Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of food colorants on photooxidation of lipids added to Alaska pollack Theragra chalcogramma surimi

Food colorants including rose bengal, erythrosine B, phloxine B and cochineal extracts were added to Alaska pollack Theragra chalcogramma surimi which was previously added with methyl linoleate (MeLe) or ethyl eicosapentaenoate (EPA Et). All the surimi were exposed to a 100-watt light tungsten bulb (4500 lux) in a cold room at 5°C. Four isomeric hydroperoxides, including 13-cis, trans-, 13-trans, trans-, 9-trans,cis- and 9-trans,trans-MeLe hydroperoxides, were generated by oxidation of MeLe added to surimi with and without cochineal extracts after exposure to light for several hours. For the surimi containing rose bengal, erythrosine B and phloxine B, extracted lipids contained not only the above four hydroperoxide isomers but also 12-cis,trans-as well as 10-trans,cis- MeLe hydroperoxides. EPA Et added to surimi which contained rose bengal, erythrosine B and phloxine B generated characteristic isomeric hydroperoxides, including 6-cis,trans- and 17-cis,trans-EPA Et hydroperoxides due to 1O2 mediated oxidation. The pro-oxidative potentials of four food colorants in surimi against added EPA Et or MeLe increased as the order of cochineal extracts < phloxine B<erythrosine B<rose bengal. These results clearly demonstrated that the addition of certain food colorants to surimi accelerated 1O2 mediated lipid oxidation to result in the generation of characteristic hydroperoxide isomers.
Part Detection, Description and Selection Based on Hidden Conditional Random Fields

In this paper, the problem of part detection, description and selection is discussed. This problem is crucial in the learning algorithms of part-based models, but can’t be solved well when some candidate parts are extracted from background. This paper studies this problem and introduces a new algorithm, HCRF-PS (Hidden Conditional Random Fields for Part Selection), for part detection, description, especially selection. Our algorithm is distinguished for its power to optimize multiple kinds of information at the same time, including texture, color, location and part label. Finally, we did some experiments with HCRF-PS algorithm which give good results on both virtual and real data.
Affective State Classification Using Bayesian Classifier

This paper elaborates the basic structure of a machine learning system in classifying affective state. There are several techniques in classifying the states depending on the type of input-output dataset. A proper selection of techniques is crucial in determining the success rate of the system prediction. The paper proposes a machine learning technique in classifying affective states of human subjects by using Bayesian Network (BN). A structured experimental setup is designed to induce the affective states of the subjects by using a set of audiovisual stimulants. The affective states under study are happy, sad, and nervous. Preliminary results demonstrate the ability of the BN to predict human affective state with 86% accuracy.
Trigger-Action Programming for Personalising Humanoid Robot Behaviour

In the coming years humanoid robots will be increasingly used in a variety of contexts, thereby presenting many opportunities to exploit their capabilities in terms of what they can sense and do. One main challenge is to design technologies that enable those who are not programming experts to personalize robot behaviour. We propose an end user development solution based on trigger-action personalization rules. We describe how it supports editing such rules and its underlying software architecture, and report on a user test that involved end user developers. The test results show that users were able to perform the robot personalization tasks with limited effort, and found the trigger-action environment usable and suitable for the proposed tasks. Overall, we show the potential for using trigger-action programming to make robot behaviour personalization possible even to people who are not professional software developers.
A constrained optimization algorithm for training locally recurrent globally feedforward neural networks

This paper presents a novel learning algorithm for training locally recurrent globally feedforward neural networks. The training task is formulated as a constrained optimization problem, whose objective is twofold: (i) minimization of an error measure, leading to successful approximation of the input/output mapping and (ii) optimization of an additional functional, which aims at accelerating the learning process. Simulation results on a benchmark identification problem demonstrate that, compared to other learning schemes, the proposed algorithm has enhanced qualities, including improved speed of convergence, accuracy and robustness.
In-Motes EYE: A Real Time Application for Automobiles in Wireless Sensor Networks

Wireless sensor networks have been identified as one of the key technologies for the 21st century. In order to overcome their limitations such as fault tolerance and conservation of energy, we propose a middleware solution, In-Motes. In-Motes stands as a fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort the deployed applications to run in an energy efficient manner inside the network. The proposed scheme is evaluated through the In-Motes EYE application, aiming to test its merits under real time conditions. In-Motes EYE application which is an agent based real time In-Motes application developed for sensing acceleration variations in an environment. The application was tested in a prototype area, road alike, for a period of four months.
ARPen: Mid-Air Object Manipulation Techniques for a Bimanual AR System with Pen & Smartphone

