Miracle-dz/UniFAR-FTUs huggingface dataset:
The stitched puppet: A graphical model of 3D human shape and pose

We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D and deform to represent different body shapes and to capture pose-dependent shape variations. Pairwise potentials define a “stitching cost” for pulling the limbs apart, giving rise to the stitched puppet (SP) model. Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives SP the realism of recent 3D body models with the computational advantages of part-based models. We apply SP to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge, where ours is the first method to successfully align all 3D meshes with no pose prior. The second involves estimating pose and shape from crude visual hull representations of complex body movements.
On Overlay Construction and Random Node Selection in Heterogeneous Unstructured P 2 P Networks

Unstructured p2p and overlay network applications, including several that the authors wish to build, often require that a random graph be constructed, and that some form of random node selection take place over that graph. A key and difficult requirement of many such applications is heterogeneity: peers have different node degrees in the random graph based on their capacity. Using simulations, this paper compares a number of techniques—some novel and some variations on known approaches—for heterogeneous graph construction and random node selection on top of such graphs. Our focus is on practical criteria that can lead to a genuinely deployable toolkit that supports a wide range of applications. These criteria include simplicity of operation, support for node heterogeneity, quality (uniformity) of random selection, efficiency and scalability, load balance, and robustness. We show that all these criteria can more-or-less be met by all the approaches. Our novel approach, however, stands out as the best from a practical perspective because of its simplicity: it achieves the criteria while requiring each node to set only a single tuning parameter, its desired relative load.
Indexing Algorithm Based on Improved Sparse Local Sensitive Hashing

In this article, we propose a new semantic hashing algorithm to address the new-merging problems such as the difficulty in similarity measurement brought by high-dimensional data. Based on local sensitive hashing and spectral hashing, we introduce sparse principal component analysis (SPCA) to reduce the dimension of the data set which exclude the redundancy in the parameter list, and thus make high dimensional indexing and retrieval faster and more efficient. In the meanwhile, we employ Boosting algorithm in machine learning to determine the threshold of hashing, so as to improve its adaptive ability to real data and extend its range of application. According to experiments, this method not only has satisfying performance on multimedia data sets such as images and texts, but also performs better than the common indexing methods.
An Enhanced Model for Early Software Reliability Prediction Using Software Engineering Metrics

Even though, few models exist to predict the reliability of a software system based on software engineering metrics, due to fuzziness present in the data, models developed for early prediction are inadequate to provide trustworthy results. Moreover, besides early reliability prediction, software professionals are also keen to know the phase in which corrective action need to be performed in order to achieve the target reliability estimate. Considering this issue, given a specific target reliability estimate by the customer, the model proposed in this paper enables software professionals and researchers to identify the phase in which corrective action need to be preformed.
Effect of Self Preferred Romantic Music after Physical Exertion in Males

Romantic music is considered to promote relaxation and reduce stress. Past researchers reveals that different types of music varying in tempo can have physiological effects on the body. To examine the effects of Romantic music after physical exertion, 15 male students performed a stressful, cardiovascular exercise. After performing the running exercise, participants were made listened to their preferred romantic bollywood music. The physiological effects of romantic music promoting relaxation are indicated by changes in Heart rate variability (HRV). Specifically, the relative change in low to high frequency (LF/HF) ratio component of heart rate variability proven to be a non invasive technique was used to indicate physiological changes occurring in the Autonomic nervous system (ANS). The results obtained demonstrate the effects of romantic music after physical exertion based on individual preference for the song.
Evaluation of the diffusion-like accelerated demons algorithm

In this paper we present a new approach for the smoothing of displacement field in the nonrigid accelerated demons algorithm for a discontinuities MRI image registration. accelerated demons algorithm use Gaussian smoothing filter to penalize oscillatory motion in the displacement fields during the registration, which has significant impact on registration quality. Simulated images show that Gaussian smoothing cannot properly handle these discontinuities. For this reason we introduce diffusion-like smoothing which shows promising results, and start as base for our further research on the topic of discontinuous image registration. This significant problem impedes recovering of organ motion in the case for example persons breathing cycle.
Linking bar codes to recycling information for mobile phones

