Miracle-dz/UniFAR-FTUs huggingface dataset:
Provisioning Australian Seed Carrot Agroecosystems with Non-Floral Habitat Provides Oviposition Sites for Crop-Pollinating Diptera

Simple Summary Planting a diverse array of flowers in crop field settings can support insect crop pollinators, as many pollinating insects feed on floral pollen and nectar as adults. Although adult pollinating flies that feed on floral resources will also be supported by flower plantings, fly larvae rarely feed on floral nectar and pollen. Here, we deployed pools filled with habitats (decaying plant materials, soil, water) that pollinating fly larvae are known to feed on in seed carrot fields in an attempt to attract flies to lay eggs. We found many fly eggs and larvae within the pools after 12 to 21 days. More eggs were laid on decaying plant stems and carrot roots, compared to other locations (e.g., on decaying carrot flowers, leaves, etc.) within the pools. The habitat pools we trialed within the seed carrot fields could be a quick and easy way to support the reproduction of beneficial fly pollinators. These results can be used to support future studies to examine the effect of habitat pools in crop fields on the number of flies that visit crop flowers. Abstract The addition of floral resources is a common intervention to support the adult life stages of key crop pollinators. Fly (Diptera) crop pollinators, however, typically do not require floral resources in their immature life stages and are likely not supported by this management intervention. Here, we deployed portable pools filled with habitat (decaying plant materials, soil, water) in seed carrot agroecosystems with the intention of providing reproduction sites for beneficial syrphid (tribe Eristalini) fly pollinators. Within 12 to 21 days after the pools were deployed, we found that the habitat pools supported the oviposition and larval development of two species of eristaline syrphid flies, Eristalis tenax (Linnaeus, 1758) and Eristalinus punctulatus (Macquart, 1847). Each habitat pool contained an average (±S.E.) of 547 ± 117 eristaline fly eggs and 50 ± 17 eristaline fly larvae. Additionally, we found significantly more eggs were laid on decaying plant stems and carrot roots compared to other locations within the pool habitat (e.g., on decaying carrot umbels, leaves, etc.). These results suggest that deploying habitat pools in agroecosystems can be a successful management intervention that rapidly facilitates fly pollinator reproduction. This method can be used to support future studies to determine if the addition of habitat resources on intensively cultivated farms increases flower visitation and crop pollination success by flies.
In Vitro Study of the Biological and Physical Properties of Dual-Cure Resin-Modified Calcium Silicate-Based Cement

Background: The aim of the present study was to compare the biological and mechanical properties of a novel dual-cure, resin-modified calcium silicate material, Theracal PT® (TP), with those of Theracal LC® (TL) and BiodentineTM (BD). Methods: The cell counting kit-8 was used on human dental pulp cells to test cell the viability of the three materials. Antibacterial activity of TP, TL, and BD against Enterococcus faecalis was investigated under anaerobic conditions. The ability of the materials to support odontogenic differentiation was studied by examining the relative gene expression of osteocalcin (OCN), osteopontin (OPN), and Collagen I (ColI) using real-time polymerase chain reaction. For mechanical property tests, microhardness was evaluated using the Vickers microhardness (VHN) test, and the bond strength to the resin was evaluated using a shear bond test machine. Results: There was no significant difference in cell viability between TL and TP after 48 h, and BD showed the highest cell viability, while TP showed the highest antibacterial effect. At the 12-h time point, there was no significant difference in ColI and OCN expression between BD and TP, but TP showed a higher expression of OPN than BD. However, at the 48-h time point, ColI and OCN showed higher levels of expression for BD than for TP and TL. At the same time point, only OPN had a higher diffusion for TP than for BD. TP demonstrated a VHN of approximately 30–35. This value was higher than that of TL and lower than that of BD. In contrast to VHN, the shear bond strength to resin was significantly higher for TL and TP than for BD. Conclusion: TP showed lower biocompatibility than BD but higher OPN expression and antibacterial effects than BD and TL. TP showed higher shear bond strength than BD and higher VHN than TL and BD at the 24-h time point.
Adapting a Self-Supervised Speech Representation for Noisy Speech Emotion Recognition by Using Contrastive Teacher-Student Learning

