Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of conducting carbon inks for direct writing of soft strain gauges

Soft robotics is a rapidly emerging eld, and the demand for intelligent, compliant actuators is on the rise. To this end, a strain gauge that can be 3D printed on a soft substrate is required, to match this ever-evolving eld. Conductive carbon materials, such as carbon nanotubes (CNTs) have been previously presented as such a material. In this project, a vapour grown carbon nano bre (VGCNF): gellan gum (GG) stable dispersion is produced through horn sonication, and is characterised through electrical, mechanical and rheological testing on a dry lm, as well as surface morphology. The damaging e ects of horn sonolysis is thoroughly investigated, to understand the reduction in properties caused to the production method. A codispersant, KetjenBlack (KB, a tradename for carbon black) is also added to further reduce the resistance of the dry lms. This dispersion is optimised and evaluated through the same methods, and compared to a VGCNF dispersion. To produce a exible strain gauge, a custom 3D printer was developed to perform patterned deposition of the dispersion. The dispersion is 3D printed onto a hydrogel substrate (poly-hydroxyethyl methacrylate, p-HEMA) and is found to have a gauge factor (GF) of 1.8 ± 0.2 and 0.76 ± 0.04 for one and two layers of the ink respectively, with a higher degree of repeatability with the two layer device. This project has attributed to the eld of soft robotics through the production of a 3D printed strain gauge that can be easily adapted for various substrates. It has highlighted the damage and lling e ects that sonolysis can incur, and how it should be monitored. It also contributes to the general eld of 3D printing through the production of a printer that can be used for multiple materials with varying rheological properties.
Syllable nucleus and boundary detection in noisy conditions

In this paper, utilizing the feature of the sonority, the problems of detecting the syllable nuclei and the syllable boundaries are explored. As there is minimal obstruction in the oral cavity in the production of sonorant sounds, sonority feature is used. A method is proposed for extracting the sonority proﬁle from the strength of the dominant resonance frequency using the zero time windowing method. The strength of the dominant resonance frequencies in the speciﬁc band of 500 Hz to 1700 Hz is used. The proposed method is evaluated on SVL-DD-NEWS-corpora. The results are compared with the Fourier transform method and the energy-based method of extracting the sonor-ity proﬁle. The proposed method performed better compared to other methods, especially in the presence of noise such as white, pink and babble. The method is also evaluated on TIMIT test database and the performance is on par with the current methods in detecting the syllable nuclei.
Towards real-time Body Pose Estimation for Presenters in Meeting Environments

This paper describes a computer vision-based approach to body pose estimation. The algorithm can be executed in real-time and processes low resolution, monocular image sequences. A silhouette is extracted and matched against a projection of a 16 DOF human body model. In addition, skin color is used to locate hands and head. No detailed human body model is needed. We evaluate the approach both quantitatively using synthetic image sequences and qualitatively on video test data of short presentations. The algorithm is developed with the aim of using it in the context of a meeting room where the poses of a presenter have to be estimated. The results can be applied in the domain of virtual environments.
A Numerical Study on the Wiretap Network With a Simple Network Topology

In this paper, we study a security problem on a simple wiretap network, consisting of a source node S, a destination node D, and an intermediate node R. The intermediate node connects the source and the destination nodes via a set of noiseless parallel channels, with sizes n1 and n2, respectively. A message M is to be sent from S to D. The information in the network may be eavesdropped by a set of wiretappers. The wiretappers cannot communicate with one another. Each wiretapper can access a subset of channels, called a wiretap set. All the chosen wiretap sets form a wiretap pattern. A random key K is generated at S, and a coding scheme on (M, K) is employed to protect M. We define two decoding classes at D. In Class-I, only M is required to be recovered, and in Class-II, both M and K are required to be recovered. The objective is to minimize H(K)/H(M) for a given wiretap pattern under the perfect secrecy constraint. The first question we address is whether routing is optimal on this simple network. By enumerating all the wiretap patterns on the Class-I/II (3,3) networks and harnessing the power of Shannon-type inequalities, we find that gaps exist between the bounds implied by routing and the bounds implied by Shannon-type inequalities for a small fraction (c2%) of all the wiretap patterns. The second question we investigate is the following: What is min H(K)/H(M) for the remaining wiretap patterns where gaps exist? We study some simple wiretap patterns and find that their Shannon bounds (i.e., the lower bound induced by Shannon-type inequalities) can be achieved by linear codes, which means routing is not sufficient even for the (3, 3) network. For some complicated wiretap patterns, we study the structures of linear coding schemes under the assumption that they can achieve the corresponding Shannon bounds. This paper indicates that the determination of the entropic region of six linear vector spaces cannot be sidestepped. Some subtle issues on the network models are discussed, and interesting observations are stated.
Combining planning techniques for manipulation using realtime perception

We present a novel combination of motion planning techniques to compute motion plans for robotic arms. We compute plans that move the arm as close as possible to the goal region using sampling-based planning and then switch to a trajectory optimization technique for the last few centimeters necessary to reach the goal region. This combination allows fast computation and safe execution of motion plans even when the goals are very close to objects in the environment. The system incorporates realtime sensory inputs and correctly deals with occlusions that can occur when robot body parts block the sensor view of the environment. The system is tested on a 7 degree-of-freedom robot arm with sensory input from a tilting laser scanner that provides 3D information about the environment.
The effect of raising and lowering tryptophan levels on human mood and social behaviour

Acute tryptophan depletion (ATD) studies indicate that low serotonin can lower mood and also increase aggression, although results vary somewhat between studies with similar participants. Lowering of mood after ATD is related to the susceptibility of the study participants to clinical depression, and some participants show no effect on mood. This indicates that low serotonin can contribute to lowered mood, but cannot—by itself—cause lowered mood, unless other unknown systems interact with serotonin to lower mood. Studies using tryptophan supplementation demonstrate that increased serotonin can decrease quarrelsomeness and increase agreeableness in everyday life. Social interactions that are more agreeable and less quarrelsome are associated with better mood. Thus, serotonin may have direct effects on mood, but may also be able to influence mood through changes in social behaviour. The increased agreeableness and decreased quarrelsomeness resulting from increases in serotonin will help foster congenial relations with others and should help to increase social support. As social support and social isolation have an important relationship with both physical and mental health, more research is needed on the implications of the ability of serotonin to modulate social behaviour for the regulation of mood, and for future physical and mental health.
Large Scale Inhomogeneity Versus Source Evolution -- Can We Distinguish Them Observationally?

We reconsider the issue of proving large scale spatial homogeneity of the universe, given isotropic observations about us and the possibility of source evolution both in numbers and luminosities. Two theorems make precise the freedom available in constructing cosmological models that will fit the observations. They make quite clear that homogeneity cannot be proven without either a fully determinate theory of source evolution, or availability of distance measures that are independent of source evolution. We contrast this goal with the standard approach that assumes spatial homogeneity a priori, and determines source evolution functions on the basis of this assumption.
An Ontology Based Semantic Machine Tool Selection for Multi Scale Wire EDM Processes

Manufacturing high-tech complex products which contain multi-scale complex features without outsourcing considering company's capabilities is quite difficult. Outsourcing some processes to other independent companies is a crucial step toward fabricating a product. To find the most suitable partner company several critical parameters should be considered including company machine-park, skilled personnel, infrastructure, etc. Having comprehensive information about necessary machine tool(s) to outsourcing related manufacturing process is essential. Focusing on Wire Electro Discharge Machining (WEDM) process, the objective of this paper is to introduce a platform to store and analyze information and data about part(s) and machine tools and show out coming results as a list of capable machine tools to produce the desired parts with multi-scale features.
On a Quaternionic Representation for Sp(4, R)

This work provides a quaternioinc reprsentation for real symplectic matrices in dimension four, analogous to the pair of unit quaternions representation for special orthogonal matrices. In the process of finding formulae for this representation in terms of the entries of the symplectic matrix being thereby represented, it shows how to compute the polar decomposition of a symplectic matrix without any need for spectral calculation. The technique just requires the solution of a simple 2x2 linear system. The work also characterizes symplectic, positive definite matrices in dimension four, and thus can be used in applications where the non-compact part of the symplectic group in dimension four is of utility.
Chinese and Global Distribution of H9 Subtype Avian Influenza Viruses

H9 subtype avian influenza viruses (AIVs) are of significance in poultry and public health, but epidemiological studies about the viruses are scarce. In this study, phylogenetic relationships of the viruses were analyzed based on 1233 previously reported sequences and 745 novel sequences of the viral hemagglutinin gene. The novel sequences were obtained through large-scale surveys conducted in 2008-2011 in China. The results revealed distinct distributions of H9 subtype AIVs in different hosts, sites and regions in China and in the world: (1) the dominant lineage of H9 subtype AIVs in China in recent years is lineage h9.4. 2.5 represented by A/chicken/Guangxi/55/2005; (2) the newly emerging lineage h9.4. 2.6, represented by A/chicken/Guangdong/FZH/2011, has also become prevalent in China; (3) lineages h9.3. 3, h9.4. 1 and h9.4. 2, represented by A/duck/Hokkaido/26/99, A/quail/Hong Kong/G1/97 and A/chicken/Hong Kong/G9/97, respectively, have become globally dominant in recent years; (4) lineages h9.4. 1 and h9.4. 2 are likely of more risk to public health than others; (5) different lineages have different transmission features and host tropisms. This study also provided novel experimental data which indicated that the Leu-234 (H9 numbering) motif in the viral hemagglutinin gene is an important but not unique determinant in receptor-binding preference. This report provides a detailed and updated panoramic view of the epidemiological distributions of H9 subtype AIVs globally and in China, and sheds new insights for the prevention of infection in poultry and preparedness for a potential pandemic caused by the viruses.
Spectral clustering of protein sequences

