Miracle-dz/UniFAR-FTUs huggingface dataset:
A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate

Abstract A major limitation in nutritional science is the lack of understanding of the nutritional intake of free-living people. There is an inverse relationship between accuracy of reporting of energy intake by all current nutritional methodologies and body weight. In this pilot study we aim to explore whether using a novel lightweight, wearable micro-camera improves the accuracy of dietary intake assessment. Doubly labelled water (DLW) was used to estimate energy expenditure and intake over a 14-d period, over which time participants (n 6) completed a food diary and wore a micro-camera on 2 of the days. Comparisons were made between the estimated energy intake from the reported food diary alone and together with the images from the micro-camera recordings. There was an average daily deficit of 3912 kJ using food diaries to estimate energy intake compared with estimated energy expenditure from DLW (P=0·0118), representing an under-reporting rate of 34 %. Analysis of food diaries alone showed a significant deficit in estimated daily energy intake compared with estimated intake from food diary analysis with images from the micro-camera recordings (405 kJ). Use of the micro-camera images in conjunction with food diaries improves the accuracy of dietary assessment and provides valuable information on macronutrient intake and eating rate. There is a need to develop this recording technique to remove user and assessor bias.
Multi-lingual unsupervised acoustic modeling using multi-task deep neural network under mismatch conditions

This Cross-lingual knowledge sharing based acoustic modeling methods are usually used in Automatic Speech Recognition (ASR) of languages which do not have enough transcribed speech for acoustic model (AM) training. Conventional methods such as IPA based universal acoustic modeling have been proved to be effective under matched acoustic conditions, while usually poorly preformed when mismatch appears between the target language and the source languages. This paper proposes a method of multi-lingual unsupervised AM training for zero-resourced languages under mismatch conditions. The proposed method includes two main steps. In the first step, initial AM of the target low-resourced language was obtained using multi-task training method, in which original source language data and mapped source language data are jointly used. In the second step, AM of the target language is trained using automatically transcribed target language data, in the way of iteratively training new AMs and adapting the initial AMs. Experiments were conducted on a corpus with 100 hours untranscribed Japanese speech and 300 hours transcribed speech of other languages. The best result achieved by this paper is 51.75% character error rate (CER), which obtains 24.78% absolute reduction compared to baseline IPA system.
Development of Shallow-Depth Soil Temperature Estimation Model Based on Thermal Response in Permafrost Area

A soil temperature estimation model for increasing depth in a permafrost area in Alaska near the Bering Sea is proposed based on a thermal response concept. Thermal response is a measure of the internal physical heat transfer of soil due to transferred heat into the soil. Soil temperature data at different depths from late spring to the early autumn period at multiple permafrost sites were collected using automatic sensor measurements. From the analysis results, a model was established based on the relationship between the normalized cumulative soil temperatures (CRCST*i,m and CST*ud,m) of two different depths. CST*ud,m is the parameter of the soil temperature measurement at a depth of 5 cm, and CRCST*i,m is the parameter of the soil temperature measured at deeper depths of i cm (i = 10, 15, 20, and 30). Additionally, the fitting parameters of the mathematical models of the CRCST*i,m–CST*ud,m relationship were determined. The measured soil temperature depth profiles at a different site were compared with their predicted soil temperatures using the developed model for the model validation purpose. Consequently, the predicted soil temperatures at different soil depths using the soil temperature measurement of the uppermost depth (5 cm) were in good agreement with the measured results.
Real-time robust and adaptive solutions to zero finding problems with uncertainty

This paper proposes extensions to a recent control Liapunov function (CLF) based method for designing dynamical systems with trajectories that converge to the zeros of a nonlinear vector function f . Specifically, the CLF design method is extended to the case when the Jacobian of the vector function can be decomposed into a known part and a partially known part, for which certain norm bounds are known. The extension of existing zero finding methods to this setting allows the consideration of functions which have singular Jacobians as well as the underdetermined case of a nonlinear system of equations in which there are more unknowns than equations. It is shown through simulation examples that methods that use dynamical systems that correspond to first order ordinary differential equations (ODEs) are less effective than those which use second order ODEs.
Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.
Divergent Roles in Arabidopsis thaliana Development and Defense of Two Homologous Genes, ABERRANT GROWTH AND DEATH2 and AGD2-LIKE DEFENSE RESPONSE PROTEIN1, Encoding Novel Aminotransferases

