Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic intervention in the treatment of adult patients with metastatic cancer : a comparative study of two group counseling approaches

THERAPEUTIC INTERVENTION JN THE TREATMENT OF ADULT PATIENTS WITH METASTATIC CANCER: A COMPARATIVE STUDY OF TWO GROUP COUNSELING APPROACHES Gilbert G am er Cumbia The College of William and Mary In Virginia, April 1985 Chairman: Professor Jack A. Duncan The purpose of this study was to analyze the efficacy of two group counseling approaches upon adult patients diagnosed with metastatic cancer since August l, 1980. Eighteen adult outpatients from metropolitan Richmond, Virginia area hospitals were the subjects. Ail were volunteers and were randomly assigned, using a modified random sampling procedure, to a nondirective, S tre s s management, or control group. Five personality characteristics were examined: s ta te anxiety, tra it anxiety, self-esteem, innerdlrectedness, and time competence. The nondirective and stress management groups met fo r 90 minute sessions twice a week for five consecutive weeks. The control group did not meet. Each group facilitator distributed the State-Tralt Anxiety Inventory, Tennessee Self Concept Scale, and the Personal Orientation Inventory to the participants at an eleventh session and explained the directions. These assessment Instruments were selfadministered a t home. Proper test-taking procedures were stressed by the facilitator. The same Instruments were taken to the homes of the control group members for self-administration. The research design used In this study was the Posttest-Only Control Group Design, The statistical procedure employed was the analysis of variance. Five null hypotheses provided the basis for testing for significant difference ( * =.05) among the nondirective, stress management, and control groups on posttest anxiety, self-esteem, tnnerdtrectednesS) and time competence measures. Analyses of the test data revealed tha t there was no significant difference among the nondirective, stress management, and control groups on any of the posttest measures. The investigator felled to reject all five null hypotheses. Individual post-group Interviews were conducted within two weeks of the termination of each group. They served as a validity oheck of the Instrumentation results, and a content analysis was conducted with the interview data. The Interview data analyses revealed that there was no appreciable difference among the nondirective, stress management, and control groups. There was, however, a qualitative difference when comparing the nondirective and stress management groups with the control group. In each trea tm ent group, the group experience was credited for a diminution of stress and anxiety and an enhancement of self-esteem, innerdlrectedness, and time competence. The control group period did not appear to be as meaningful or beneficial.
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware

We propose an approximation of echo state networks (ESNs) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed integer ESN (intESN) is a vector containing only <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-bits integers (where <inline-formula> <tex-math notation="LaTeX">$n < 8$ </tex-math></inline-formula> is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The proposed intESN approach is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs, classifying time series, and learning dynamic processes. Such architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss. The experiments on a field-programmable gate array confirm that the proposed intESN approach is much more energy efficient than the conventional ESN.
Clinical evaluation of the Lumac bioluminescence method for screening urine specimens

A bioluminescence method for screening urine cultures to provide rapid reporting of negative specimens and to select appropriate urine cultures for direct application of automated identification methods was evaluated. A total of 2,000 specimens were processed in the Lumac Biocounter (3M, St. Paul, Minn.), and the results were compared with quantitative culture techniques by using a 0.001-ml inoculating loop. A total of 841 specimens were positive by the bioluminescence method; 291 specimens were culture positive (greater than or equal to 50,000 CFU of one or two organisms per ml). Positive cultures represented more than 20 different organisms. Approximately two-thirds of the false-positive results represented mixed flora or pure cultures of less than 5 X 10(4) organisms per ml. The predictive value of a negative result was 98.4%, reflecting a false-negative rate of only 0.7%. No advantages in cost or technician time were noted, but the Lumac method appears to be a useful technique in decreasing reporting time, especially for negative urine cultures.
Role of glycosylation in expression of functional diphtheria toxin receptors