An important problem in genomics is automatically clustering homologous proteins when only sequence information is available. Most methods for clustering proteins are local, and are based on simply thresholding a measure related to sequence distance. We first show how locality limits the performance of such methods by analysing the distribution of distances between protein sequences. We then present a global method based on spectral clustering and provide theoretical justification of why it will have a remarkable improvement over local methods. We extensively tested our method and compared its performance with other local methods on several subsets of the SCOP (Structural Classification of Proteins) database, a gold standard for protein structure classification. We consistently observed that, the number of clusters that we obtain for a given set of proteins is close to the number of superfamilies in that set; there are fewer singletons; and the method correctly groups most remote homologs. In our experiments, the quality of the clusters as quantified by a measure that combines sensitivity and specificity was consistently better [on average, improvements were 84% over hierarchical clustering, 34% over Connected Component Analysis (CCA) (similar to GeneRAGE) and 72% over another global method, TribeMCL].
A Neighborhood-Based Trajectory Clustering Algorithm

Existing trajectory clustering algorithm TRACLUS uses global parameters, it can not distinguish small, close, and dense trajectory clusters from large and sparse trajectory clusters. Moreover, TRACLUS needs two input parameters and is sensitive to input parameters. To avoid the shortcomings of TRACLUS, a neighborhood-based trajectory clustering algorithm named NBTC is proposed based on the improved framework. Our key insight is that neighborhood-based local density is quite different from the absolute global density used in TRACLUS. NBTC keeps the efficient of TRACLUS and needs only one input parameter. Experimental results demonstrate that NBTC can discover trajectory clusters in arbitrary shape and different densities trajectory database effectively.
PVL: A Framework for Navigating the Precision-Variety Trade-Off in Automated Animation of Smiles

Animating digital characters has an important role in computer assisted experiences, from video games to movies to interactive robotics. A critical challenge in the field is to generate animations which accurately reflect the state of the animated characters, without looking repetitive or unnatural. In this work, we investigate the problem of procedurally generating a diverse variety of facial animations that express a given semantic quality (e.g., very happy). To that end, we introduce a new learning heuristic called Precision Variety Learning (PVL) which actively identifies and exploits the fundamental trade-off between precision (how accurate positive labels are) and variety (how diverse the set of positive labels is). We both identify conditions where important theoretical properties can be guaranteed, and show good empirical performance in variety of conditions. Lastly, we apply our PVL heuristic to our motivating problem of generating smile animations, and perform several user studies to validate the ability of our method to produce a perceptually diverse variety of smiles for different target intensities.
Cardiovascular outcomes in new users of coxibs and nonsteroidal antiinflammatory drugs: high-risk subgroups and time course of risk.

OBJECTIVE Controversy persists regarding the cardiovascular risks of treatment with selective cyclooxygenase 2 inhibitors (coxibs) and nonselective nonsteroidal antiinflammatory drugs (NSAIDs). This study was undertaken to examine, in a large group of new users of coxibs and NSAIDs, the rate of cardiovascular events, their time course, and whether baseline cardiovascular risk modified the rate ratios (RRs) for future events. METHODS This cohort study included Medicare beneficiaries who enrolled in a state-run prescription drug plan that fully covered NSAIDs and coxibs without restriction. All study patients started use of a coxib or NSAID after January 1, 1999. The primary composite end point was a hospital admission for either myocardial infarction or ischemic stroke. Predefined exposure groups included the 3 coxibs available in the US during the study period (celecoxib, rofecoxib, and valdecoxib), as well as oral formulations of diclofenac, ibuprofen, naproxen, and a composite of all other NSAIDs. We compared the rate of cardiovascular events associated with each of these agents with that in a reference group of patients who did not use NSAIDs or coxibs, but started other medications unrelated to cardiovascular risk. Daily exposure to all study drugs was assessed based on filled prescription data. A Cox proportional hazards model stratified on calendar year that included other baseline cardiovascular risk factors constituted the primary analysis. RESULTS We identified 74,838 users of NSAIDs or coxibs, and 23,532 comparable users of other drugs comprised the reference group. Adjusted models demonstrated a significant elevation in the event rate for rofecoxib (RR 1.15, 95% confidence interval [95% CI] 1.06-1.25) and a significant reduction in the rate for naproxen (RR 0.75, 95% CI 0.62-0.92). No other coxib or NSAID was associated with a significant increase or decrease in cardiovascular event rate. The increased rate associated with rofecoxib was seen in the first 60 days of use (adjusted RR 1.14, 95% CI 1.01-1.29) and thereafter (adjusted RR 1.14, 95% CI 1.02-1.28). Kaplan-Meier event curves showed a similar pattern of risk (early and persistent separation of the event curves) among long-term rofecoxib users at low or high baseline cardiovascular risk. CONCLUSION We found an increased cardiovascular event rate among users of rofecoxib, and a decreased rate with naproxen use. Other coxibs and NSAIDs did not appear to be associated with a difference in event rate compared with users of other drugs. The increase in rate associated with rofecoxib was seen within the first 60 days and persisted. There was no important modification of the event rate based on the patient's baseline cardiovascular risk.
Sparse coding in practice