To fulfill the requirements for a broad range of information on their products, manufacturers must address two problems at the same time. They need to gather and develop the necessary information on products, and they need to provide the information to recycling and dismantlement facilities. To address this problem, a system was developed, which links the existing bar codes on mobile phones to web sites containing disassembly information. The system makes use of the barcode label containing the IMEI (international mobile equipment identity) number, which is on every GSM mobile phone. The IMEI contains information on the manufacturer and the model type. By scanning the IMEI barcode, typing in the IMEI number or the model type, the program automatically connects with the appropriate web site containing the disassembly information. The recycler can thus access the information needed for treatment by simply scanning the barcodes. A demonstrator proved the concept of linking disassembly information by scanning a product barcode. Thus the information needed by recyclers can be stored and obtained efficiently and cost effectively.
Identifying Recurrent and Unknown Performance Issues

For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.
Supervised typing of big graphs using semantic embeddings

We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15× speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.
A Realistic Validation Study of a New Nitrogen Multiple-Breath Washout System

Background For reliable assessment of ventilation inhomogeneity, multiple-breath washout (MBW) systems should be realistically validated. We describe a new lung model for in vitro validation under physiological conditions and the assessment of a new nitrogen (N2)MBW system. Methods The N2MBW setup indirectly measures the N2 fraction (FN2) from main-stream carbon dioxide (CO2) and side-stream oxygen (O2) signals: FN2 = 1−FO2−FCO2−FArgon. For in vitro N2MBW, a double chamber plastic lung model was filled with water, heated to 37°C, and ventilated at various lung volumes, respiratory rates, and FCO2. In vivo N2MBW was undertaken in triplets on two occasions in 30 healthy adults. Primary N2MBW outcome was functional residual capacity (FRC). We assessed in vitro error (√[difference]2) between measured and model FRC (100–4174 mL), and error between tests of in vivo FRC, lung clearance index (LCI), and normalized phase III slope indices (Sacin and Scond). Results The model generated 145 FRCs under BTPS conditions and various breathing patterns. Mean (SD) error was 2.3 (1.7)%. In 500 to 4174 mL FRCs, 121 (98%) of FRCs were within 5%. In 100 to 400 mL FRCs, the error was better than 7%. In vivo FRC error between tests was 10.1 (8.2)%. LCI was the most reproducible ventilation inhomogeneity index. Conclusion The lung model generates lung volumes under the conditions encountered during clinical MBW testing and enables realistic validation of MBW systems. The new N2MBW system reliably measures lung volumes and delivers reproducible LCI values.
Reputation for Inter-Domain QoS Routing

Video traffic, which represents an increasing fraction of the Internet traffic, requires end-to-end quality of service (QoS) guarantees for inter-domain routing. However, providing such guarantees remains a challenge essentially because it requires a strong and fair cooperation among the different network operators or Autonomous Systems (ASes), crossed by the traffic. Having a single AS on the path that does not meet its QoS engagement is sufficient to violate the end-to-end QoS guarantees. Unfortunately, the client is not capable of distinguishing unfair ASes from honest ones at the time it selects its path. Reputation mechanisms turn out to be very efficient tools to estimate how trustworthy and reliable entities can be without requiring the help of any central authority. They are effective to foster cooperation by remedying selfishness. In this position paper, we identify the main properties a reputation mechanism should meet to improve inter-domain QoS routing, and we provide a coarsed-grain vision of the design of such a mechanism.
Online condition monitoring of lithium-ion batteries using impedance spectroscopy