We have previously demonstrated, by using a detergent-solubilized system, the existence of specific diphtheria toxin-binding glycoproteins on the surface of toxin-sensitive cells. We have now tested the effect of tunicamycin treatment on the sensitivity of cells in culture to diphtheria toxin and have investigated the toxin sensitivity of mutant cells with known defects in glycosylation of asparagine-linked glycoproteins. Treatment of CHO-K1 cells with tunicamycin, which blocks the synthesis of both high-mannose-type and complex-type oligosaccharide chains of asparagine-linked glycoproteins, resulted in a 50- to 100-fold decrease in sensitivity to diphtheria toxin. In contrast, CHO-K1 mutants, defective in the synthesis of either high-mannose-type or complex-type oligosaccharides, showed no difference in toxin sensitivity compared with that of their parental cell lines. When we used an acid shock system, which is believed to result in receptor-dependent direct toxin penetration at the cell surface, the toxin sensitivity of tunicamycin-treated cells was not restored to that of untreated cells, suggesting that tunicamycin treatment results in a decrease in functional toxin receptors. Direct binding studies with 125I-labeled toxin demonstrated that this decrease in functional receptors is due to a decrease in the affinity of the receptors rather than to a change in the number of receptors. Taken together, these data are consistent with the interpretation that the diphtheria toxin receptor is a glycoprotein and suggest that the toxin binds neither to carbohydrate residues unique to the high-mannose-type oligosaccharides nor to those unique to the complex-type oligosaccharides. Furthermore, these data are consistent with the hypothesis that diphtheria toxin binds to the peptide backbone of the glycoprotein receptor.
Wundheilung an der Hornhautrückfläche im Tierversuch

Autoradiographic studies using tritiated thymidine as a precursor of DNA synthesis show that the process of wound healing of the corneal endothelium in both rhesus monkeys and rabbits following scarification and cryocoagulation takes place for the most part by means of cell proliferation. After injection of tritiated thymidine into the anterior chamber immediately prior to sacrifice, autoradiographically labeled flat mounts and cross-section preparations of the corneas of 7 rhesus monkeys and 14 rabbits were examined at lesion ages ranging from 4 hours to 10 days. In the monkeys scarcely 10% of the endothelial cells in the areas of the corneal lesions or at their borders were in the S-phase in the autoradiographically labeled flat mounts 24 hours to 3 days after injury, whereas up to 39% of those in the rabbits were seen to be so after 18 hours. In the monkeys one could discern mitoses between the 2nd and 5th days, and amitoses (binuclear cells) were found in 1-day and 3-day animals. In rabbits numerous mitoses could be discerned between the 1st and 5st days, but no multinuclear cells were seen. Healing of the corneal endothelium in the monkeys occurred some-what later and was less pronounced than in the rabbits.
MMNet: Multi-Stage and Multi-Scale Fusion Network for RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) methods directly extract and fuse raw features from RGB and depth backbones. Such methods can be easily restricted by low-quality depth maps and redundant cross-modal features. To effectively capture multi-scale cross-modal fusion features, this paper proposes a novel Multi-stage and Multi-Scale Fusion Network (MMNet), which consists of a cross-modal multi-stage fusion module (CMFM) and a bi-directional multi-scale decoder (BMD). Similar to the mechanism of visual color stage doctrine in human visual system, the proposed CMFM aims to explore the useful and important feature representations in feature response stage, and effectively integrate them into available cross-modal fusion features in adversarial combination stage. Moreover, the proposed BMD learns the combination of cross-modal fusion features from multiple levels to capture both local and global information of salient objects and further reasonably boost the performance of the proposed method. Comprehensive experiments demonstrate that the proposed method can achieve consistently superior performance over the other 14 state-of-the-art methods on six popular RGB-D datasets when evaluated by 8 different metrics.
Surface Reconstruction with Unconnected Normal Maps: An Efficient Mesh-based Approach

Normal integration is a key step in dense 3D reconstruction methods such as shape-from-shading and photometric stereo. However, normal integration cannot be guaranteed between spatially unconnected normal maps, which can ultimately cause a shape deformation in surface-from-normals (SfN). For the first time, this paper presents an efficient approach to address the fundamental problem of surface reconstruction from unconnected normal maps (denoted as "SfN+") using discrete geometry. We first design a normal piece pairing metric to measure the virtually pairing quality between two unconnected normal fragments, which is used as a new constraint for the boundary vertexes during mesh deformation. We then adopt a normal connecting significance indicator to adjust the influence of virtually connected vertexes, which further improves the overall shape deformation. Finally, we model the shape reconstruction of unconnected normal maps as a light-weight energy optimization framework by jointly considering the relaxation of connecting constraints and overall reconstruction error. Experiments show that the proposed SfN+ achieves a robust and efficient performance on dense 3D surface reconstruction.
Urban Scale Trade Area Characterization for Commercial Districts with Cellular Footprints

