Miracle-dz/UniFAR-FTUs huggingface dataset:
A model to predict the occurrence of Asiatic black bears at the municipal level using mast production data

Abstract: This study analyzed how 3 Fagaceae species (Fagus crenata, Quercus crispula, and Q. serrata) affect the occurrence of Asiatic black bears (Ursus thibetanus) in and around residential areas from generalized linear mixed models based on monitoring data of bear occurrence and masting over 14 years (2005–2018) in municipalities in Hyogo Prefecture, Japan. The constructed models suggest that it is important to consider the effects of mast production by multiple dominant Fagaceae species, not only within a municipality but also in the surrounding area, to predict bear occurrence with practical accuracy at the municipal level. The accuracy of the predictive model increased as the number of Fagaceae species in the model increased. Models differed among municipalities in their accuracy to predict bear occurrence, which was related to the effect of F. crenata mast production, which was correlated with the proportion of F. crenata forests in each municipality. I suggest that the accuracy of prediction at the municipal level depended on the effect of F. crenata mast production because the spatial and temporal synchrony of F. crenata mast production was stronger than that of other species. To take preventive action to reduce conflict between humans and bears, it would be useful to construct a model to predict bear occurrence at the municipal level based on monitoring data of mast production in multiple Fagaceae species.
Benthic Community Responses to the Filling of a Hurricane-Induced Barrier Island Inlet

ABSTRACT Rodil, I.F. ; Long, Z.T. ; Fegley, S.R. ; Rodriguez, A.B. , and Peterson, C.H. , 2021. Benthic community responses to the filling of a hurricane-induced barrier island inlet. Journal of Coastal Research, 37(3), 601–610. Coconut Creek (Florida), ISSN 0749-0208. In September 2003, Hurricane Isabel created a new tidal inlet through Hatteras Island, a barrier island of North Carolina (U.S.A.). Within two months, the breach was filled with sand dredged from nearby areas. Subsequent ecological response of the benthic macroinvertebrate communities to the filling differed on shores on opposite sides of the island. Thus, the filled area on the high-energy sandy beach exhibited convergence in community composition and abundance with a nearby reference area within 30 months, whereas the filled area on the lower-energy sound shoreline still possessed a depauperate benthos. The high volume of long-shore sediment transport documented along the ocean beaches of Hatteras Island probably acted quickly to transport benthic macrofauna to the fill along with natural beach sediments, and thereby activating a rapid ecological response of the beach community. The beach community was dominated by annual macroinvertebrate species, which could account for mixing from nearby areas in the rapid macrofauna community response. In contrast, the Pamlico Sound shoreline experiences only local wind-driven waves and modest long-shore currents likely insufficient to import macrobenthos readily. Infrequent passive transport of macrobenthos and persistence of defaunated sediment habitats nearby probably slowed the macrofauna community response on the sound-shore side. Management intervention through habitat restoration would be recommended in the less dynamic sound shore environment compared to the high-energy ocean beach where ecological responses of the invertebrate community occurred rapidly.
The Effect of Some Coating Materials on the amount of Leaching Monomer from Poly Methyl Methacrylate Denture Base

Aims: to evaluate the effect of three coating materials on the amount of residual monomer leaching from acrylic resin denture base. Materials and methods: twenty specimens of a heat–cured acrylic resin denture base were prepared at dimensions of 30 x 10 x 1.5 mm length, width and thickness; respectively. These specimens were divided into four groups: control (in which the denture base left uncoated), monopoly coated, grape seeds oil coated and olive oil coated groups. For monomer leaching analysis, each specimen was put in a glass tube containing 10 ml of deionized water that was changed daily. Monomer levels were measured by spectrophotometer at 254 nm, every 24 hour for 7 days analytic period. The results were expressed as a percentage of leached monomer mass with respect to the weight of specimen. Results: statistical analysis showed that there was a significant difference in the percentage of residual monomer leaching in uncoated and monopoly coated groups; and insignificant difference in grape and olive oils coated groups. However, there was a daily decrease in monomer leaching for all groups. Grape and olive oils coated groups, showed lower leached residual monomer during the whole experimental period than the uncoated and monopoly coated specimens, the daily reduction of monomer leaching was sharp in the uncoated (control) and monopoly coated groups, and it was gradual and slow in oils coated groups. Conclusions: the using of natural oils (grape and olive oils), was more beneficial in reduction of residual monomer leaching, than the resin coating materials (monopoly) in comparison to uncoated acrylic resin denture base.
Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.
Incremental multi-view object detection from a moving camera

Object detection in a single image is a challenging problem due to clutters, occlusions, and a large variety of viewing locations. This task can benefit from integrating multi-frame information captured by a moving camera. In this paper, we propose a method to increment object detection scores extracted from multiple frames captured from different viewpoints. For each frame, we run an efficient end-to-end object detector that outputs object bounding boxes, each of which is associated with the scores of categories and poses. The scores of detected objects are then stored in grid locations in 3D space. After observing multiple frames, the object scores stored in each grid location are integrated based on the best object pose hypothesis. This strategy requires the consistency of object categories and poses among multiple frames, and thus it significantly suppresses miss detections. The performance of the proposed method is evaluated on our newly created multi-class object dataset captured in robot simulation and real environments, as well as on a public benchmark dataset.
Exploring Deep Reinforcement Learning for Task Dispatching in Autonomous On-Demand Services

Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.
Hungry networks: 3D mesh reconstruction of a dish and a plate from a single dish image for estimating food volume

Dietary calorie management has been an important topic in recent years, and various methods and applications on image-based food calorie estimation have been published in the multimedia community. Most of the existing methods of estimating food calorie amounts use 2D-based image recognition. On the other hand, in this paper, we would like to make inferences based on 3D volume for more accurate estimation. We performed 3D reconstruction of a dish (food and plate) and a plate (without foods), from a single image. We succeeded in restoring the 3D shape with high accuracy while maintaining the consistency between a plate part of an estimated 3D dish and an estimated 3D plate. To achieve this, the following contributions were made in this paper. (1) Proposal of "Hungry Networks," a new network that generates two kinds of 3D volumes from a single image. (2) Introduction of plate consistency loss that matches the shapes of the plate parts of the two reconstructed models. (3) Creating a new dataset of 3D food models that are 3D scanned of actual foods and plates. We also conducted an experiment to infer the volume of only the food region from the difference of the two reconstructed volumes. As a result, it was shown that the introduced new loss function not only matches the 3D shape of the plate, but also contributes to obtaining the volume with higher accuracy. Although there are some existing studies that consider 3D shapes of foods, this is the first study to generate a 3D mesh volume from a single dish image.
Attention-constraint facial expression recognition

To make full use of existing inherent correlation between facial regions and expression, we propose an attention-constraint facial expression recognition method, where the prior correlation between facial regions and expression is integrated into attention weights for extracting better representation. The proposed method mainly consists of four components: feature extractor, local self attention-constraint learner (LSACL), global and local attention-constraint learner (GLACL) and facial expression classifier. Specifically, feature extractor is mainly used to extract features from overall facial image and its corresponding cropped facial regions. Then, the extracted local features from facial regions are fed into local self attention-constraint learner, where some prior rank constraints summarized from facial domain knowledge are embedded into self attention weights. Similarly, the rank correlation constraints between respective facial region and a specified expression are further embedded into global-to-local attention weights when the global feature and local features from local self attention-constraint learner are fed into global and local attention-constraint learner. Finally, the feature from global and local attention-constraint learner and original global feature are fused and passed to facial expression classifier for conducting facial expression recognition. Experiments on two benchmark datasets validate the effectiveness of the proposed method.
A multimedia solution to motivate childhood cancer patients to keep up with cancer treatment

Childhood cancer is a deadly illness that requires the young patient to adhere to cancer treatment for survival. Sadly, the high treatment side-effect burden can make it difficult for patients to keep up with their treatment. However, childhood cancer patients can manage these treatment side effects through daily self-care to make the process more bearable. This paper outlines the design and development process of a multimedia-based solution to motivate these young patients to adhere to cancer treatment and manage their treatment side effects. Due to the high appeal of multimedia-based interventions and the proficiency of young children in using mobile devices, the intervention of this study takes the form of a virtual pet serious game developed for mobile. The intervention which is developed based on the Protection Motivation Theory, includes multiple game modules with the purpose of improving the coping appraisal of childhood cancer patients on using cancer treatment to fight cancer, and taking daily self-care to combat treatment side-effects. The prototype testing results show that the intervention is well received by the voluntary play testers. Future work of this study includes the evaluation of the intervention developed with childhood cancer patients to determine its effectiveness.
Attention feature matching for weakly-supervised video relocalization

Localizing the desired video clip for a given query in an untrimmed video has been a hot research topic for multimedia understanding. Recently, a new task named video relocalization, in which the query is a video clip, has been raised. Some methods have been developed for this task, however, these methods often require dense annotations of the temporal boundaries inside long videos for training. A more practical solution is the weakly-supervised approach, which only needs the matching information between the query and video. Motivated by that, we propose a weakly-supervised video relocalization approach based on an attention-based feature matching method. Specifically, it recognizes the video clip by finding the clip whose frames are the most relevant to the query clip frames based on the matching results of the frame embeddings. In addition, an attention module is introduced to identify the frames containing rich semantic correlations in the query video. Extensive experiments on the ActivityNet dataset demonstrate that our method can outperform several weakly-supervised methods consistently and even achieve competing performance to supervised baselines.
Cross-cultural design of facial expressions for humanoids: is there cultural difference between Japan and Denmark?

In this research, the authors succeeded in creating facial expressions made with the minimum necessary elements for recognizing a face. The elements are two eyes and a mouth made using precise circles, which are transformed to make facial expressions geometrically, through rotation and vertically scaling transformation. The facial expression patterns made by the geometric elements and transformations were composed employing three dimensions of visual information that had been suggested by many previous researches, slantedness of the mouth, openness of the face, and slantedness of the eyes. The authors found that this minimal facial expressions can be classified into 10 emotions: happy, angry, sad, disgust, fear, surprised, angry*, fear*, neutral (pleasant) indicating positive emotion, and neutral (unpleasant) indicating negative emotion. The authors also investigate and report cultural differences of impressions of facial expressions of above-mentioned simplified face.
Mobile Monitoring of Air Pollution Reveals Spatial and Temporal Variation in an Urban Landscape

Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m3 (PM1), 13.48 ± 5.59 μg/m3 (PM2.5), 16.13 ± 5.80 μg/m3 (PM10), and 1.56 ± 0.39 μg/m3 (BC). We find that fine PM size fractions (PM2.5) constitute approximately 84% of PM10 and that BC comprises 11.6% of observed PM2.5. Air pollution hotspots across three size fractions of PM (PM1, PM2.5, and PM10) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM.
Investigations for the detection of genotoxic substances on TLC plates

ABSTRACT The present study describes modifications to an existing bioautographic method for detecting genotoxic substances on thin-layer chromatography (TLC) plates by using the umu assay. It will be shown that the complex procedure that is described in the literature can be significantly simplified. Using the modified method, it is possible to detect 0.3 ng of the genotoxic substance 4-nitroquinoline-N-oxide. With 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside (X-gal), a suitable substrate for environmental samples for the detection of the resulting β-galactosidase was found. Experiments showed, in contrast, that indirectly genotoxic substances could not thus far be identified on the TLC plate with overlayer bioautography. GRAPHICAL ABSTRACT
Progress in Borneol Intervention for Ischemic Stroke: A Systematic Review

Background: Borneol is a terpene and bicyclic organic compound that can be extracted from plants or chemically synthesized. As an important component of proprietary Chinese medicine for the treatment of stroke, its neuroprotective effects have been confirmed in many experiments. Unfortunately, there is no systematic review of these studies. This study aimed to systematically examine the neuroprotective effects of borneol in the cascade reaction of experimental ischemic stroke at different periods. Methods: Articles on animal experiments and cell-based research on the actions of borneol against ischemic stroke in the past 20°years were collected from Google Scholar, Web of Science, PubMed, ScienceDirect, China National Knowledge Infrastructure (CNKI), and other biomedical databases. Meta-analysis was performed on key indicators in vivo experiments. After sorting the articles, we focused on the neuroprotective effects and mechanism of action of borneol at different stages of cerebral ischemia. Results: Borneol is effective in the prevention and treatment of nerve injury in ischemic stroke. Its mechanisms of action include improvement of cerebral blood flow, inhibition of neuronal excitotoxicity, blocking of Ca2+ overload, and resistance to reactive oxygen species injury in the acute ischemic stage. In the subacute ischemic stage, borneol may antagonize blood-brain barrier injury, intervene in inflammatory reactions, and prevent neuron excessive death. In the late stage, borneol promotes neurogenesis and angiogenesis in the treatment of ischemic stroke. Conclusion: Borneol prevents neuronal injury after cerebral ischemia via multiple action mechanisms, and it can mobilize endogenous nutritional factors to hasten repair and regeneration of brain tissue. Because the neuroprotective effects of borneol are mediated by various therapeutic factors, deficiency caused by a single-target drug is avoided. Besides, borneol promotes other drugs to pass through the blood-brain barrier to exert synergistic therapeutic effects.
Dietary Tomato Consumption and the Risk of Prostate Cancer: A Meta-Analysis

Objective: Several epidemiological studies have linked tomato products consumption with prostate cancer risk; however, the findings yielded inconsistent results. The aim of the present meta-analysis is to summary the evidence on this association based on eligible cohort studies. Materials and Methods: A comprehensive literature search of articles was performed in March 2021 using PubMed, ISI Web of Science, and Scopus databases. A random-effects model was used to calculate the combined relative risks (RRs) and their corresponding 95% confidence intervals (CIs). Heterogeneity across studies was assessed using Cochran's Q statistic and the I2 score. Results: A total of 10 prospective studies were finally included in our meta-analysis. There was no evidence of a significant association between tomato products consumption and prostate cancer risk (RR 0.91, 95% CI 0.79–1.03, P = 0.138). Subgroup meta-analyses were performed by tomato types, geographical region, publication year, study quality and number of cases. No significant associations were observed in any subgroups (all P > 0.05). No significant publication bias was observed using Begg's test (P = 0.602) or Egger's test (P = 0.957). Conclusion: The results of this meta-analysis indicated that tomato consumption was not related with the risk of prostate cancer. Further prospective large-scale cohort studies are still warranted to verify our findings.
Patients With Epilepsy Who Underwent Epilepsy Surgery During the COVID-19 Pandemic Showed Less Depressive Tendencies

Introduction: Our hypothesis in this study was that differences might exist between patients with epilepsy (PWE) who underwent epilepsy surgery before and within the period of the coronavirus disease 2019 (COVID-19) pandemic. The purpose of this study was to compare results of the Zung Self-Rating Depression Scale (SDS) between PWE who underwent epilepsy surgery before and during the pandemic period. Methods: Participants were PWE who underwent open cranial epilepsy surgery between February 2019 and February 2021 in our hospital. Patients who underwent surgery in the first half of this period, between February 2019 and January 2020, were defined as the pre-pandemic period group (pre-Group) and those treated in the second half, between February 2020 and February 2021, were categorized as the pandemic period group (within-Group). All patients completed the SDS before surgery, and scores were compared between groups. Results: SDS score was significantly higher in the pre-Group than in the within-Group (p = 0.037). Other factors, including age (p = 0.51), sex (p = 0.558), epilepsy duration from onset to SDS score evaluation (p = 0.190), seizure frequency (p = 0.794), number of anti-seizure medications (p = 0.787), and intelligence quotient (p = 0.871) did not differ significantly between groups. Conclusion: SDS score was higher in the pre-pandemic group than in the within-pandemic group, which may indicate that PWE with less-positive outlooks may be less likely to seek medical attention during stressful periods.