Miracle-dz/UniFAR-FTUs huggingface dataset:
Are psychological interventions effective and accepted by cancer patients? I. Standards and levels of evidence

Different standards and levels of evidence for evaluating the effectiveness of psychological interventions for managing distress in cancer patients are presented and discussed. We conclude that the strongest evidence comes from systematic qualitative and quantitative (i.e., meta-analyses) reviews of the relevant literature and that the most appropriate standard of evidence is the “preponderance of evidence” rather than “beyond a reasonable doubt. ” Results of four selected qualitative and quantitative systematic reviews of the literature are described. The preponderance of evidence furnished by these systematic reviews, particularly that gleaned from meta-analyses, suggests that psychological interventions are effective in managing distress in cancer patients. Although effectiveness may vary as a function of the specific nature of the intervention, overall, effectiveness appears strongest for anxiety-related outcomes and when participants are prescreened for distress. Different standards and indexes for evaluating evidence regarding the acceptability of psychological interventions with cancer patients are presented and discussed. The use of simple study accrual rates as an index of intervention acceptability is deemed inappropriate. We suggest alternative indexes of acceptability and conclude that sufficient information does not exist at the present time to draw the conclusion that contemporary psychological interventions for managing distress in cancer patients are unacceptable.
Inspiration for wing design: how forelimb specialization enables active flight in modern vertebrates

Harnessing flight strategies refined by millions of years of evolution can help expedite the design of more efficient, manoeuvrable and robust flying robots. This review synthesizes recent advances and highlights remaining gaps in our understanding of how bird and bat wing adaptations enable effective flight. Included in this discussion is an evaluation of how current robotic analogues measure up to their biological sources of inspiration. Studies of vertebrate wings have revealed skeletal systems well suited for enduring the loads required during flight, but the mechanisms that drive coordinated motions between bones and connected integuments remain ill-described. Similarly, vertebrate flight muscles have adapted to sustain increased wing loading, but a lack of in vivo studies limits our understanding of specific muscular functions. Forelimb adaptations diverge at the integument level, but both bird feathers and bat membranes yield aerodynamic surfaces with a level of robustness unparalleled by engineered wings. These morphological adaptations enable a diverse range of kinematics tuned for different flight speeds and manoeuvres. By integrating vertebrate flight specializations—particularly those that enable greater robustness and adaptability—into the design and control of robotic wings, engineers can begin narrowing the wide margin that currently exists between flying robots and vertebrates. In turn, these robotic wings can help biologists create experiments that would be impossible in vivo.
A tensor decomposition for geometric grouping and segmentation

While spectral clustering has been applied successfully to problems in computer vision, their applicability is limited to pairwise similarity measures that form a probability matrix. However many geometric problems with parametric forms require more than two observations to estimate a similarity measure, e.g. epipolar geometry. In such cases we can only define the probability of belonging to the same cluster for an n-tuple of points and not just a pair, leading to an n-dimensional probability tensor. However spectral clustering methods are not available for tensors. In this paper we present an algorithm to infer a similarity matrix by decomposing the n-dimensional probability tensor. Our method exploits the super-symmetry of the probability tensor to provide a randomised scheme that does not require the explicit computation of the probability tensor. Our approach is fast and accurate and its applicability is illustrated on two significant problems, namely perceptually salient geometric grouping and parametric motion segmentation (like affine, epipolar etc).
Supervised Gaussian process latent variable model based on Gaussian mixture model

In this paper, we propose a supervised Gaussian Process Latent Variable Model(GPLVM) based on Gaussian mixture model(GMM). In this model, we assume that latent variables satisfy the Gaussian mixture distribution, and the conditional distribution of latent variable given the label of the corresponding sample satisfy a Gaussian distribution. In the training process, the parameters for nonlinear dimensionality reduction are learned with the training sample and data labels. Meanwhile, the mean and variance for each class are learned. Therefore, it is tractable to evaluate the probability of a sample belongs to a particular class. The effectiveness of our model is demonstrated by comparing with some representative dimensionality reduction method. We evaluate the effectivebess of our method on UCI data set. The experiment results show that the proposed method is capable of distinguish data from different classes in low dimensional space, and it have good performance for classification.
Nanog binds to Smad1 and blocks bone morphogenetic protein-induced differentiation of embryonic stem cells