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data and social network check-in data, MFRs can model customer mobility patterns comprehensively at urban scale. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on two large-scale cellular network datasets covering 3.5 million and 1.8 million mobile phone users in two metropolis in China, respectively. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.
An Advanced LiDAR Point Cloud Sequence Coding Scheme for Autonomous Driving

Due to the huge volume of point cloud data, storing or transmitting it is currently difficult and expensive in autonomous driving. Learning from the high efficiency video coding (HEVC) coding framework, we propose an advanced coding scheme for large-scale LiDAR point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of intra-coding and inter-coding. For intra-coding, we utilize a cluster-based prediction method to remove the spatial redundancy. For inter-coding, a predictive recurrent network is designed, which is capable of generating future frames according to the previously encoded frames. By calculating the residual error between the predicted and real point cloud data, the temporal redundancy can be removed. Finally, the residual data is quantized and encoded by lossless coding schemes. Experiments are conducted on the KITTI data set with four different scenes to verify the effectiveness and efficiency of the proposed method. Our approach can deal with multiple types of point cloud data from the simple to more complex, and yields better performance in terms of compression ratio compared with octree, Google Draco, MPEG TMC13 and other recently proposed methods.
Incentive Mechanisms for Crowdsensing

Crowdsensing is a popular method that leverages a crowd of sensor users to collect data. For many crowdsensing applications, the collected raw data need to be preprocessed before further analysis, and the preprocessing work is mainly done by the crowdsourcer. However, as the amount of collected data increases, this type of preprocessing approach has many disadvantages. In this article, we construct monetary-based incentive mechanisms to motivate users to preprocess the collected raw data for the crowdsourcer. For two common crowdsensing scenarios, we propose two system models, which are the single-task-multiple-participants (STMP) model and the multiple-tasks-multiple-participants (MTMP) model. In the STMP model, we design an incentive mechanism based on game theory and prove that there is a Nash equilibrium. In the MTMP model, we develop an incentive mechanism based on an auction and demonstrate that the incentive mechanism has the desirable properties of truthfulness, individual rationality, profitability, and computational efficiency. Furthermore, the utility maximization problems of the crowdsourcer and users are simultaneously considered in our incentive mechanisms. Through theoretical analysis and extensive experiments, we evaluate the performance of our incentive mechanisms.
Simulation-based optimization methods applied in hospital emergency departments: A systematic review

Since high performance is essential to the functioning of emergency departments (EDs), they must constantly pursue sensible and empirically testable improvements. In light of recent advances in computer science, an increasing number of simulation-based approaches for studying and implementing ED performance optimizations have become available in the literature. This paper aims to offer a survey of these works, presenting progress made on the topic while indicating possible pitfalls and difficulties in EDs. With that in mind, this review considers research studies reporting simulation-based optimization experiments published between 2007 and 2019, covering 38 studies. This paper provides bibliographic background on issues covered, generates statistics on methods and tools applied, and indicates major trends in the field of simulation-based optimization. This review contributes to the state of the art on ED modeling by offering an updated picture of the present state of the field, as well as promising research gaps. In general, this review argues that future studies should focus on increasing the efficiency of multi-objective optimization problems by decreasing their cost in time and labor.
ChefGAN: Food Image Generation from Recipes

Although significant progress has been made in generating images from the text by using generative adversarial networks (GANs), it is still challenging to deal with long text, which contains complex semantic information like recipes. This paper focuses on generating images with high visual realism and semantic consistency from the complex text of recipes. To achieve this, we propose a GANs based method termed ChefGAN. The critical concept of ChefGAN is that a joint image-recipe embedding model is used before the generation task to provide high-quality representations of recipes, and it acts as an extra regularization during the generation to improve semantic consistency. Two modules are designed for this image text embedding module (ITEM) and a cascaded image generation module (CIGM). The generation process is carried out in 3 steps: (1) Two encoders in ITEM are trained simultaneously to generate similar representations for each image-recipe pair. (2) CIGM generates images according to the representations from ITEM's text encoder. (3) The generated image is fed into ITEM's image encoder to calculate the similarity with the given recipe. This process can provide additional regularization effect other than the impact of a discriminator. To facilitate convergence, we applied a two-stage training strategy, which generates an image with low resolution and then one with high resolution in the CIGM module. Compared with other representative state-of-the-art methods, ChefGAN demonstrates better performance both in visual realism and semantic consistency.
DroidCloud: Scalable High Density AndroidTM Cloud Rendering