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when deployed on real-world applications. For adaptation, it is essential to balance between acquiring new knowledge from noisy speech and keeping the previous knowledge acquired during the pre-training and fine-tuning of the model. Therefore, we propose a contrastive teacher-student learning framework to retrain a self-supervised speech representation model for noisy SER. To keep the knowledge of the original model, we minimize the root mean square error between the clean embeddings from the original SER model and the noisy embeddings from the retrained model. To acquire the discriminative knowledge in the target noisy condition, we also minimize the InfoNCE loss by selecting the corresponding clean embedding as a positive sample and other noisy embeddings with different emotional labels as negative samples. Our experiment with the clean and noisy version of the MSP-Podcast corpus demonstrates that the contrastive teacher-student learning framework can significantly improve the performance of the model only trained with the clean speech in the target noisy condition for all the emotional attributes.
Lymphoscintigraphy versus Indocyanine Green Lymphography—Which Should Be the Gold Standard for Lymphedema Imaging?

Early detection and treatment can slow the progression of lymphedema. To diagnose lymphedema in the subclinical phase, a sensitive imaging modality is required. Radioisotope-based lymphoscintigraphy (LSG) has been the “gold standard” for a century. Indocyanine green lymphography (ICGL) is being used at our institute for diagnosing and grading all lymphedema patients. In this study, ICGL disease detection rate was compared to that of LSG. Chart review of all patients who presented for lymphedema consult between February 2020 and April 2022 was conducted. Patients who underwent both LSG and ICG for extremity edema in symptomatic/asymptomatic limbs were included. A total of 50 limbs in 23 patients met the inclusion criteria. Of those, 37 were symptomatic and 13 were asymptomatic. LSG detected lymphatic dysfunction in 26/37(70%) of the symptomatic limbs while ICG detected the same in 37/37(100%) limbs (p < 0.01). In the asymptomatic group, LSG detected the disease in 1/13(8%) limbs while ICG detected lymphatic dysfunction in 8/13 (62%) limbs (p < 0.01). LSG missed symptomatic limbs 30% of the time, whereas ICG did not miss any symptomatic limbs (p < 0.01). LSG missed asymptomatic disease 54% of the time (p < 0.01) compared to ICG. In conclusion, ICG lymphography was determined to have a higher lymphatic dysfunction detection rate compared to LSG.
Framework based on conformal predictors and power martingales for detection of fixed football matches

One of the difficult problems that arises during football competitions is match-fixing. In terms of negative effect, such shameful phenomena are commensurate with the problem of doping. This paper has analyzed known methods for the possible detection of match-fixing, including sociological analysis of participants in match-fixing, methods for predicting the outcome of the match, analysis of bets and performance of the player or team during the match. It is noted that the assessment of match-fixed results in the considered methods is carried out based on the analysis of a large amount of data. But such information is not always available. Given the insufficient formalization of the problem area, it is relevant to conduct research that does not require a large amount of non-publicly available data but, at the same time, makes it possible to effectively identify potentially suspicious matches regarding a fixed result. The description of the input data is formalized in the form of a data structure containing a chronological history of the results of football seasons, the ranking of teams and matches of the season depending on the overall result of the teams in the season. A method for detecting suspicious football matches with a fixed result has been built using conformal predictors and power martingales within which a new measure of non-conformity has been introduced to determine atypical football matches. To obtain a generalization of the statistics of atypical matches, a power submartingale was used. Evaluation of the effectiveness of the developed method for detecting suspicious football matches was carried out based on precision and recall of the classification metrics using data on the 2013–2014 season of the French II League. The quality of work of the developed method reaches 85 % in terms of precision metric, 96 % in terms of recall metric, and 0.853 in terms of metric F1.
Robustness and Convergence of Mirror Descent for Blind Deconvolution