ES cells represent a valuable model for investigating early embryo development and hold promise for future regenerative medicine strategies. The self-renewal of pluripotent mouse ES cells has been shown to require extrinsic stimulation by the bone morphogenetic protein (BMP) and leukemia inhibitory factor signaling pathways and the expression of the transcription factors Oct4 and Nanog. However, the network of interactions among extrinsic and intrinsic determinants of ES cell pluripotency is currently poorly understood. Here, we show that Nanog expression is up-regulated in mouse ES cells by the binding of T (Brachyury) and STAT3 to an enhancer element in the mouse Nanog gene. We further show that Nanog blocks BMP-induced mesoderm differentiation of ES cells by physically interacting with Smad1 and interfering with the recruitment of coactivators to the active Smad transcriptional complexes. Taken together, our findings illustrate the existence of ES cell-specific regulatory networks that underlie the maintenance of ES cell pluripotency and provide mechanistic insights into the role of Nanog in this process.
Improving object detection accuracy with region and regression based deep CNNs

Object detection has made great improvements in convolutional neural networks (CNNs), which is the high-capacity visual model that yields hierarchies of discriminative features. Object detection based on CNNs is in general divided into two aspects: region based detection and regression based detection. In this paper, we aim at further advancing object detection performance by properly utilizing the complementary results of those two streams. By investigating errors of several previous state-of-the-art methods about the two streams, we discover that those detection results of two general streams are complementary in object recognition and localization. Region based methods achieve high recall but simultaneously struggle with localization problems, while regression based methods make less localization errors by iteratively regressing the object to target localization. Driven by these observations, we propose two kinds of fusion paradigms to combine the results of those two streams. One is direct fusion utilizing the complementary results of those two streams and adopting non-maximal suppression (NMS) and voting operation to make full use of the results generated by two streams. In addition, considering direct fusion may compromise the original performance of object detections, we also propose another method, modifies voting operation that just refines the box coordinate without having any other impact on the original detections and further boosts the performance by an adding operation. Extensive experiments show that our two ensemble paradigms both boost the state-of-the-art results on Pascal VOC dataset.
Multi-objective optimal power flow of power system with FACTS devices using PSO algorithm

Flexible AC Transmission System (FACTS) devices are integrated with the power system networks for improving technical, economical and environmental performance of these power systems. In this paper, three different types of FACTS devices (SVC, UPFC and STATCOM) are incorporated to the power system network. The effect of FACTS devices on the system performance is studied by solving the multi-objective optimal power flow (MOPF) problem under certain constraints considering two different objective functions simultaneously. The MOPF problem is solved by using multi-objective particle swarm optimization (MOPSO) technique to get the optimal allocation and parameters setting of FACTS devices at each solution of the Pareto front set. The objective functions of MOPF problem are considered as indices of the system performance. Moreover, the best compromise solutions are extracted from the trade-off curve via the fuzzy-set theory. The active power losses is taken into account in the energy losses cost which is combined to the installation cost of FACTS devices in order to form the operational cost function. The addition of a new power generation units is considered by adding the fuel cell energy source to the STATCOM device. The simulation results are obtained by combining each FACTS device to IEEE 30-bus test system.
A study with Bangladeshi women: Seeking care for breast health

ABSTRACT In many low- and middle-income countries, breast cancer survival is low. Reasons for this are multifactorial, but delayed presentation for care is a common theme. In this survey study with 100 urban Bangladeshi women, we examined the role of socioeconomic and sociocultural factors on their likelihood to seek breast care from a family physician. In our multivariate model, a woman's age and education significantly predicted her likelihood to see a physician. Sociocultural aspects (e.g., concerns about time commitment of family members, personal household obligations) were significant at bivariate level. Findings are discussed in relation to practice, policy, and research.
Road traffic topic modeling on Twitter using latent dirichlet allocation

Information about road traffic is the one of the most important information for people around the world. TMC (Traffic Management Center) as one of the unit within the Indonesian National Police institution who in charge of traffic management has utilized Twitter as the medium to share about traffic information to the Indonesian citizen. This research aims to create the topic model regarding traffic information on Indonesian Twitter messages. The data used in this research were retrieved from the official Twitter account of the Traffic Management Center in Java using the method of LDA (Latent Dirichlet Allocation) to build the topic model from the dataset. The topic model obtained will represent what kind of topics which posted by TMC in each region in Java. Therefore, the result of this experiment could illustrate valuable and important information that happened in Java Island.
Structural Determinants of the Gain-of-Function Phenotype of Human Leukemia-associated Mutant CBL Oncogene*