The disease-resistant Arabidopsis thaliana aberrant growth and death2 (agd2-1) mutant has elevated levels of the defense signal salicylic acid (SA), altered leaf morphology, and mild dwarfism. AGD2 and its close homolog ALD1 (for AGD2-LIKE DEFENSE RESPONSE PROTEIN1) encode aminotransferases that act on an overlapping set of amino acids in vitro. However, kinetic parameters indicate that AGD2 and ALD1 may drive the aminotransferase reaction in opposite directions. ALD1-deficient mutants have the opposite phenotypes from agd2-1, showing reduced SA production and increased disease susceptibility. Furthermore, ALD1 transcript levels are elevated in agd2-1 and are induced in the wild type by bacterial pathogen infection. ALD1 is responsible for some of the elevated SA content and a majority of the disease resistance and dwarfism of agd2-1. A complete knockout of AGD2 renders embryos inviable. We suggest that AGD2 synthesizes an important amino acid–derived molecule that promotes development and suppresses defenses, whereas ALD1 generates a related amino acid–derived molecule important for activating defense signaling.
Miniaturization and harmonic suppression of the branch-line coupler based on radial stubs

In this article, a compact branch-line hybrid coupler (BLC) which can suppress upper order harmonics of the conventional BLC is proposed. By adding a radial stub in the center of each branch, a compact BLC which can suppress the upper order harmonics are achieved. The proposed BLC is designed and simulated at 0.93GHz. The proposed structure is 27% of the conventional BLC, while maintaining the characteristics of the conventional BLC at the center frequency. There are two transmission zeroes at 3.6 and 5.6GHz. The third upto seventh harmonic are suppressed at least 20dB.
An achievability proof for the lossy coding of Markov sources with feed-forward

The lossy source coding problem with feed-forward link was recently introduced in [1] and the corresponding rate-distortion function was derived in [2] and [3] for stationary and ergodic sources and for arbitrary sources with memory, respectively. The achievability schemes of [2] and [3] are mainly based on codetrees. In this work, we give an alternative proof of achievability for binary asymmetric Markov sources via a simple coding scheme that utilizes optimal lossy coding for Bernoulli sources. We then generalize this coding scheme for m-ary Markov sources and show its optimality for the distortion region where the Shannon lower bound is tight.
A model for asynchronous reactive systems and its application to secure message transmission

We present a rigorous model for secure reactive systems in asynchronous networks with a sound cryptographic semantics, supporting abstract specifications and the composition of secure systems. This enables modular proofs of security, which is essential in bridging the gap between the rigorous proof techniques of cryptography and tool-supported formal proof techniques. The model follows the general simulatability approach of modern cryptography. A variety of network structures and trust models can be described such as static and adaptive adversaries, some examples of this are given. As an example of our specification methodology we provide an abstract and complete specification for Secure Message Transmission, improving on recent results by Lynch (1999), and verify one concrete implementation. Our proof is based on a general theorem on the security of encryption in a reactive multi-user setting, generalizing a recent result by Bellare et. al (2000).
An automatic singing skill evaluation method for unknown melodies using pitch interval accuracy and vibrato features

This paper presents a method of evaluating singing skills that does not require score information of the sung melody. This requires an approach that is different from existing systems, such as those currently used for Karaoke systems. Previous research on singing evaluation has focused on analyzing the characteristics of singing voice, but were not aimed at developing an automatic evaluation method. The approach presented in this study uses pitch inter- val accuracy and vibrato as acoustic features which are indepen- dent from specific characteristics of the singer or melody. The ap- proach was tested by a 2-class (good/poor) classification test with 600 song sequences, and achieved an average classification rate of 83.5%. The aim of this study is to explore a method of automatic evalua- tion of singing skills without score information. Our interest lies in identifying the criteria that human subjects use in judging the quality of singing for unknown melodies, using acoustic features which are independent from specific characteristics of the singer or melody. Such evaluation systems can be useful tools for im- proving singing skills, and also can be applied to broadening the scope of music information retrieval and singing voice synthesis. Previous work related to singing skills include those based on a control model of fundamental frequency (F0) trajectory (1), gen- eral characteristics (2, 3), as well as work on automatic discrimi- nation of singing and speaking voices (4), and acoustic differences between trained and untrained singers' voices (5, 6, 7). None of these work have gone as far as presenting an automatic evaluation method. This paper presents a singing skill evaluation scheme based on pitch interval accuracy and vibrato, which are regarded as features that function independently from the individual characteristics of singer or melody. To test the validity of these features, an experi- ment of automatic evaluation of singing performance by a 2-class classification (good/poor) was conducted. The following sections describe our approach and the exper- imental results of its evaluation. Section 2 presents discussion of features. Section 3 describes the classification experiment and its evaluation. Section 4 concludes the paper, with discussion on di- rections for future work.
Model-Based Reuse of APIs using Concern-Orientation

