is connected with external markets. An aggregator operates the power grid to maintain power balance between supply and demand. Aiming at minimizing the long-term system cost, we first propose a real-time centralized power balancing solution, taking into account the uncertainty of the renewable generation, loads, and energy prices. We then provide a distributed implementation algorithm, significantly reducing both computational burden and communication overhead. We demonstrate that our proposed algorithm is asymptotically optimal as the storage capacity increases and the CG ramping constraint loosens. Moreover, the distributed implementation enjoys a fast convergence rate, and enables each RG and the aggregator to make their own decisions. Simulation shows that our proposed algorithm outperforms alternatives and can achieve near-optimal performance for a wide range of storage capacity.
Rate of cholesteryl ester transfer between high and low density lipoproteins in human serum and a case with decreased transfer rate in association with hyperalphalipoproteinemia.

The rate of cholesteryl ester transfer between intrinsic high and low density lipoproteins (HDL and LDL) was measured in human serum in the absence of lecithin:cholesterol acyltransferase (LCAT) and very low density lipoproteins. The rate was calculated according to the equilibrium transfer model between the two lipoprotein cholesteryl ester pools. The average rate of the transfer was 213 +/- 95 nmol/h/ml (mean +/- S.E.) for all 20 normal and 13 hyperlipoproteinemic subjects. The transfer rate in individual serum was higher by several times than the cholesterol esterification rate, showing that cholesteryl ester generated by LCAT on HDL is promptly distributed among various lipoprotein subclasses. The rate of cholesteryl ester transfer in the serum was significantly proportional to the product of intrinsic HDL and LDL concentrations, a possible indicator of the frequency of collision between these lipoproteins, for the range of physiological lipoprotein concentrations. Among the subjects analyzed was a patient with remarkable hyperalphalipoproteinemia accompanied by hypertriglyceridemia, who showed a very low rate of cholesteryl ester transfer in relation to his LDL and HDL concentrations. Compositional analysis of lipoprotein lipids of the patients also supported the possibility of impaired neutral lipids transfer activity among lipoproteins in the blood.
Automatic Analysis and Classification of Obfuscated Bot Binaries

Botnets is a serious threat to Internet security. Popular defense strategies such as traffic filtering and malware detection all require a good understanding of the constituent bot binaries for creating the corresponding filter rules or signatures. This means that an effective analysis and classification process for bot binaries is needed for dealing with the threat of botnets. Unfortunately, the rampant usage of binary obfuscation these days has made the analysis and classification rather difficult. A simple string pattern matching or disassembly of the binary no longer suffices as the exact instruction sequence can be easily altered by obfuscation. In this work, we propose a new framework for automatic analysis and classification of bot binaries. The framework analyzes a bot binary’s runtime system call trace and uses the longest common subsequences between system call traces for the classification of bot binaries. The framework can effectively deal with obfuscated bot binaries. Experiment result shows that the framework can attain an overall 94% true positive rate and 93% true negative rate.
Information-theoretic measures of predictability for music content analysis

This thesis is concerned with determining similarity in musical audio, for the purpose of applications in music content analysis. With the aim of determining similarity, we consider the problem of representing temporal structure in music. To represent temporal structure, we propose to compute information-theoretic measures of predictability in sequences. We apply our measures to track-wise representations obtained from musical audio; thereafter we consider the obtained measures predictors of musical similarity. We demonstrate that our approach benefits music content analysis tasks based on musical similarity. For the intermediate-specificity task of cover song identification, we compare contrasting discrete-valued and continuous-valued measures of pairwise predictability between sequences. In the discrete case, we devise a method for computing the normalised compression distance (NCD) which accounts for correlation between sequences. We observe that our measure improves average performance over NCD, for sequential compression algorithms. In the continuous case, we propose to compute information-based measures as statistics of the prediction error between sequences. Evaluated using 300 Jazz standards and using the Million Song Dataset, we observe that continuous-valued approaches outperform discrete-valued approaches. Further, we demonstrate that continuous-valued measures of predictability may be combined to improve performance with respect to baseline approaches. Using a filter-and-refine approach, we demonstrate state-of-the-art performance using the Million Song Dataset. For the low-specificity tasks of similarity rating prediction and song year prediction, we propose descriptors based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. We evaluate our descriptors using a dataset of 15 500 track excerpts of Western popular music, for which we have 7 800 web-sourced pairwise similarity ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.
Long-term culture at elevated atmospheric CO2 fails to evoke specific adaptation in seven freshwater phytoplankton species