Mutations of the tyrosine kinase-directed ubiquitin ligase CBL cause myeloid leukemias, but the molecular determinants of the dominant leukemogenic activity of mutant CBL oncogenes are unclear. Here, we first define a gain-of-function attribute of the most common leukemia-associated CBL mutant, Y371H, by demonstrating its ability to increase proliferation of hematopoietic stem/progenitor cells (HSPCs) derived from CBL-null and CBL/CBL-B-null mice. Next, we express second-site point/deletion mutants of CBL-Y371H in CBL/CBL-B-null HSPCs or the cytokine-dependent human leukemic cell line TF-1 to show that individual or combined Tyr → Phe mutations of established phosphotyrosine residues (Tyr-700, Tyr-731, and Tyr-774) had little impact on the activity of the CBL-Y371H mutant in HSPCs, and the triple Tyr → Phe mutant was only modestly impaired in TF-1 cells. In contrast, intact tyrosine kinase-binding (TKB) domain and proline-rich region (PRR) were critical in both cell models. PRR deletion reduced the stem cell factor (SCF)-induced hyper-phosphorylation of the CBL-Y371H mutant and the c-KIT receptor and eliminated the sustained p-ERK1/2 and p-AKT induction by SCF. GST fusion protein pulldowns followed by phospho-specific antibody array analysis identified distinct CBL TKB domains or PRR-binding proteins that are phosphorylated in CBL-Y371H-expressing TF-1 cells. Our results support a model of mutant CBL gain-of-function in which mutant CBL proteins effectively compete with the remaining wild type CBL-B and juxtapose TKB domain-associated PTKs with PRR-associated signaling proteins to hyper-activate signaling downstream of hematopoietic growth factor receptors. Elucidation of mutant CBL domains required for leukemogenesis should facilitate targeted therapy approaches for patients with mutant CBL-driven leukemias.
On-chip checkpointing with 3D-stacked memories

Since deep-submicron process technologies induce soft errors, advanced computing systems face a low dependability problem. Checkpointing, which copies data required for continuing the program execution as a backup, is expected as a promising approach to keeping a high dependability. However, checkpointing causes additional memory accesses, which cause performance and energy overheads. To reduce these overheads, this paper focuses on 3D-stacking technologies. Since the technologies realize large on-chip memories with a short latency and a high bandwidth, the overheads of checkpointing are expected to decrease. In order to examine the reduction of the overheads, this paper supposes a future 3D-stacked processor-memory module, and proposes an on-chip checkpointing mechanism. The evaluation results indicate that the on-chip checkpointing with 3D-stacked memories can reduce the execution time by 15% and energy consumption by 26% on average, compared with the checkpointing mechanism with off-chip memories.
Patient-reported symptoms and changes up to 1 year after meniscal surgery

Background and purpose — Detailed information on the symptoms and limitations that patients with meniscal tears experience is lacking. This study was undertaken to map the most prevalent self-reported symptoms and functional limitations among patients undergoing arthroscopic meniscal surgery and investigate which symptoms and limitations had improved most at 1 year after surgery. Patients and methods — Patients aged 18–76 years from the Knee Arthroscopy Cohort Southern Denmark (KACS) undergoing arthroscopic meniscal surgery were included in this analysis of individual subscale items from the Knee Injury and Osteoarthritis Outcome Score and 1 question on knee stability. Severity of each item was scored as none, mild, moderate, severe, or extreme. Improvements were evaluated using Wilcoxon’s signed-rank test and effect size (ES). Results — The most common symptoms were knee grinding and clicking, knee pain in general, pain when twisting and bending the knee and climbing stairs (88–98%), while the most common functional limitations were difficulty bending to the floor, squatting, twisting, kneeling, and knee awareness (97–99%). Knee pain in general and knee awareness improved most 1 year after meniscal surgery (ES –0.47 and –0.45; p < 0.001), while knee instability and general knee difficulties improved least (ES 0.10 and –0.08; p < 0.006). Interpretation — Adults undergoing surgery for a meniscal tear commonly report clinical symptoms and functional limitations related to their daily activities. Moderate improvements were observed in some symptoms and functional limitations and small to no improvement in others at 1 year after surgery. These findings can assist the clinical discussion of symptoms, treatments, and patients’ expectations.
Story Popularity Prediction using Neural Networks with Time Series-Based Circular Dependencies

Science and Technology Department of Information and Computer Science Master of Computer Science Short Story Popularity Prediction using Neural Networks with Time Series-Based Circular Dependencies by Karl-Johan Alm The act of judging the quality (or popularity) of a short story is a highly complex and subjective matter, dependent on numerous external factors such as the reader audience, the environment in which the short story is presented, as well as the undulation of trends. Even by looking exclusively at the short story content, ignoring all external factors, there is no good architecture available in Machine Learning to analyze very long sequences, such as a several hundred thousand word story, without resorting to approximations where only a small part (e.g. 40 words) are processed at a time. This thesis introduces and evaluates an extension to neural networks called a memory module, a persistent representation of sequential data, as well as several adjustments to the training process necessary to facilitate the training of the memory module. The memory module is a persistent representation of the input as a whole, and is continuously updated as the network iterates over each input sample. The accuracy of the memory module-enabled model is compared to traditional neural network models performing the same task using the same input, such as recursive neural networks. While the results are still in their infant stages, requiring more and deeper analysis beyond the scope of this thesis, the memory module shows great promise, beating the traditional approaches by roughly 20% in certain cases.
Extracting gamers' cognitive psychological features and improving performance of churn prediction from mobile games