Lithium-ion batteries are at the forefront of energy storage technology due to its high energy density and durability; they are however expensive and must be operated within certain limits to prevent damaging them. Thus, there is a need for improved condition monitoring to increase its life expectancy and performance. In this work, Electrochemical Impedance spectroscopy (EIS) is implemented using a bi-directional DC-DC converter to characterize the lithium-ion battery under near real-time conditions. EIS is a widely used technique for characterizing electrochemical cells, however, due to long procedural time involved, it is limited to offline (laboratory) testing. This paper investigates several types of impedance spectroscopy (IS) techniques that can be performed while a battery is connected to a load, to characterize the lithium-ion battery and hence determine and monitor its State of Health (SoH) and State of Charge (SoC). The main contribution of this work is the implementation of a multi-sine broadband impedance spectroscopy (BIS) technique in a switching converter to reduce measurement time and thus enable near real-time IS measurement. The proposed technique is carried out using the associated battery converter without additional components incorporated into the converter. Results obtained show that the BIS implemented on the switching converter is effective at characterizing the battery's impedance spectrum as compared to the conventional EIS.
Characterization of public datasets for Recommender Systems

As Recommender Systems are becoming very common and widespread, there is an increasing need to evaluate their characteristics such as accuracy, diversity, scalability etc. One of the most fruitful ways to do this is by using public datasets with explicit user feedback about the items. In this paper we present and describe more than 20 available datasets covering different domains such as movies, books, music etc. Each dataset is described over a number of attributes such as size, domain, format of the data, type of access. Unfortunately we did not find any information about the quality of the data contained, that remains an open issue. We also refer to examples from the literature about using the datasets to evaluate recommendation algorithms or solutions. Overall aim of the paper is to offer a convenient resource for finding and selecting datasets as a support for the empirical evaluation of recommendation algorithms and techniques.
Influence of Synthetic Conditions on the Polyol Process for High Power High Energy Materials: Structural and Electrochemical Properties

Author(s): Yoon, Hyojung | Advisor(s): Meng, Ying S | Abstract: With the rapid depletion of conventional energy sources based on fossil fuels and increasing energy demand, sustainable and renewable energy technologies, such as solar, wind, hydroelectric power, have been drawn worldwide attention in energy-based economics. Because of their uncontrollable and intermittent nature, large-scale high- power electrical energy storage (EES) system is required to increase reliability of renewable energy resources. Among various energy storage systems, lithium-ion batteries (LiBs) are considered as the most important rechargeable energy storage system. However current LiB technology is still inadequate to compete against the conventional fossil fuels and implement in the large-scale applications: Key questions are cost, energy density, cycling life, and safety issues. In the first part of this dissertation, rare-earth-free permanent magnets (PMs) are intensively studied. Our innovative approach, using polyol process, developed nanostructured cobalt carbide (NCC) consisting of an assembly of Co2C and Co3C nanoparticles. The resulting material exhibits an energy product of greater than 15.84 MGOe at room temperature before compaction, which is confirmed through vibrating sample magnetometer (VSM) and first order reversal curves (FORCs). An understanding of the formation mechanism, using in situ time-resolved x-ray absorption spectroscopy (XAS), and the correlation between phase contributions to the properties are described in detail. Our success in the development of rare-earth-free PMs may provide a cost- effective and sustainable platform for clean technology applications. Although the performances of LiBs have been drastically improved, the limit in terms of the energy density and material stability still needs to be resolved. In the second part, high-voltage cathode materials, spinel LiNi0.5Mn1. 5O4 (LNMO) and olivine LiCoPO4, are investigated to understand the influence of synthetic conditions of polyol synthesis on their electrochemical properties and structural stabilities upon cycling. High- angle annular dark field scanning transmission electron microscopy (HAADF-STEM) and X-ray photoelectron spectroscopy (XPS) allow us to determine the evolution of phase transformation accompanied with the migration of transition metal (TM) ions at the surface and electrode/electrolyte interfaces.
Patients with Dilated Cardiomyopathy and Sustained Monomorphic Ventricular Tachycardia Show Up-Regulation of KCNN3 and KCNJ2 Genes and CACNG8-Linked Left Ventricular Dysfunction