The concentration of CO2 in the atmosphere is expected to double by the end of the century. Experiments have shown that this will have important effects on the physiology and ecology of photosynthetic organisms, but it is still unclear if elevated CO2 will elicit an evolutionary response in primary producers that causes changes in physiological and ecological attributes. In this study, we cultured lines of seven species of freshwater phytoplankton from three major groups at current (approx. 380 ppm CO2) and predicted future conditions (1000 ppm CO2) for over 750 generations. We grew the phytoplankton under three culture regimes: nutrient-replete liquid medium, nutrient-poor liquid medium and solid agar medium. We then performed reciprocal transplant assays to test for specific adaptation to elevated CO2 in these lines. We found no evidence for evolutionary change. We conclude that the physiology of carbon utilization may be conserved in natural freshwater phytoplankton communities experiencing rising atmospheric CO2 levels, without substantial evolutionary change.
An epidemiologic approach to gene‐environment interaction

This paper illustrates how epidemiologic principles can be used to investigate relationships between genetic susceptibility and other risk factors for disease. Five plausible models are described for relationships between genetic and environmental effects, and an example of a simple mendelian disorder that fits each model is given. Each model leads to a different set of predictions about disease risk in individuals with the genetic susceptibility alone, the risk factor alone, both, or neither. The risk predictions for the different models are described, and research designs for testing them are discussed.
SPEAKER VERIFICATION UNDER ADDITIVE NOISE CONDITIONS WITH NON-STATIONARY SNR USING PMC Lit

In real speaker verification applications, additive or convolutive noise creates a mismatch between training and recognition environments, degrading performance. Parallel Model Combination (PMC) is used successfully to improve the noise robustness of Hidden Markov Model (HMM) based speech recognisers [5]. This paper presents the results of applying PMC to compensate for additive noise with non-stationary signal-tonoise ratios (SNRs) in HMM-based text-dependent speaker verification. Speech and noise data were obtained from the YOHO [6] and NOISEX-92 databases [13] respectively. Speaker recognition Equal Error Rates (EER) are presented for noise-contaminated speech at different SNRs and different noise sources. For example, average EER for speech in operations room noise at 6dB SNR dropped from approximately 20% uncompensated to less than 5% using PMC. Finally, it is shown that PMC improves verification by an average EER reduction of 18.29% under varying SNRs (50% of the actual speech duration under 0dB SNR).
Integrative Data Analysis to Improve Protein Identification in Shotgun Proteomics Experiments Supplementary Notes

1 Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712 2 Center for Systems and Synthetic Biology, Department of Chemistry and Biochemistry & Institute for Cellular and Molecular Biology, 2500 Speedway, The University of Texas at Austin, Austin, TX 78712 3 Children’s Cancer Research Institute; The University of Texas Health Science Center at San Antonio; San Antonio, TX 78229
Folding simulation of Trp-cage utilizing a new AMBER compatible force field with coupled main chain torsions

A newly developed AMBER compatible force field with coupled backbone torsion potential terms (AMBER032D) is utilized in a folding simulation of a mini-protein Trp-cage. Through replica exchange and direct molecular dynamics (MD) simulations, a multi-step folding mechanism with a synergetic folding of the hydrophobic core (HPC) and the α-helix in the final stage is suggested. The native structure has the lowest free energy and the melting temperature predicted from the specific heat capacity Cv is only 12 K higher than the experimental measurement. This study, together with our previous study, shows that AMBER032D is an accurate force field that can be used for protein folding simulations.
Real-Time Activity Recognition Technique for Surveillance Camera and Implementation on Digital Signal Processor

Video Surveillance allows users to easily monitor the secure areas with surveillance cameras, and thus eliminating the need for manual work and saves the huge monitoring costs. A novel method for object tracking, based on image segmentation is proposed to automatically recognize activities around restricted area to improve safety and security of the servicing area by multiplexing hundreds of video streams in real time. Key component for the proposed system includes background learning and updating, foreground segmentation, features extraction, and decision-making process. The proposed method uses adaptive background subtraction techniques to handle illumination changes to improve the performance of the video surveillance and video- enable operations. The algorithms are simulated using MATLAB tool to verify its stability in various conditions by giving various input video samples and its output is taken as benchmark for real time implementation in DSP processor.