Miracle-dz/UniFAR-FTUs huggingface dataset:
Re-using prior tactile experience by robotic hands to discriminate in-hand objects via texture properties

This paper proposes an online tactile transfer learning strategy for discriminating objects through the surface texture properties via a robotic hand and an artificial robotic skin. The proposed method has the ability to autonomously select and exploit the previously learned multiple texture models while discriminating new textures with a very few available training samples or even one. The experimental results show that employing the proposed method and 10 prior texture models, the robotic hand could discriminate 12 objects via their surface textures with 97% and 100% recognition accuracy with only one and ten training samples respectively. Moreover, the experimental outcomes illustrate that our proposed algorithm is robust against of any negative tactile knowledge transfer.
Tamoxifen Citrate Encapsulated Sustained Release Liposomes: Preparation and Evaluation of Physicochemical Properties

The present study was designed for the development of a stable sustained release liposomal drug delivery system for tamoxifen citrate (TC) using soya phosphatidylcholine (SPC), cholesterol (CH) and span 20 as main ingredients. Liposomes were prepared by formation of thin lipid film followed by hydration. The mean vesicle diameter was found to be 203.5 ± 19.5 nm with 21% of the liposomal population having average diameter below 76.72 ± 6.7 nm. There was a good vesicular distribution with the polydispersity index of 0.442 ± 0.03. The maximum loading of drug was determined to be 53.60% of the initial amount that is 34.58 μg of drug per mg of lipid. Amongst the different storage conditions, liposomes stored at 2–8°C were found to be most stable and only 4% of the drug was lost over the storage period of 5 weeks. In vitro release studies of liposomes showed that 50% of drug was released within 3 hours (h) whereas 95% drug was released in 30 h. This indicates the usefulness of the liposomal delivery system for sustaining the in vitro release of tamoxifen citrate.
A cost effective intelligent solution to reduce traffic congestion

Major difficulty of finding a solution for heavily congested roads in city areas is the unpredictable nature of traffic flow rates at a given time. There can be variations due to unexpected accidents, breakdowns, events and other emergency situations which may occur due to political or environmental issues. Developing road infrastructure or imposing restrictions on motor vehicles entering city areas would not be cost effective solutions for this problem, especially in developing countries like Sri Lanka. This may be due to lack of funds to improve the infrastructure, the objections of the general public on applying restrictions on the inflow of vehicles or difficulties on acquiring lands to construct roads in highly populated areas. If it is possible to have proper traffic directing plans with the variation of traffic flow rates it is possible to reduce congestions with the existing infrastructure with minimal cost. This paper presents such a solution where new efficient traffic plans can be generated at real-time with the latest information which can be acquired with the latest communication technologies. Traffic conditions were transmitted using mobile or satellite communication channels as required from the pre-determined locations. Then a better traffic plan is achieved by identifying a congested junction or junctions in a city and rearranging the traffic flow in such a way that the interruptions to the continuous traffic flow are minimized at the congested location or locations. The proposed intelligent software tool can generate efficient traffic plans and can be used as a decision support system to reduce the traffic congestion.
Automatic Approach for Emulsions Stability Assessment in Microscope Images

The problem of emulsions stability evaluations from images acquired in microscopy is considered. The proposed approach consists of four consecutive steps: 1) images enhancement; 2) segmentation based on Watershed algorithm; 3) drops identification using a template matching procedure; 4) stability evaluation. Finally, an analysis of results is performed by comparing the estimated results with respect to results obtained manually by the expert.
Pipelined Multi-step Interpolating A / D Converter by Edmond Patrick Coady Pipelined Multi-step Interpolating A / D Converter by

This work investigates the feasibility of an interpolating approach to pipelined multi-step analog-to-digital conversion. The proposed architecture aims to ease the severe constraints imposed on amplifier design in standard pipelined multi-step architectures. By easing the amplifier performance criteria, circuits for low voltage supply and low power operation are feasible. The design of a 10 bit, 20MSPS ADC is presented to illustrate the benefits of the pipelined multi-step interpolating converter. The chosen architecture is a 4-residue interpolator with a 5-stage pipeline. The first 4 stages resolve 3 bits each and the last stage resolves 2 bits. The circuit functions are implemented in 0.6gm CMOS using primarily switchedcapacitor techniques. By taking advantage the reduced gain requirements of the amplifiers, the circuitry can operate on a 2.5V supply. The analog circuitry dissipates a simulated 65mW while achieving <1/2 LSB DNL performance at 10-bits. Thesis Supervisor: Katsufumi Nakamura Title: Senior Design Engineer Thesis Supervisor: James K. Roberge Title: Professor of Electrical Engineering
Investigating the Impact Factors for Event Detection Using Web Search Engines