Modeling in Augmented Reality (AR) lets users create and manipulate virtual objects in mid-air that are aligned to their real environment. We present ARPen, a bimanual input technique for AR modeling that combines a standard smartphone with a 3D-printed pen. Users sketch with the pen in mid-air, while holding their smartphone in the other hand to see the virtual pen traces in the live camera image. ARPen combines the pen's higher 3D input precision with the rich interactive capabilities of the smartphone touchscreen. We studied subjective preferences for this bimanual input technique, such as how people hold the smartphone while drawing, and analyzed the performance of different bimanual techniques for selecting and moving virtual objects. Users preferred a bimanual technique casting a ray through the pen tip for both selection and translation. We provide initial design guidelines for this new class of bimanual AR modeling systems.
Building a vocabulary self-learning speech recognition system

This paper presents initial studies on building a vocabulary selflearning speech recognition system that can automatically learn unknown words and expand its recognition vocabulary. Our recognizer can detect and recover out-of-vocabulary (OOV) words in speech, then incorporate OOV words into its lexicon and language model (LM). As a result, these unknown words can be correctly recognized when encountered by the recognizer in future. Specifically, we apply the word-fragment hybrid system framework to detect the presence of OOV words. We propose a better phoneme-to-grapheme (P2G) model so as to correctly recover the written form for more OOV words. Furthermore, we estimate LM scores for OOV words using their syntactic and semantic properties. The experimental results show that more than 40% OOV words are successfully learned from the development data, and about 60% learned OOV words are recognized in the testing data. Index Terms: Vocabulary learning, OOV word detection and recovery, lexicon expansion
A Quorum RFID System Using Threshold Cryptosystem

The applications of radio frequency identification (RFID) technology have been expanded to be used in different fields that need automatic defining objects without human intervention. Recently, RFID applications have been increasingly used in the world due to its significant advantage in providing accurate information, ease of control, and reduce labour cost. Even though, researchers have reported issues with the RFID technology in terms of security and privacy which makes the RFID technology vulnerable to many threats. To satisfy such threats, many RFID based authentication schemes have been proposed. Secret sharing schemes have been also proposed for RFID system due to key management ability. Elliptical curve cryptography (ECC) has attracted researchers to apply within RFID system due to security and stability properties. This paper attempts to develop an RFID scheme that allows multi RFID tags to be authenticated within an RFID reader by using ElGamal cryptosystem or the lite version of the Cramer-Shoup cryptosystem and elliptical curve cryptography. With the threshold of the ElGamal or the lite version of Cramer-Shoup, the tag's secret information is split by using Shamir secret sharing scheme among n tags with n secrets shares such that any k quorum tags can reconstruct and decrypt the secret information. As the Shamir secret shares are to be stored on RFID tags with limited memory, the elliptic curve version of Cramer-Shoup will be used in the implementation. The security of the scheme is based on the security achievement of the Shamir secret sharing schem, ElGamal cryptosystem and the lite version of Cramer-Shoup cryptosystem.
Planar surface SLAM with 3D and 2D sensors

We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ significantly in their measurable fields of view and maximum ranges. We present experiments to evaluate the contributions of each type of sensor.
Public-Transportation-Assisted Data Delivery Scheme in Vehicular Delay Tolerant Networks

As an important component of transportation system, public transportation accounts for a considerable proportion in the whole traffic flow. The public transportation vehicles can be categorized into two categories as follows: one with determinate trajectories and schedules such as bus, tramway, and light rail; the other with flexible and variable running paths, such as taxis. In this paper, we firstly present a destination-gathering-based driving path prediction method for taxis, which can make taxis' driving paths prescient in the initial stage of carrying passengers every time. Compared with ordinary vehicles, public transportation vehicles have such features as long time running on roads and no privacy-protection need, and thus their trajectories can be opened. Through utilizing the features above, we propose a novel public-transportation-assisted data delivery scheme (PTDD) used to improve the performance of data delivery of Vehicular Delay Tolerant Networks (VDTNs). Simulation results based on a real map demonstrate the effectiveness of the proposed scheme.
Recommending investors for crowdfunding projects

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.
Regional Availability of Mechanical Embolectomy for Acute Ischemic Stroke in California, 2009 to 2010