We revisit the Blind Deconvolution problem with a focus on understanding its robustness/convergence properties. Interest in provable robustness to noise and other perturbations is growing – from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in mission critical applications. Further, many blind deconvolution methods based on deep architectures internally make use of or optimize the basic formulation, so a clearer understanding of how this sub-module behaves and when it can be solved is a first order requirement. We derive new theoretical insights for the core blind deconvolution problem. The algorithm that emerges has nice convergence guarantees and is provably robust in a sense we formalize in the paper. Proof of concept implementations play out well across standard datasets.
Could the BubbleView Metaphor be used to Infer Visual Attention on 3D Graphical Content?

Understanding the deployment of human gaze on 3D graphical objects is of critical importance in order to propose rich and complex 3D environments without strong latency nor rendering constraints. However, the data needed to study this gaze deployment can be costly and difficult to obtain, especially in the context of the Covid-19 pandemic where in-lab experiments are strongly discouraged. In order to alleviate these issues, we propose to use the BubbleView metaphor as a way of crowdsourcing visual attention data on 3D graphical content. In this paper, we question the adequacy of this method to provide a reliable proxy for visual attention in the context of 3D graphical objects. Moreover, we show how data obtained in this manner can be used to train visual saliency models, with only a slight tradeoff in performances compared to the use of ground-truth eye-tracking data.
Inspiratory versus expiratory incentive spirometry: A randomised control trial study protocol

Background Respiratory impairments refer to a reduction in pulmonary function, which may adversely affect an individual’s health. Incentive spirometry is a technique designed to assist patients in achieving a pre-set airflow volume; the volume is determined from predicted values or baseline measurements. Our study aims to assess the effect of incentive spirometry on respiratory impairments. Method Fifty-four patients aged 40 years and above with obstructive, restrictive or mixed respiratory impairments attending the respiratory clinic at the Lagos State University Teaching Hospital, Ikeja (LASUTH), will be recruited and assigned to three groups of 18 participants based on the class of respiratory impairment. Participants in each category of respiratory impairment will be subdivided into three groups. A final group of six participants per class of impairment will participate in the experiment. Our study will be a double-blind, randomised control trial with two intervention groups and one parallel placebo control group. Pulmonary function will be assessed before and after every procedure while the six-minute walk test (6MWT), Medical Research Council dyspnoea scale and the Pulmonary Functional Status and Dyspnea Questionnaire-Modified will be assessed fortnightly during our study. Data will be analysed using descriptive and inferential statistics and a repeated MANOVA; p < 0.05. Discussion The outcome of our study may reveal the effect of inspiratory and expiratory incentive spirometry on obstructive, restrictive or mixed respiratory impairments. Conclusion Our study may contribute to the body of knowledge on pulmonary rehabilitation. Clinical implication Our study results may indicate if inspiratory incentive spirometry or expiratory incentive spirometry is better suited for the treatment of the respiratory impairment. Trial Registration www.pactr. org: PACTR202005904039357
Robotics: Five Senses plus One - An Overview

Robots can be equipped with a range of senses to allow them to perceive and interact with the world in a more natural and intuitive way. These senses can include vision, hearing, touch, smell, and taste. Vision allows the robot to see and recognize objects and navigate its environment. Hearing enables the robot to recognize sounds and respond to vocal commands. Touch allows the robot to perceive information about the texture, shape, and temperature of objects through the sense of touch. Smell enables the robot to recognize and classify different odors. Taste enables the robot to identify the chemical composition of materials. The specific senses used in a robot will depend on the needs of the application, and many robots use a combination of different senses to perceive and interact with the environment. This paper reviews the five senses used in robots, their types, how they work, and other related information, while also discussing the possibility of a Sixth Sense.
Multi-Stream Facial Adaptive Network for Expression Recognition from a Single Image

Facial expression recognition from a single image has potential applications in fields including human-computer interaction and medical diagnosis. Most recent methods use deep neural networks to directly learn from a roughly cropped facial image which is usually detected from a whole image by face detection algorithms. We observe that unrelated surrounding regions in the rough facial image prevent deep neural networks from learning facial-related discriminate features. To solve this problem, we present a Facial Adaptive Network (FAN) which is able to adaptively select an interest region from the given facial image, thus suffering less from the effect of unrelated regions. Based on the selected interest region, we further apply the self-attention mechanism to learn discriminate facial features. Moreover, we introduce a multi-stream FAN (ms-FAN) that learns richer facial features from multiple interest regions that are selected from pose-augmented facial images. Extensive experiments on Oulu-CASIA, CK+, and RAF-DB datasets consistently verify the effect of our proposed MS-FAN by achieving comparable results with state-of-the-art methods. Our code is available at https://github.com/zhangbc12/DAtt-ViT.
Uniform Expression of Solutions of Ill-posed Problems in Surveying Adjustment and the Fitting Method by Selection of the Parameter Weights

By analysis and comparison of several mathematical models in surveying adjustment, it could be found that their solution expressions may be unified in form. The unified formula of these solutions could be derived based on the principle of Tikhonov regularization.Due to the inspiration of Quasi-Stable adjustment, the new idea is put forward by author of the paper to solve ill-posed problems, that is named "fitting method by selection of the parameter weights". It is emphasized that a specific analysis of the parameters should be performed based on the specific situation when an ill-posed problem is considered to be solved. The results in accord with objective practice will be obtained by using the uniform formula of the solutions as a reasonable weight matrix or restricted condition about the unknown parameters is constructed. In the final section of the paper, two examples are introduced to illustrate the effect of the new method.
Identifiable Bounded Component Analysis Via Minimum Volume Enclosing Parallelotope

In this paper, we revisit bounded component analysis (BCA) and formulate it as a geometric problem of finding the minimum volume enclosing parallelotope (MVEP) of a set of data points in the Euclidean space. A parallelotope is an affine transformation of the standard box, also known as the L∞-norm ball. An immediate benefit of the novel formulation is that the bounds on the supports of the latent components can be arbitrary, unlike most existing BCA works that assume the bounds are symmetric around zero. The main contribution is that the MVEP solution exactly recovers the latent components, up to the inherent (and inconsequential) permutation, shift, and scaling ambiguities, if the groundtruth components satisfy a so-called "sufficiently scattered" condition in the standard box. This is a great improvement to the existing result that requires all vertices of the box are contained in the data set, which requires exponentially many data points, or that of ICA, which essentially requires infinite amount of data points to guarantee exact recovery. We also present a new learning algorithm to solve the (NP-hard) MVEP problem based on Frank-Wolfe, and show numerically that the performance is surprisingly effective.
Non-binary functional synthesis using ACO-based heuristic

Multiple-valued logic (MVL) synthesis is a problem that has attracted increased interest in recent years. The MVL synthesis problem is more involved compared to its binary counterpart. The search space for finding optimal MVL functional synthesis is enormous. Conventional deterministic methods for MVL functional synthesis are prohibitively expensive, indicating an eminent need for the use of iterative heuristic-based synthesis techniques. In this paper, an ant colony optimization (ACO) based heuristic algorithm for synthesis of MVL functions is proposed. The algorithm mimics the ants' behaviour in the real world. Real ants are found to be able to select the shortest path between their nest and food sources in the existence of alternate paths, or even hurdles between the two. The proposed ACO algorithm uses some of the known real ant techniques in finding the shortest paths to the (near) optimal number of product terms that can cover the minterms of a given MVL function. The proposed algorithm is tested using 50 000 randomly generated 2-variable 4-valued functions. The results obtained using the proposed approach show that the proposed approach outperforms existing direct cover (DC) techniques in terms of the average number of product terms (implicants) required to synthesize a given MVL function.
Utilizing Wav2Vec In Database-Independent Voice Disorder Detection

Automatic detection of voice disorders from acoustic speech signals can help to improve reliability of medical diagnosis. However, the real-life environment in which speech signals are recorded for diagnosis can be different from the environment in which the detection system’s training data was originally collected. This mismatch between the recording conditions can decrease detection performance in practical scenarios. In this work, we propose to use a pre-trained wav2vec 2.0 model as a feature extractor to build automatic detection systems for voice disorders. The embeddings from the first layers of the context network contain information about phones, and these features are useful in voice disorder detection. We evaluate the performance of the wav2vec features in single-database and crossdatabase scenarios to study their generalizability to unseen speakers and recording conditions. The results indicate that the wav2vec features generalize better than popular spectral and cepstral baseline features.
Learning Cross-Modal Audiovisual Representations with Ladder Networks for Emotion Recognition

Representation learning is a challenging, but essential task in audiovisual learning. A key challenge is to generate strong cross-modal representations while still capturing discriminative information contained in unimodal features. Properly capturing this information is important to increase accuracy and robustness in audiovisual tasks. Focusing on emotion recognition, this study proposes novel cross-modal ladder networks to capture modality-specific information while building strong cross-modal representations. Our method utilizes representations from a backbone network to implement unsupervised auxiliary tasks to reconstruct intermediate layer representations across the acoustic and visual networks. The skip connections between the cross-modal encoder and decoder provide powerful modality-specific and multimodal representations for emotion recognition. Our model on the CREMA-D corpus achieves high performance with precision, recall, and F1 scores over 80% on a six-class problem.
Prevalence of Polypharmacy of Older People in a Large Brazilian Urban Center and its Associated Factors

Background: With the aging population comes greater risks associated with polypharmacy, a significant public health problem. Objective: This study aimed to identify the prevalence of polypharmacy and its associated factors through Comprehensive Geriatric Assessment (CGA) among older adults treated in primary health care (PHC) in a large Brazilian urban center. Methods: We conducted a cross-sectional study with a random sampling of 400 older adults using primary health care. Polypharmacy was defined as the cumulative use of five or more daily medications. An assessment of a sociodemographic and health survey, fear of falling, and physical disabilities affecting activities of daily living and instrumental activities of daily living was conducted. Results: The mean age was 75.23 (SD: 8.53) years. The prevalence of polypharmacy and hyperpolypharmacy was 37% (n = 148) and 1% (n = 4), respectively. The adjusted logistic regression showed that participants with chronic non-communicable diseases (CNCDs) (OR = 9.24; p = 0.003), diabetes (OR = 1.93; p = 0.003), and obesity (OR = 2.15; p = 0.005) were associated with a greater propensity to use polypharmacy. Conclusion: Our results show that older adults with CNCDs, diabetes, and obesity were more likely to use polypharmacy. The results reinforce the importance of using CGA in clinical practice in PHC.
Perimeter-based Fourier analysis: a new morphometric method applied to the trilobite cranidium

A new Fourier method is presented to quantify shapes too complex to be described by conventional polar Fourier analysis. The length along a closed curve serves as the independent variable. The centroid of the curve is determined and for each point on the curve two different dependent variables are defined, based on: 1) the angle defined by the starting point, the centroid, and the point on the curve; and 2) the radial distance from the centroid to the point on the curve. The method is used to describe the trilobite cranidium, and 12 harmonic coefficients are found to summarize 99 percent of the shape information contained in the cranidial outline. In an application to trilobite evolution during the Cambrian and Ordovician, it is found that higher taxa of trilobites become progressively more distinct morphologically. This result is in agreement with previous qualitative observations, and is attributable to an increase in morphologic dispersion among higher taxa, but not to a decrease in morphologic dispersion within higher taxa.