Cloud rendering is an emerging technology in which rendering-heavy applications run on the cloud server and then stream the rendered contents to the end-user device. High density and high scalability of the cloud rendering services are crucial to support millions of users concurrently and cost-effectively. However, it is still challenging to run Android OS in cloud smoothly with high density and high scalability without compromising user experience. This paper presents DroidCloud, the first open-source Android\footnoteAndroid is a trademark of Google LLC. cloud rendering solution focusing on the scalable design and density aspect optimization to the best of our knowledge. To cloudify Android OS, DroidCloud utilizes thevHAL technology in order to support remote devices and keep transparent to Android applications. And aFlexible rendering scheduling policy is introduced to break the boundary of GPU physical locations. Thus, both remote GPUs and local GPUs can accommodate render tasks by forwarding rendering tasks and making it possible to support multiple Android OSes with GPU acceleration. Besides, to further improve the density, DroidCloud optimizes the resource cost both in a single instance and across instances. We show that DroidCloud can run hundreds of Android OSes on a single Intel Xeon server with GPU acceleration simultaneously, increasing the density at the scale of one order of magnitude compared to current cloud gaming systems. Further experimental results demonstrate that DroidCloud can transparently run Android applications at native speed with lower CPU, memory, and storage utilization.
Cross-Modal Relation-Aware Networks for Audio-Visual Event Localization

We address the challenging task of event localization, which requires the machine to localize an event and recognize its category in unconstrained videos. Most existing methods leverage only the visual information of a video while neglecting its audio information, which, however, can be very helpful and important for event localization. For example, humans often recognize an event by reasoning with the visual and audio content simultaneously. Moreover, the audio information can guide the model to pay more attention on the informative regions of visual scenes, which can help to reduce the interference brought by the background. Motivated by these, in this paper, we propose a relation-aware network to leverage both audio and visual information for accurate event localization. Specifically, to reduce the interference brought by the background, we propose an audio-guided spatial-channel attention module to guide the model to focus on event-relevant visual regions. Besides, we propose to build connections between visual and audio modalities with a relation-aware module. In particular, we learn the representations of video and/or audio segments by aggregating information from the other modality according to the cross-modal relations. Last, relying on the relation-aware representations, we conduct event localization by predicting the event relevant score and classification score. Extensive experimental results demonstrate that our method significantly outperforms the state-of-the-arts in both supervised and weakly-supervised AVE settings.
AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition

Micro-expressions (MEs) are important clues for reflecting the real feelings of humans, and micro-expression recognition (MER) can thus be applied in various real-world applications. However, it is difficult to perceive and interpret MEs correctly. With the advance of deep learning technologies, the accuracy of micro-expression recognition is improved but still limited by the lack of large-scale datasets. In this paper, we propose a novel micro-expression recognition approach by combining Action Units (AUs) and emotion category labels. Specifically, based on facial muscle movements, we model different AUs based on relational information and integrate the AUs recognition task with MER. Besides, to overcome the shortcomings of limited and imbalanced training samples, we propose a data augmentation method that can generate nearly indistinguishable image sequences with AU intensity of real-world micro-expression images, which effectively improve the performance and are compatible with other micro-expression recognition methods. Experimental results on three mainstream micro-expression datasets, i.e., CASME II, SAMM, and SMIC, manifest that our approach outperforms other state-of-the-art methods on both single database and cross-database micro-expression recognition.
AI-SAS: Automated In-match Soccer Analysis System

Real-time in-match soccer statistics provide continuous tracking of soccer ball and player positions and speeds, enabling advanced analytics. Currently, only elite soccer leagues have the luxury of tracking in-match soccer statistics operated with a large number of trained personnel. In this work, we present an Automated In-match Soccer Analysis System (AI-SAS), using a domain-knowledge-based multi-view global tracking. This system tracks player team, position, and speed automatically, providing real-time in-match team- and individual-level statistics and analyses. In comparison with the latest soccer analysis systems, AI-SAS is more scalable in streaming multiple video sources for real-time process and more flexible in hosting plug-and-play deep-learning-based tracking-by-detection algorithms. The global multi-view tracking also overcomes the single-view limitation and improves the tracking accuracy.