Aims Disruptions in cardiac ion channels have shown to influence the impaired cardiac contraction in heart failure. We sought to determine the altered gene expression profile of this category in dilated cardiomyopathy (DCM) patients and relate the altered gene expression with the clinical signs present in our patients, such as ventricular dysfunction and sustained monomorphic ventricular tachycardia (SMVT). Methods and Results Left ventricular (LV) tissue samples were used in RNA-sequencing technique to elucidate the transcriptomic changes of 13 DCM patients compared to controls (n = 10). We analyzed the differential gene expression of cardiac ion channels, and we found a total of 34 altered genes. We found that the calcium channel CACNG8 mRNA and protein levels were down-regulated and highly and inversely related with LV ejection fraction (LVEF) (r = –0.78, P<0.01). Furthermore, the potassium channels KCNN3 and KCNJ2 mRNA and protein levels were up-regulated and showed also a significant and inverse correlation with LVEF (r = –0.61, P<0.05; r = –0.60, P<0.05) in patients with SMVT. Conclusion A broad set of deregulated genes have been identified by RNA-sequencing technique. The relationship of CACNG8, KCNN3 and KCNJ2 with LVEF, and the up-regulation of KCNN3 and KCNJ2 in all patients with SMVT, irrespective of CACNG8 expression, suggest a significant role for these three ion flux related genes in the LV dysfunction present in this cardiomyopathy and an important relationship between KCNN3 and KCNJ2 up-regulation and the presence of SMVT.
Family Matters: Exploring the Complexities of Families of Immigrant Adolescents and Achievement in Four G8 Countries

This study analyzes the PISA 2003 data to describe the family demographic and educational process character- istics of immigrant adolescents in Canada, France, Germany and the United States of America (USA) and investigates the relationship between family circumstances and the adolescents' educational success. The four countries are among those that receive the most immigrants, have the highest percentage of foreign born among the total population, and where the discrepancy between the immigrant and native students varies dramatically between countries. Adolescent immigrant stu- dents within each country were diverse but different patterns were seen in the family characteristics between Canada, the USA and the European countries. Family demographic and educational process characteristics were related to educational achievement of adolescent immigrant students but the associations varied somewhat between countries. Results are dis- cussed in terms of relevance for educational policy, practice, and future research.
TTI model: model extracting individual's curiosity level in urban spaces

Recommendation systems have become widespread, however these systems only determine information inputted from the customers through a browser, and cannot be used when actually moving around outside. This paper presents TTI Model, a model extracting individual's curiosity level in urban spaces on their spare time by collecting behavior data from sensors. It calculates person's real time curiosity level by analyzing behavior depending on the walking speed within the city, such as window shopping or just hanging around by themselves. This paper evaluates this model with a sensor device prototype, and elaborates possibilities when understanding individuals in detail, by extracting the curiosity predicted from current behaviors using sensors.
Learning to efficiently rank

It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency requirements necessary to deploy in a production environment. In this work, we present a unified framework for jointly optimizing effectiveness and efficiency. We propose new metrics that capture the tradeoff between these two competing forces and devise a strategy for automatically learning models that directly optimize the tradeoff metrics. Experiments indicate that models learned in this way provide a good balance between retrieval effectiveness and efficiency. With specific loss functions, learned models converge to familiar existing ones, which demonstrates the generality of our framework. Finally, we show that our approach naturally leads to a reduction in the variance of query execution times, which is important for query load balancing and user satisfaction.
New Tunnel FET Charge-Trapping Memory with Large Memory Window for Ultra Low Power Operation

Charge-trapping memory needs a larger memory window for low power operation. Tunnel FET structure is one of the promising structure to achieve the large memory window due to its asymmetric structure. In the case of tunnel FET with N+ gate, there is higher electric field between P+ source and N+ gate edge compared with conventional FET structure. This high electric field enables a large amount of charge injection into the charge storage layer. In this study, we fabricated a SONOS memory with tunnel FET and evaluated write-operation characteristics.