The goal in sparse coding is to seek a linear basis representation where each image is represented by a small number of active coefficients. The learning algorithm involves adapting a basis vector set while imposing a low-entropy, or sparse, prior on the output coefficients. Sparse coding applied on natural images has been shown to extract wavelet-like structure [9, 4]. However, our experience in using sparse coding for extracting multi-scale structure in object-specific ensembles, such as face images or images of a gesturing hand, has been negative. In this paper we highlight three points about the reliability of sparse coding for extracting the desired structure: using an overcomplete representation projecting data into a low-dimensional subspace before attempting to resolve the sparse structure and applying sparsity constraint on the basis elements, as opposed to the output coefficients.
Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors

Traditionally, traffic monitoring requires data from traffic cameras, loop detectors, or probe vehicles that are usually operated by dedicated employees. In efforts to reduce the capital and operational costs associated with traffic monitoring, departments of transportation have explored the feasibility of using global positioning system (GPS) data loggers on their probe vehicles that are postprocessed for analyzing the traffic patterns on desired routes. Furthermore, most cell phones are equipped with embedded assisted-GPS (AGPS) chips, and if the mode of transportation the phone is in can be anonymously identified, the phones can be treated as if they are probe vehicles that are voluntarily hovering throughout the city, at minimal additional costs. Emerging cell phones known as “smartphones” are equipped with additional sensors including an accelerometer and magnetometer. The accelerometer can directly measure the acceleration values, as opposed to having acceleration values derived from speed values in conventional GPS sensors. The magnetometer can measure mode-specific electromagnetic levels. Smartphones are subscribed with roadside Internet data plans that can provide an essential platform for real-time traffic monitoring. In this article, neural network-based artificial intelligence is used to identify the mode of transportation by detecting the patterns of distinct physical profile of each mode that consists of speed, acceleration, number of satellites in view, and electromagnetic levels. Results show that newly available values in smartphones improve the mode detection rates when compared with using conventional GPS data loggers. When smartphones are in known orientations, they can provide three-dimensional (3-D) acceleration values that can further improve mode detection accuracies.
The effect of regular exercise on cognitive functioning and personality.

The effect of regular exercise on cognitive functioning and personality was investigated in 32 subjects representing 4 discrete groups based on sex and age. Before and after a 10 week exercise programme of jogging, calisthenics, and recreational activities, a test battery was administered to assess functioning in a number of domains: intelligence (WAIS Digit Symbol and Block Design); brain function (Trail-Making); speed of performance (Crossing-Off); memory and learning (WMS Visual Reproduction and Associate Learning); morale and life satisfaction (Life Satisfaction and Control Ratings); anxiety (MAACL); and depression (MAACL). Improvement was observed on several physiological parameters. ANOVA revealed significant sex and age differences on Digit Symbol and Block Design and age differences on Trail-Making, Crossing-Off, Associate Learning, and anxiety. Regardless of sex and age, significant improvement in performance was observed from pre to post-test on Digit Symbol, Block Design, Trail-Making, Crossing-Off, and on Associate Learning. In addition, an increase on health status rating (p less than .01) and decrease in anxiety were observed from pre to post-test. These data illustrate beneficial effects of exercise on certain measures of cognitive functioning and personality.
A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication

Record linkage is the process of matching records from several databases that refer to the same entities. When applied on a single database, this process is known as deduplication. Increasingly, matched data are becoming important in many application areas, because they can contain information that is not available otherwise, or that is too costly to acquire. Removing duplicate records in a single database is a crucial step in the data cleaning process, because duplicates can severely influence the outcomes of any subsequent data processing or data mining. With the increasing size of today's databases, the complexity of the matching process becomes one of the major challenges for record linkage and deduplication. In recent years, various indexing techniques have been developed for record linkage and deduplication. They are aimed at reducing the number of record pairs to be compared in the matching process by removing obvious nonmatching pairs, while at the same time maintaining high matching quality. This paper presents a survey of 12 variations of 6 indexing techniques. Their complexity is analyzed, and their performance and scalability is evaluated within an experimental framework using both synthetic and real data sets. No such detailed survey has so far been published.