Background and Purpose— We sought to assess the geographic proximity of patients with stroke in California to centers that performed specific threshold volumes of mechanical embolectomy procedures each year. Methods— We identified all patients who were hospitalized for acute ischemic stroke at all nonfederal acute care hospitals in California from 2009 to 2010, and all hospitals that performed any mechanical embolectomy procedures by case volume during the same period, using nonpublic data from the Office of Statewide Health Planning and Development. We computed geographic service areas around each hospital on the basis of prespecified ground transport distance thresholds. We then calculated the proportion of hospitalized patients with stroke who lived within service areas for centers that performed a low volume and high volume of mechanical embolectomy procedures each year. Results— During the 2-year study period, 15% (53/360) of hospitals performed at least 1 mechanical embolectomy for acute stroke, but only 19% (10/53) performed >10 cases per year. Most hospitalized patients with stroke (94%) lived within a 2-hour transport time (65 miles) to a hospital that performed ≥1 procedure during the 2-year period. Approximately 93% of the patients with stroke who received mechanical embolectomy lived within 20 miles from an embolectomy-capable hospital compared with 7% of those who lived >20 miles. Conclusions— In California, most patients with stroke lived within reasonable ground transport distances from centers that performed ≥1 mechanical embolectomy in a 2-year period. The probability of receiving mechanical embolectomy for acute ischemic stroke was associated with living in close geographic proximity to these hospitals.
An analysis of software interface issues for SMT processors

Abstract : Simultaneous Multithreading (SMT) has progressed from concept to commercial technology. This thesis explores three software interface issues on SMT. These issues are: operating system performance on SMT, the impact of spinning on SMT, and register file limitations to scaling SMT. We investigate these issues with a new, detailed simulation infrastructure capable of modeling all operating system activity. First, we present an analysis of operating system execution on SMT. We compare Apache's user- and kernel-mode behavior to a multi-programmed SPECInt workload. Our results demonstrate the micro-architectural impact of an OS-intensive workload on an SMT processor. The synergy between the SMT processor and Web and OS software produces a greater throughput gain over superscalar execution than seen on any previously examined workloads. Second, we study the cost of synchronization on SMT. Spinning can exact a large performance cost on SMT, because all threads share execution resources. We quantify the impact of spinning on SMT and the performance benefit of replacing spinning with SMT-lock-based code. We observe that spinning's degradation of performance ranges widely between more than 3x on multi-programmed workloads to a negligible amount on the Apache workload. Finally, we explore architectural register sharing on SMT. A significant impediment to the construction of SMTs larger than two or four contexts is register file size. We introduce and evaluate mini-threads, a simple extension to SMT that increases thread-level parallelism without the commensurate increase in register hardware. A mini-threaded SMT CPU adds additional per-thread state to each hardware context; an application executing in a context can create mini-threads that will utilize its own per-thread state, but share the context's architectural register set. Our results quantify the factors affecting performance in detail and demonstrate that mini-threads can improve performance significantly.
Nonparametric feature extraction based on Minimax distance

We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose framework to employ Minimax distances with many machine learning methods that perform on numerical data. For this purpose, first, we compute the pairwise Minimax distances between the objects, using the equivalence of Minimax distances over a graph and over a minimum spanning tree constructed on that. Then, we perform an embedding of the pairwise Minimax distances into a new vector space, such that their squared Euclidean distances in the new space equal to the pairwise Minimax distances in the original space. In the following, we study the case of having multiple pairwise Minimax matrices, instead of a single one. Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition. Finally, we perform several experimental studies to illustrate the effectiveness of our framework.
Layered interpolation for interactive avatar control

Data driven motion interpolation methods have been widely used in interactive avatar control, however, their ability to generalize example database has not been explored very well. This paper aims to discuss this issue in interactive avatar application domain. A new layered interpolation model is presented in this paper, where partial motions in limb subspace are interpolated and then final natural posture is further achieved by compensating the motion correlation between limbs. By comparing previous methods in representative interactive avatar applications such as pose editing and performative user interface, experimental results show that the proposed method outperforms previous ones.
Extracellular Vesicles Derived from Gram-Negative Bacteria, such as Escherichia coli, Induce Emphysema Mainly via IL-17A–Mediated Neutrophilic Inflammation

Recent evidence indicates that Gram-negative bacteria–derived extracellular vesicles (EVs) in indoor dust can evoke neutrophilic pulmonary inflammation, which is a key pathology of chronic obstructive pulmonary disease (COPD). Escherichia coli is a ubiquitous bacterium present in indoor dust and secretes nanometer-sized vesicles into the extracellular milieu. In the current study, we evaluated the role of E. coli–derived EVs on the development of COPD, such as emphysema. E. coli EVs were prepared by sequential ultrafiltration and ultracentrifugation. COPD phenotypes and immune responses were evaluated in C57BL/6 wild-type (WT), IFN-γ–deficient, or IL-17A–deficient mice after airway exposure to E. coli EVs. The present study showed that indoor dust from a bed mattress harbors E. coli EVs. Airway exposure to E. coli EVs increased the production of proinflammatory cytokines, such as TNF-α and IL-6. In addition, the repeated inhalation of E. coli EVs for 4 wk induced neutrophilic inflammation and emphysema, which are associated with enhanced elastase activity. Emphysema and elastase activity enhanced by E. coli EVs were reversed by the absence of IFN-γ or IL-17A genes. In addition, during the early period, lung inflammation is dependent on IL-17A and TNF-α, but not on IFN-γ, and also on TLR4. Moreover, the production of IFN-γ is eliminated by the absence of IL-17A, whereas IL-17A production is not abolished by IFN-γ absence. Taken together, the present data suggest that E. coli–derived EVs induce IL-17A–dependent neutrophilic inflammation and thereby emphysema, possibly via upregulation of elastase activity.
The time and order of appearance of ossification centers in the hamster before birth.

The time and order of appearance of the ossification centers of the skull, the axial skeleton, the appendicular skeleton, and the pectoral and pelvic girdles were studied in fetuses of the golden hamster. They were cleared with KOH and stained with alizarin red S by Dawson's method. The results obtained were as follows. 1. The first appearance of an ossification center in all the skeletons is in the clavicle at 11 days and 15 hours post coitus. 2. In the axial skeletons, the first ossification center appears in the cervical vertebral arches at 12 days and 15 hours. The vertebral bodies ossify at 13 days and 12 hours in the thoracic bodies somewhat later than the respective vertebral arches. 3. The anterior appendages begin to ossify a few hours earlier than the posterior ones. 4. The first ossification center in the skull appears in the mandible at 12 days. Before the time of birth all the skull bones except for the ethmoid begin to ossify. 5. The ossification of newborn hamsters seems immature when compared to those of the rat and the mouse in which more ossification centers are present at birth than in the hamster, especially in the manus and pes. •\Exp. Animals, 23 (3), 113•\122, 1974.
To what degree can log data profile a web searcher?

In this paper, we report ongoing efforts in a large scale research project to develop methods for profiling individual Web search engine users by leveraging data recorded in the transaction logs of search engines. Our research aim is to investigate how completely one can profile a Web searcher using log data. Taking a broad brush approach, we present an array of profiling attributes to illustrate the spectrum of user characteristics possible from log data. Specifically, we present ongoing research for determining a user 's location, geographical interest, topic of interest, level of interest, the degree of commercial intent, whether the user plans to make a purchase, and whether the user will click a link. We present the state of our ongoing research in user profiling along with that of other researchers. Our findings show that one can develop a fairly robust profile of a Web searcher using log data. We also discuss issues of determining the specific identity of the user. We conclude with a discussion of the implications for the areas of system development, online advertising, privacy, and policies concerning the use of such profiling.
Magnetoelectricity in Piezoelectric/Magnetostrictive Nanocomposites at Microwave Frequencies

We present a comprehensive study of the magnetic and microwave properties of piezoelectric BaTiO3 /magnetostrictive Ni nanocomposites (NCs), fabricated under uniaxial compression, at room temperature. In the current work, we investigated samples in function of magnetic phase content in a piezoelectric matrix and from 6 to 28 GHz using broadband ferromagnetic resonance (FMR-stripline) experiments in the microwave regime in combination with atomic (AFM) and magnetic force microscopy (MFM), X-ray diffraction (XRD), electron transport, and broadband (0.1-6 GHz) microwave spectroscopy experiments to correlate magnetization dynamics properties, electromagnetic materials parameters, and microstructural information to the magnetoelectric (ME) coupling coefficient. We provide the experimental evidence for ME effect, i.e., the effective permittivity at microwave frequencies can be controlled by an external magnetic field, which makes these nanostructures ready for microwave tunable devices, sensors, and transducers.