Miracle-dz/UniFAR-FTUs huggingface dataset:
Oligosaccharide Chitosan: Viscosity, Molecular Weight, Antibacterial Activity, and Impact of γ Radiation

Chitosan is a bioactive polymer produced from shrimp and crab shells, etc. According to VASEP (Vietnam Association of Seafood Exporters and Producers), the production of raw shrimp cultured in Vietnam was about 800,000 tons in 2018. Therefore, the shrimp processing industry has generated about 320,000 tons of wastes, including heads and shells. If wastes are not utilized and managed in proper ways, it can lead to serious environmental problems. In our study, shrimp shells were used to produce chitosan and further obtained oligochitosan for application in food preservation. The cobalt-60 radiation technology has been used to segment chitosan into oligochitosan. The radiation dose applied to chitosan solution was in the range of 25 ÷ 50 kGy and in the range of 66 ÷ 166 kGy for chitosan flakes. The results showed that the chitosan solution had higher segmental efficiency compared to that of chitosan flakes. The antibacterial activities of oligosaccharide chitosan segmented from chitosan flakes were higher than those of oligosaccharide chitosan segmented from chitosan solution. The highest antibacterial activities were observed in the oligochitosan segmented from chitosan flakes at the radiation dose of 66 kGy for all tested bacteria: E. coli O157:H7, Salmonella typhimurium, Listeria monocytogenes, Staphylococcus aureus, Bacillus subtilis. In addition, oligochitosan segmented from chitosan flakes at the radiation dose of 66 kGy had higher antibacterial activities on bacteria gram (-) than bacteria gram (+). The strongest antibacterial activities on L. monocytogenes and B. subtilis at the concentration of 0.3125%.
Structural analyses of duration dependence and persistence in job-o¤er arrival rates and wages

* We are grateful to the Department of Family and Community Services for funding this research. The views expressed in this paper are those of the authors and do not represent the views of the Minister for Family and Community Services, the Department of Family and Community Services or the Commonwealth Government. We would also like to thank the Australian Bureau of Statistics for providing access to the Survey of Employment and Unemployment Patterns in a Melbourne Datalab facility, and Je¤ Borland, Bruce Chapman, Chris Ryan and sta¤ members of the Department of Family and Community Services for helpful suggestions and comments.
Anatomy and physiology of trochanteral campaniform sensilla in the stick insect, Cuniculina impigra

ABSTRACT. Four groups of campaniform sensilla are found on the trochanter of Cuniculina impigra Tedtenbacher (Phasmidae). One of these groups can be divided into two sub‐groups. The sensilla are approximately parallel within each group or sub‐group. As sensilla with parallel orientation will respond to the same direction of shear force, each group or sub‐group of campaniform sensilla should act as one unit. When the coxa is fixed, activity in the nerve supplying the campaniform sensilla can be released by bending the femur forwards and backwards. The sensilla are sensitive to movement only in one direction. The investigated sensilla react to the stimulus with phasic‐tonic discharge patterns. The dependence of the phasic component upon the velocity of the stimulus can be described by a power function. The tonic component depends on the amplitude of the stimulus. By mechanical stimulation of individual groups of sensilla it can be shown that at least two groups of campaniform sensilla contain units which respond to bending the femur backwards. The activity of some motor neurones can be influenced by slightly bending the leg in the horizontal plane. The levator trochanteris muscle is activated when the femur is bent forwards, and the frequency of the slow extensor tibiae motor neurone is increased when the femur is bent backwards. The reaction of both muscles is phasic. There is no detectable reaction in the protractor or the retractor of the coxa or the depressor trochanteris.
Effects of Soy protein concentrate and Xanthan gum on physical properties of Silver carp (Hypophthalmichthys molitrix ) surimi