Despite the promises of Model-Driven Engineering (MDE) to address complexity and improve productivity, no widespread adoption has been observed in industry. One reason this paper focuses on is reuse, which is essential in modern software engineering. In the context of MDE, poor availability of reusable models forces modellers to create models from scratch. At the same time, reusable code artifacts, such as frameworks/APIs are widespread. They are an essential part when creating software. This paper presents model-based reuse of APIs, which makes use of concern-driven development (CDD) to raise the level of abstraction of APIs to the modelling level. The interface (API) of a framework is modelled using a feature model and design models for each feature enabling their reuse in MDE. Additional information is embedded, such as impacts and protocols, to assist the developer in the reuse process. We discuss how this enables reuse at the modelling level, the required tool support and future work.
Performance-aware energy optimization on mobile devices in cellular network

In cellular networks, it is important to conserve energy while at the same time ensuring users to have good transmission experiences. The energy cost can result from tail energy due to the radio resource control strategies designed in cellular networks and data transmission. Existing efforts generally consider one of the energy issues, and also ignore the adverse impact on user transmission performance due to energy conservation. In addition, many existing algorithms are based on prediction and knowledge on future traffic, which are hard to apply in a practical wireless system with dynamic user traffic and channel condition. The goal of this work is to design an efficient online scheduling algorithm to minimize energy consumption both due to tail energy and transmissions while meeting user performance expectation. We prove the problem to be NP-hard, and design a practical online scheduling algorithm PerES to minimize the total energy cost of multiple mobile applications subject to user performance constraints. We propose a comprehensive performance cost metric to capture the impacts due to task delay, deadline violation, different application profiles and user preferences. We prove that our proposed scheduling algorithm can make the energy consumption arbitrarily close to that of the optimal scheduling solution. The evaluation results demonstrate the effectiveness of our scheme and its higher performance than peers. Moreover, by supporting dynamic performance requirement by mobile users, PerES can achieve 2 times faster convergence to both the performance degradation bound and optimal energy conversation bound than those of traditional static methods. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32%-56% energy savings with different levels of user experience.
Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

This paper examines the problem of estimating vehicle position and direction, i.e., pose, from a single vehicle-mounted camera. A drawback of pose estimation using vision only is that it fails when image information is poor. Consequently, other information sources, e.g., motion models and sensors, may be used to complement vision to improve the estimates. We propose to combine standard in-vehicle sensor data and vehicle motion models with the accuracy of local visual bundle adjustment. This means that pose estimates are optimized with regard not only to observed image features but also to a single-track vehicle model and standard in-vehicle sensors. The described method has been experimentally tested on challenging data sets at both low and high vehicle speeds as well as a data set with moving objects. The vehicle motion model in combination with in-vehicle sensors exhibit good accuracy in estimating planar vehicle motion. Results show that this property is preserved, when combining these information sources with vision. Furthermore, the accuracy obtained from vision-only in direction estimation is improved, primarily in situations in which there are few matched visual features.
Estimating Human Pose with Flowing Puppets

We address the problem of upper-body human pose estimation in uncontrolled monocular video sequences, without manual initialization. Most current methods focus on isolated video frames and often fail to correctly localize arms and hands. Inferring pose over a video sequence is advantageous because poses of people in adjacent frames exhibit properties of smooth variation due to the nature of human and camera motion. To exploit this, previous methods have used prior knowledge about distinctive actions or generic temporal priors combined with static image likelihoods to track people in motion. Here we take a different approach based on a simple observation: Information about how a person moves from frame to frame is present in the optical flow field. We develop an approach for tracking articulated motions that "links" articulated shape models of people in adjacent frames through the dense optical flow. Key to this approach is a 2D shape model of the body that we use to compute how the body moves over time. The resulting "flowing puppets" provide a way of integrating image evidence across frames to improve pose inference. We apply our method on a challenging dataset of TV video sequences and show state-of-the-art performance.
Resveratrol and Coumarin: Novel Agricultural Antibacterial Agent against Ralstonia solanacearum In Vitro and In Vivo

Bacterial wilt is a destructive disease caused by the phytopathogen Ralstonia solanacearum (R. solanacearum), which is widely found in various tobacco-growing areas all over the world. Botanical bactericidal substances have gradually emerged as a hot topic in modern pesticide research. In this study, the antibacterial activities of two phytochemicals (resveratrol and coumarin) against R. solanacearum and their in vivo and in vitro efficacy for controlling tobacco bacterial wilt were evaluated. We rule out significant biological effects of both phytochemicals using transmission electron microscope (TEM) and fluorescence microscope, which suppressed the growth of R. solanacearum. Furthermore, we demonstrated that the toxicity mechanisms mainly involved damaging bacterial cell membrane and preventing swarming motility and biofilm formation. A further pot experiment demonstrated that coumarin and resveratrol significantly inhibited early adhesion and colonization of R. solanacearum in tobacco plants and the corresponding control efficacies were 68% and 85% after incubation for 13 days, respectively. The findings of this study suggest that both resveratrol and coumarin have potential as non-toxic antimicrobial strategies for controlling tobacco bacterial wilt disease.
GNAQ and GNA11 mutations in uveal melanoma

G-protein-coupled receptors signal through heterotrimeric G proteins, G&agr; and G-&bgr;&ggr;, to manage numerous aspects of physiologic homeostasis. Many neoplastic processes harbor alterations in G-protein-coupled receptors and/or G-&agr; proteins, best exemplified by recurrent activating mutations in GNAQ or GNA11 in uveal melanomas. This review will discuss the multiple activated signaling targets downstream of mutant GNAQ and GNA11 in uveal melanoma, including MEK, PI3-kinase/Akt, protein kinase C, and YAP. This knowledge has led to the rapid expansion of clinical trials that are specific to patients with uveal melanoma and promises future breakthroughs in therapies.
Double Relief with progressive weighting function

Feature weighting algorithms try to solve a problem of great importance nowadays in machine learning: The search of a relevance measure for the features of a given domain. This relevance is primarily used for feature selection as feature weighting can be seen as a generalization of it, but it is also useful to better understand a problem's domain or to guide an inductor in its learning process. Relief family of algorithms are proven to be very effective in this task. On previous work, a new extension was proposed that aimed for improving the algorithm's performance and it was shown that in certain cases it improved the weights' estimation accuracy. However, it also seemed to be sensible to some characteristics of the data. An improvement of that previously presented extension is presented in this work that aims to make it more robust to problem specific characteristics. An experimental design is proposed to test its performance. Results of the tests prove that it indeed increase the robustness of the previously proposed extension.
The Role of Pastor Support in a Faith-Based Health Promotion Intervention

Pastor support has been viewed as an integral part of successful faith-based health promotion programs; however, few studies have systematically studied these relationships. This study examined associations between pastor support and program-related variables among African American churches taking part in a physical activity and dietary intervention. Results showed that some pastor support–related variables were associated with participant recruitment, retention, and implementation of study requirements but not to changes in health behavior outcomes. Much work remains in how to conceptualize and measure pastor support. A better understanding of the pastor's role may assist in developing more effective faith-based programs.
An Opportunistic Relay Method for Increasing Throughput in Multirate IEEE 802.11 Wireless LAN

In this letter, we demonstrate a Relay-based Adaptive Auto Rate (RAAR) protocol that can find a suitable relay node for data transmission between transmitter and receiver, and can dynamically adjust its modulation scheme to achieve the maximal throughput of a node according to the transmission distance and the channel condition. Evaluation results show that this scheme provides significant throughput improvement for nodes located at the fringe of the AP's transmission range, thus remarkably improving overall system performance.
Supporting the Transformative Process: Experiences of Cancer Patients Receiving Integrative Care

Purpose . The purpose of this study is to describe the essential features of the transformative experience among people living with cancer who are seeking integrative care and to identify factors supporting this process. It is hoped that after establishing the nature and meaning of this change or shift, one will better understand what is most meaningful in terms of providing appropriate care and support to patients seeking integrative care. Study Design . An interpretational, qualitative approach guided sampling, data collection, and analysis with 11 individuals. A purposeful sample was drawn from selected integrative care facilities according to sociodemographics and type of cancer. Due to the complexity of this subject, second interviews were conducted with 5 participants to enhance the richness and validity of the data. Results . The experience of transformation is a dynamic 4-stage process in which participants learned about themselves and became more aware of who they are and how they relate to the world. Participants found that 4 dimensions of integrative medicine played a fundamental role in supporting this process. These dimensions include (1) having access to a range of appropriate therapies to support individual journeys, (2) care that focuses on one’s overall well-being, (3) control over cancer management, and (4) developing healing relationships with care providers. Conclusion . Although practitioners may not be able to create transformative experiences for patients, they may be able to establish and maintain conditions that support this process.