Online popular events, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. Recently, the web is becoming an important event information provider and poster due to its real-time, open, and dynamic features. However, it is difficult to detect events since the huge scale and dynamics of the internet. In this paper, we define the novel problem of investigating impact factors for event detection. We give the definitions of five impact factors including the number of increased web pages, the number of increased keywords, the number of communities, the average clustering coefficient, and the average similarities of web pages. These five impact factors contain statistic and content information of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that that the number of web pages and the average clustering coefficient can be used to detect events. Some strategies integrating the number of web pages and the average clustering coefficient are also employed. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly.
Evaluation of Ontology-based User Interests Modeling

Deriving users’ interests from their online searching and browsing behaviors is an important research direction with several applications in content search and management. Manually edited Web directories, such as Open Directory Project (ODP) or Yahoo! 2 directory, provide ontology of concepts (categories) along with pages relevant to those categories. Aiming to evaluate and compare the performance of different cues in searching and browsing activities for user interests modeling, we used four inputs (search query, expanded query, snippet, and page content) and mapped each of them into a set of ODP categories. Then we automatically identified an output category as representative of user interest that initiated a search. Through a controlled experiment, we compared the performance of different inputs in interest mapping using two metrics hit rate and average hit index. We found that the use of page content achieved the best results, i.e. highest hit rate and lowest average hit index. Also an expanded query (the original query with a few additional terms) was a better input to identify user interests than the original query and the snippet. The expanded query produced hit rates 21-121% higher than those achieved by the original query and had 34% lower average hit index than that by the original query.
A Parallel Implementation of Idea Graph to Extract Rare Chances from Big Data

In current days, data tend to become much bigger than before, and the distributed computing system is an prevalent option to deal with them. As one of powerful tools, MapReduce framework provides a cheap and efficient way to write parallel programs to run on distributed computing systems. Chance discovery (CD) is an extension of data mining, where chance refers to rare but important events or situations. Idea Graph is an efficient algorithm proposed to detect chances. However, the traditional implementation of Idea Graph is sequential, and its performance encounters some bottlenecks when dealing with big data. In this paper, we propose a parallel implementation of Idea Graph using MapReduce to better meet with the challenge of big data. First, we introduce the MapReduce framework, and then Idea Graph is introduced in brief. After that, we present the details on how we design the parallel Idea Graph implementation. In the end of the paper, several experiments are conducted to evaluate the proposed implementation. The experimental results demonstrate the validation of the proposed implementation and its better performance as compared with that of sequential Idea Graph implementation when handling big data.
IDSS used as a framework for collaborative projects in conglomerate enterprises

Purpose: of this paper is to summarize the application study of the general framework of intelligent decision support system (IDSS) to collaborative projects in conglomerate enterprises. Today’s market competition requires project managers to make correct decisions fast. In some situations, even with the knowledge of how to find right information and which decision making methods to apply, people do not have enough time to make right decisions at the right time. In this paper, the framework of an IDSS system to support real-time collaboration and enable seamless data exchange is presented. The IDSS system is able to support the information flow between internal and external sources. Data are shared, prepared, and streamlined to appropriate analytical tools. A case study of manufacturing projects in a specific European shipbuilding conglomerate is used to illustrate the process and usefulness of IDSS in combination with Enterprise Resource Planning (ERP) systems. Design/methodology/approach: A model of internal and external information flows is proposed for enterprise information management. The important roles of facilitation and organization that the IDSS plays are demonstrated. In the case study examples of manufacturing projects analysis are given with the known methods, including Analytical Hierarchy Process and Bayes rule. Findings: It was found that IDSS is an essential component to enhance decision makings with complex information flows in large conglomerates. Main conclusions is to show what we will achieve through IDSS systems, to show how perform analysis in logical way. Research limitations/implications: The functionality of the developed framework is limited by the willingness of management style and culture changes in companies, as well as the level of interoperability between commercial software components. Practical implications: Project engineers and managers need to adapt to the new IT-based working environment. Intensive use of software tools may become the major challenge for those who cannot absorb information based on the new format of information presentation. Originality/value: The proposed new information management model and the framework of IDSS system enable ease of data sharing and processing by internal and external stakeholders in decision makings. The collaborative decision making consists of different parts: the management of information flow, the preparation of data for decision making, and actual decision making supported by several methods.
Effects of Functional Oils on Coccidiosis and Apparent Metabolizable Energy in Broiler Chickens