Studies were conducted to evaluate the effects of soy protein concentrate (SPC) and Xanthan gum (X) on physical properties of surimi prepared from Silver carp (Hypophthalmichthys molitrix) using various amount of soy protein concentrate (0, 5 and 10%) and xanthan gum (0, 0.25 and 0.5%) in fish paste during 3 months of frozen storage at -18oC. Results obtained from folding test showed that the gel-forming ability of the gel treated with commercial blend (4% sorbitol+4% sucrose) was higher than the gels containing soy protein concentrate and xanthan gum (P< 0.05). Xanthan gum had a harmful effect on gel formation of surimi. The lowest expressible moisture values were reported for the gels containing 10% SPC and commercial blend (4% sorbitol+4% sucrose) that indicated the gels matrix had high water holding capacity (P< 0.05). During 3 months of frozen storage at -18oC, gel-forming ability and water holding capacity of surimi were markedly decreased (P<0.05). Results of this studies demonstrated that the samples consisting commercial blend (4% sorbitol+4% sucrose) and 10 % SPC were more acceptable from the viewpoint of water holding capacity and gel forming ability, respectively.
Growth, morphology and nutritive quality of shaded Stenotaphrum secundatum, Axonopus compressus and Pennisetum clandestinum

SUMMARY The response to shade in growth and nutritive quality of buffalo grass (Stenotaphrum secundatum) was compared with that of two other stoloniferous grasses, mat grass (Axonopus compressus) and kikuyu grass (Pennisetum clandestinum). The grasses were grown outdoors in Brisbane, Australia, in soil in pots over two growing seasons in 1985/86. Treatments in the first season (Expt 1) were shading at 0 (full sun), 42, 59 and 68% with moderate N (two spaced applications of 50 kg/ha) and, in the second season (Expt 2), 0 and 59% shade with high (50 kg/ha every 2 weeks) and low (5 kg/ha every 2 weeks) N. Unusually for tropical grasses, the top yield of all species was higher under shade than in full sun, except for S. secundatum at high N in Expt 2. S. secundatum and A. compressusshowed an increase in top yield up to 68% shading whereas P. clandestinum reached a maximum at 42% shading. Shade increased shoot:root ratio and specific leaf area in all species but had little effect on leaf:stem ratio and the proportion of dead material in the tops. Stubble yield (stem bases and stolons) was reduced under shade but to a much smaller extent than root yield. S. secundatum and A. compressus had similar morphological characteristics. Their yield was lower than that of P. clandestinum in Expt 1 (autumn growth) but was similar to that of P. clandestinum in Expt 2 (summer growth). These grasses had a higher leaf:stem ratio and lower proportion of dead material in tops than P. clandestinum. Shade generally increased herbage nutritive value through an increase in N concentration and drymatter digestibility. The latter response was somewhat variable and mostly small (1–3% units). Under shade, the general trend was towards a decrease in total nonstructural carbohydrates, cell-wall content and insoluble ash, and an increase in lignin content. A. compressus was higher in digestibility and soluble carbohydrates than the other grasses. S. secundatum was not clearly superior in response to shade and its nutritive quality was lower than that of A. compressus.
What comprises a good talking-head video generation?: A Survey and Benchmark

Over the years, performance evaluation has become essential in computer vision, enabling tangible progress in many sub-fields. While talking-head video generation has become an emerging research topic, existing evaluations on this topic present many limitations. For example, most approaches use human subjects (e.g., via Amazon MTurk) to evaluate their research claims directly. This subjective evaluation is cumbersome, unreproducible, and may impend the evolution of new research. In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies. As for evaluation, we either propose new metrics or select the most appropriate ones to evaluate results in what we consider as desired properties for a good talking-head video, namely, identity preserving, lip synchronization, high video quality, and natural-spontaneous motion. By conducting a thoughtful analysis across several state-of-the-art talking-head generation approaches, we aim to uncover the merits and drawbacks of current methods and point out promising directions for future work. All the evaluation code is available at: this https URL.
Self-Supervised Human Depth Estimation From Monocular Videos

Previous methods on estimating detailed human depth often require supervised training with ‘ground truth’ depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization, and performs much better on data in the wild.
Energy-efficient topology to enhance the wireless sensor network lifetime using connectivity control

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of research has been done to expand their applications and improve their efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two metaheuristic algorithms, namely SA (Simulated Annealing) and VNS (Variable Neighbourhood Search), to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.
Text Recognition in the Wild

The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition, (2) introduce new insights and ideas, (3) provide a comprehensive review of publicly available resources, and (4) point out directions for future work. In summary, this literature review attempts to present an entire picture of the field of scene text recognition. It provides a comprehensive reference for people entering this field and could be helpful in inspiring future research. Related resources are available at our GitHub repository: https://github.com/HCIILAB/Scene-Text-Recognition.
Active Learning With Multiple Kernels