With the continued growth of the mobile game market, many game companies aim to make money through mobile games. In this situation, knowing the tendency of gamers and predicting the churn in advance can maximize profit through effective game services. For this reason, much study has been conducted for the purpose of gamer analysis and churn prediction. However, the study was mainly conducted using surveys, bio-signals, and PC online game logs, which are likely to make detailed information. In this study, we extracted seven cognitive psychological features from the game logs of Crazy Dragon, a commercial mobile RPG game, and used these to predict the churn. In addition, we analyzed the effect of purchasing feature by comparing the churn prediction performance according to presence or absence of purchase feature. In the conclusion, we obtained higher performance when predicting the churn using cognitive psychological features than using the basic raw logs. Also, we obtained high churn prediction performance using only cognitive psychological features without purchase feature.
Perception of Emotions and Body Movement in the Emilya Database

In this paper, we examine the perception of emotions as well as the characterization and the classification of emotional body expressions based on perceptual body cues ratings. Emilya (EMotional body expression In daILY Actions), a database of body expressions of eight emotions (including Neutral) in seven daily actions performed by 11 actors, is used for these purposes. A perceptual study is conducted to explore four issues: 1) how expressed emotions are perceived by humans, 2) how emotion recognition by humans differs across daily actions, 3) how expressed emotions are characterized by humans through body cues, and 4) how emotions are automatically classified based on human rating of body cues. Across all the actions, most of the expressed emotions were correctly identified, but some were confused (e.g., Shame and Sadness). Confusions occurring at the level of emotion perception may be due to a lack of contextual factors (Emilya contains body movement of daily actions without reference to a context), to a similarity of bodily expressions, but also to the lack of other modalities that may contribute to a better recognition of bodily expression of these emotions (e.g., facial expressions). In the paper, we detail and discuss the results from these different studies.
Building An Electrochemical Contact Lens Biosensor

This review discusses the convergence of several research and engineering fields working to design and integrate electrochemical biosensing units into contact lenses. Tear fluid contains a wide variety of biochemical information about both ocular and systemic environments. By measuring biomarkers and their concentrations, it is possible to determine whether certain disease states are present. Emerging biosensor technology, most notably materials constructed at the nano scale, are showing incredible precision in the identification and quantification of individual molecules. Miniature, integrated biosensing units, which require energy self-sufficiency and wireless operation, consist of a few fundamental electronic components. The major systems discussed in this work include: energy, circuits and communication. Publicly documented contact lens prototypes provide an idea of how far the field has come and what challenges lie ahead. The projected clinical impact of real-time, continuous tear fluid monitoring is immense. It is vital that the eye care field remains keenly aware of the efforts underway and active in the conversation going forward.
Investigating accessibility issues of UI mobile design patterns in online communities: a virtual ethnographic study

With the dissemination of mobile devices and the migration of activities that were once executed only on desktop computers to smartphones and tablets, the concerns related to accessibility in this environments have increased. However, the accessibility impact of mobile interface design patterns in the life of disabled people has not been widely addressed in academic works. At the same time, the community of mobile designers and developers has made significant advances in identifying accessibility issues with design patterns on mobile interfaces, reporting these findings in virtual spaces of discussions as forums, blogs, etc. Aiming at exploring the experiences and knowledge of professionals, this paper presents an ethnography study in 18 virtual communities of mobile design and development with the goal of identifying issues on the accessibility of Android mobile UI design patterns. As a result, we analyzed 127 documents in order to propose recommendations to improve the accessibility of mobile interfaces.
Where is my Device? - Detecting the Smart Device's Wearing Location in the Context of Active Safety for Vulnerable Road Users

This article describes an approach to detect the wearing location of smart devices worn by pedestrians and cyclists. The detection, which is based solely on the sensors of the smart devices, is important context-information which can be used to parametrize subsequent algorithms, e.g. for dead reckoning or intention detection to improve the safety of vulnerable road users. The wearing location recognition can in terms of Organic Computing (OC) be seen as a step towards self-awareness and self-adaptation. For the wearing location detection a two-stage process is presented. It is subdivided into moving detection followed by the wearing location classification. Finally, the approach is evaluated on a real world dataset consisting of pedestrians and cyclists.