The objective of the present study was to investigate the effects of a mixture of functional oils (Essential, Oligo Basics Agroind. Ltda) on performance response of chickens challenged with coccidiosis and the determination of apparent metabolizable energy (AME), nitrogen-corrected apparent metabolizable energy (AMEn), the coefficients of protein and ether extract digestibility and intestinal morphology of broilers fed with diets containing Essential. In Exp. 1, a completely randomized design (CRD) was used, with one control diet without Essential inclusion with coccidiosis (Eimeria acervulina, Eimeria maxima, and Eimeria tenella) challenged birds and two different inclusion rates of Essential (1.5 kg/ton and 2 kg/ton) with coccidiosis-challenged and non-challenged birds for each inclusion rate, using 10 replicates and 50 birds per experimental unit. After 7 d of coccidiosis challenge, the livability was approximately 10% lower (p<0.05) for the control group. Intestinal lesion scores were lower (p<0.05) in the anterior intestine and the cecum for the chickens supplemented. Feed efficiency and growth rate were improved in birds supplemented with Essential (p<0.05) before the coccidiosis challenge and during the first 7 d post infection. In Exp. 2, a CRD was used, with one control diet without Essential inclusion and one diet with inclusion of Essential (1.5 kg/ton), using nine replications and 33 chicks per pen. The diets with Essential yielded approximately 4% higher AME (p = 0.003) and AMEn (p = 0.001). Essential supplementation increased villus height in the jejunum on d 14 (p<0.05). Villus height:crypt depth ratio for the supplemented birds was larger (p<0.05) in the jejunum on d 7, larger (p<0.05) in the jejunum and ileum on d 14. In conclusion, these functional oils improved the energy utilization and the livability and decreased lesions caused by coccidiosis in supplemented birds.
Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records

Heritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified millions of familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically-derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research. One Sentence Summary We demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.
Role of T1 Pelvic Angle in Assessing Sagittal Balance in Outpatients With Unspecific Low Back Pain

AbstractThe aim of the study was to explore the significance of T1 pelvic angle (TPA) for assessment of sagittal balance in a cohort of Chinese patients with unspecific low back pain.TPA has been commonly used to assess sagittal balance in adult spinal deformity. However, whether TPA could be used to assess sagittal balance in patients with unspecific low back pain effectively remains unanswered.Medical records of outpatients with unspecific low back pain who received treatment in our outpatient clinic between September 2013 and November 2014 were reviewed. Demographic data and radiographic data were collected. Correlation coefficients between TPA and other sagittal parameters were analyzed, and the intraclass correlation coefficient (ICC) analysis was performed to assess the inter- and intra-observer reliability of TPA. Patients were divided into 2 groups according to whether they were well-aligned (TPA ⩽ 20°) or poorly aligned (TPA > 20°), and then demographic and sagittal parameters were compared between the 2 groups of patients.A total of 97 patients with unspecific low back pain were included in this study. The inter- and intraobserver reliability of the TPA measure had excellent agreement (ICC = 0.985 and 0.919, respectively). There were significant correlations between TPA and age, LL, PT, PI, T1SPI, SVA, and NRS (all P < 0.05). Of the 38 well-aligned patients in Group A, SVA was ⩽5 cm in 33 (86.84%) patients and >5 cm in the other 5 (13.16%) patients, and of the 59 poorly aligned patients in Group B, SVA was >5 cm in 42 (71.19%) patients and ⩽5 cm in the other 17 (28.81%) patients. There were significant differences in age, LL, SS, PT, PI, T1SPI, SVA, and NRS between the 2 groups of patients, but no significant difference was observed in TK and TL.TPA could be used to assess sagittal balance in outpatients with unspecific low back pain effectively.
Structuring Disincentives for Online Criminals

This thesis considers the structural characteristics of online criminal networks from a technical and an economic perspective. Through large-scale measurements, we empirically describe some salient elements of the online criminal infrastructures, and we derive economic models characterizing the associated monetization paths enabling criminal profitability. This analysis reveals the existence of structural choke points: components of online criminal operations being limited in number, and critical for the operations’ profitability. Consequently, interventions targeting such components can reduce the opportunities and incentives to engage in online crime through an increase in criminal operational costs, and in the risk of apprehension. We define a methodology describing the process of distilling the knowledge gained from the empirical measurements on the criminal infrastructures towards identifying and evaluating appropriate countermeasures. We argue that countermeasures, as defined in the context of situational crime prevention, can be effective for a long-term reduction in the occurrence of online crime.
The Size of the Cooling Region of Hot Gas in Two Elliptical Galaxies

Some early-type galaxies show O VI emission, a tracer of gas at 105.5 K, and a predicted product of gas cooling from the X-ray-emitting temperatures. We studied the spatial extent and velocity structure of this cooling gas by obtaining spectra of the O VI doublet in NGC 4636 and NGC 5846 with the Far Ultraviolet Spectroscopic Explorer. For NGC 4636, the central LWRS pointing shows that the O VI lines are double-peaked and symmetrical about the systemic velocity of the galaxy, with a separation of 210 km s-1. An LWRS observation 30′′ from the center failed to show additional O VI emission. For NGC 5846, three spectra were obtained with 4′′ × 20′′ apertures (MDRS) at the center and 4′′ to the east and west of the center. The O VI line flux seen in the previous LWRS is contained in the sum of the smaller apertures, with most of the flux in a single noncentral MDRS aperture, suggesting a size for the emission ≤0.5 kpc; the emission consists of a blue and red peak. For both galaxies, the O VI velocity structure is similar to that of the optical [N II] emission and is consistent with rotation. The compactness and velocity structure of the O VI emission rules out cooling flow models with broadly distributed mass dropout but is consistent with cooling flow models in which the cooling occurs primarily in the central region. The 104 K gas may be the end state of the O VI emitting gas.
from Semileptonic D Decays

High statistics experimental studies of D mesons, such as E831 (FOCUS) and E791, are expected to yield O(106) fully reconstructed D decays with better momentum resolution than preceding experiments[1,2]. Anticipating high precision experimental results, we consider how well the CKM ratio |Vcd|/|Vcs| can be determined from a ratio of D → πeν to D → Keν decay rates and lattice QCD. For the Cabibbo allowed D → Keν decay, lower statistics and poorer resolutions in earlier experiments meant hadronic matrix elements could be described by functions of only two parameters: the CKM matrix element times a form factor at zero momentum transfer and the pole mass in a pole dominance Ansatz. The PDG quotes a 3% error for |Vcs|f + (0) based upon such techniques[3]. The uncertainty due to this Ansatz is difficult to quantify. They quote a 14% uncertainty in the theoretical determination of f + (0) resulting in a 15% uncertainty for |Vcs|. |Vcd| is determined with an error of 7% from νν production of charm off of valence d quarks[3]. Combining this uncertainty and that for |Vcs| we deduce an error of about 17% in the current best value of |Vcd|/|Vcs|. We examine uncertainties in determining |Vcd|/|Vcs| directly from a ratio of semileptonic decay rates. We expect that both experimental and theoretical systematic errors common to D → Keν and D → πeν will be reduced in ratios.
Segmentation and Characterization of Brain Tumor from MR Images

The objective of this paper is to present a method to characterize a brain tumor. The authors developed a tumor characterization technique using Marker Controlled Watershed Segmentation method and region property functions using image processing toolbox. The parameters extracted are area, major and minor axis length, eccentricity, orientation, equivdiameter, solidity and perimeter. This method is quite versatile, fast and simple to use. This can be applied to all type of 2D MR Images representing all tumors irrespective of their location in human body and their size. The proposed technique has been simulated on Matlab and results are compared with experimental data obtained from diagnostic centre
Cross-Language Opinion Lexicon Extraction Using Mutual-Reinforcement Label Propagation

There is a growing interest in automatically building opinion lexicon from sources such as product reviews. Most of these methods depend on abundant external resources such as WordNet, which limits the applicability of these methods. Unsupervised or semi-supervised learning provides an optional solution to multilingual opinion lexicon extraction. However, the datasets are imbalanced in different languages. For some languages, the high-quality corpora are scarce or hard to obtain, which limits the research progress. To solve the above problems, we explore a mutual-reinforcement label propagation framework. First, for each language, a label propagation algorithm is applied to a word relation graph, and then a bilingual dictionary is used as a bridge to transfer information between two languages. A key advantage of this model is its ability to make two languages learn from each other and boost each other. The experimental results show that the proposed approach outperforms baseline significantly.
A tracking method for weak and high dynamic GNSS inter-satellite link signal

Inter-satellite link (ISL) is an important part of the next generation of GNSS, as well as a significant symbol of GNSS modernization. This paper analyzes the future GNSS inter-satellite geometric properties and spatial parameters and studies the signal scheme of ISL. A novel tracking algorithm using Kalman Filter assisted by ephemeris is proposed to deal with the high dynamic and low carrier-to-noise ratio (CNR) ISL signals. Simulation results have shown that the proposed algorithm can achieve higher track precision than the traditional approach using Phase Lock Loop (PLL) assisted by Frequency Lock Loop (FLL).