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret $\mathcal {O}(\sqrt {T})$ as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary “on the fly. ” This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data.
Quda: Natural Language Queries for Visual Data Analytics

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.
Encoding in the Dark Grand Challenge: an Overview

A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants’ deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method.
The value of taxonomy to biodiversity and agriculture

Abstract Globally, agriculture faces biotic, abiotic and legislative challenges. Impacting on all is the increasing number of species and strains of animals, plants, fungi and microorganisms that must be managed. Planning and implementing appropriate farm- and country-level actions requires accurate identification of organisms to access information although, in developing countries, many important species are neither named scientifically nor their local taxonomies and uses well documented. Improvement and adaptation of crops and livestock to changing environment and needs has long depended on genes from wild relatives. Implementing adequate biosecurity for agriculture at national and local levels may involve information on biota globally. Agricultural and neighbouring ecosystems provide poorly-characterised reservoirs for pests, natural enemies, pollinators and other organisms detrimental, beneficial or essential to productive and resilient agro-ecosystems, and the latter must be assessed and monitored for agricultural impacts under the ecosystem approach. To support these actions taxonomy must deliver timely and appropriate information to those who can use it most effectively. This is hindered by poor funding, lack of clarity on resources required and responsibility for action, and poor delivery systems. The needs of agriculture are inadequately met. Policies developed globally to address this issue are not always simple to implement, or to integrate with other agricultural and scientific policies. Solutions lie in greater integration of activities and better information flow between taxonomy, agriculture, research and policy, and provision of appropriate infrastructure and human capacity.
2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have insufficient performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method’s particular strengths are in dealing with code mixing and named entities.
Evolution in Sexual and Asexual Populations

In a simple model case, the rates of evolution in an asexual and a sexual haploid population are compared. It is shown that, if the genetic variance of the population is generated by mutation in a uniform environment, sexual reproduction confers no advantage. But if the genetic variance has arisen because selection has favored different genotypes in different environments, then sexual reproduction will accelerate adaptation to a new environment. These conclusions differ sharply from those reached by Crow and Kimura (1965). The difference arises because those authors treated mutations as unique events, whereas they are here treated as recurrent events.
POLYSACCHARIDE LEVELS OF FLUID AND PARTICULATE MICROBES

Particulate and fluid rumen microorganisms were evaluated for changes in chemical composition as affected by time after feeding. In both populations nitrogen percentage decreased and polysaccharides increased soon after feeding. Particulate microoganisms had higher polysaccharides and lower nitrogen percentages (P < 0.05) than fluid microorganisms soon after feeding. Key words: Particulate microorganisms, fluid microorganisms, intracellular polysaccharides
Mutli-task Learning with Alignment Loss for Far-field Small-Footprint Keyword Spotting

In this paper, we focus on the task of small-footprint keyword spotting under the far-field scenario. Far-field environments are commonly encountered in real-life speech applications, and it causes serve degradation of performance due to room reverberation and various kinds of noises. Our baseline system is built on the convolutional neural network trained with pooled data of both far-field and close-talking speech. To cope with the distortions, we adopt the multi-task learning scheme with alignment loss to reduce the mismatch between the embedding features learned from different domains of data. Experimental results show that our proposed method maintains the performance on close-talking speech and achieves significant improvement on the far-field test set.
Universal Time Scale for Thermalization in Two-dimensional Systems

The Fermi-Pasta-Ulam-Tsingou problem, i.e., the problem of energy equipartition among normal modes in a weakly nonlinear lattice, is here studied in two types of two-dimensional (2D) lattices, more precisely in lattices with square cell and triangular cell. We apply the wave-turbulence approach to describe the dynamics and find multi-wave resonances play a major role in the transfer of energy among the normal modes. We show that, in general, the thermalization time in 2D systems is inversely proportional to the squared perturbation strength in the thermodynamic limit. Numerical simulations confirm that the results are consistent with the theoretical prediction no matter systems are translation-invariant or not. It leads to the conclusion that such systems can always be thermalized by arbitrarily weak many-body interactions. Moreover, the validity for disordered lattices implies that the localized states are unstable.
Vid2Curve

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods, because thin structures often lack distinct point features and have severe self-occlusion. We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera. Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on. Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures. Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods.