Miracle-dz/UniFAR-FTUs huggingface dataset:
Total aortic arch replacement with frozen elephant trunk (Thoraflex).

The Thoraflex hybrid prosthesis represents the latest evolution of the classic frozen elephant trunk (FET) (Figure 1A,B) prosthesis. It provides a multi-branched, arch hybrid graft that potentially allows treating (for specific indications and in a single surgical step), complex lesions of the thoracic aorta by combining endovascular treatment with conventional surgery (1). Open in a separate window Figure 1 The Vascutek Thoraflex hybrid stent-graft (A) and its clinical application (B).
Neural Task Planning With AND–OR Graph Representations

This paper focuses on semantic task planning, that is, predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The primary challenges are how to model the task-specific knowledge and how to integrate this knowledge into the learning procedure. In this paper, we propose training a recurrent long short-term memory (LSTM) network to address this problem, that is, taking a scene image (including prelocated objects) and the specified task as input and recurrently predicting action sequences. However, training such a network generally requires large numbers of annotated samples to cover the semantic space (e.g., diverse action decomposition and ordering). To overcome this issue, we introduce a knowledge and–or graph (AOG) for task description, which hierarchically represents a task as atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according to common sense) by training another auxiliary LSTM network with a small set of annotated samples. Furthermore, these generated samples (i.e., task-oriented action sequences) effectively facilitate training of the model for semantic task planning. In our experiments, we create a new dataset that contains diverse daily tasks and extensively evaluates the effectiveness of our approach.
Grid-Based Environment Estimation Using Evidential Mapping and Particle Tracking

Modeling and estimating the current local environment by processing sensor measurement data is essential for intelligent vehicles. Static obstacles, dynamic objects, and free space have to be appropriately represented, classified, and filtered. Occupancy grids, known for mapping static environments, provide a common low-level representation using occupancy probabilities with an implicit data association through the discrete grid structure. Extending this idea toward dynamic environments with moving objects requires a static/dynamic classification of measured occupancy and a tracking of the dynamic state of grid cells. In this paper, we propose a new dynamic grid mapping approach. An evidential representation using the Dempster–Shafer framework is used to model hypotheses for static occupancy, dynamic occupancy, free space, and their combined hypotheses. These hypotheses are consistently estimated and accumulated in a dynamic grid map by an adapted evidential filtering, allowing one to distinguish static and dynamic occupancy. The evidential grid mapping is combined with a low-level particle filter tracking that is used to estimate cell velocity distributions and predict dynamic occupancy of the grid map. Static occupancy is directly modeled in the grid map without requiring particles, increasing efficiency, and improving the static/dynamic classification due to the persistent map accumulation. Experimental results with real sensor data show the effectiveness of the proposed approach in challenging scenarios with occlusions and dense traffic.
Identifying causes of perceptual differences in problematic activities of daily life between patients with COPD and proxies: A qualitative study

BACKGROUND/AIM Proxies of patients with COPD are often unable to identify the patients' most important problematic activities of daily life (ADLs). The aim was to explore causes of perceptual differences between patients with COPD and their proxies about the problematic ADLs of the patient. METHODS Ten open structured interviews were conducted in the presence of both the patient and proxy. Patients' five most important problematic ADLs identified by the couple were compared and discussed. For data analysis, open coding, axial coding and selective coding were conducted. RESULTS Ten main causes were identified: differences in the level of satisfaction, or acceptance; estimation of own capabilities; problematic ADL goes unnoticed; proxy is not present during problematic ADL; problematic ADL is not performed (often or anymore), or not (longer) talked about; masked by another problematic ADL; value judgement; solution for problematic ADL. CONCLUSION The causes of perceptual differences between patients and proxies about patients' problematic ADLs were related to both the patient's and the proxy's perception.
WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

Social media, such as Facebook and WeChat, empowers millions of users to create, consume, and disseminate online information on an unprecedented scale. The abundant information on social media intensifies the competition of WeChat Public Official Articles (i.e., posts) for gaining user attention due to the zero-sum nature of attention. Therefore, only a small portion of information tends to become extremely popular while the rest remains unnoticed or quickly disappears. Such a typical “long-tail” phenomenon is very common in social media. Thus, recent years have witnessed a growing interest in predicting the future trend in the popularity of social media posts and understanding the factors that influence the popularity of the posts. Nevertheless, existing predictive models either rely on cumbersome feature engineering or sophisticated parameter tuning, which are difficult to understand and improve. In this paper, we study and enhance a point process-based model by incorporating visual reasoning to support communication between the users and the predictive model for a better prediction result. The proposed system supports users to uncover the working mechanism behind the model and improve the prediction accuracy accordingly based on the insights gained. We use realistic WeChat articles to demonstrate the effectiveness of the system and verify the improved model on a large scale of WeChat articles. We also elicit and summarize the feedback from WeChat domain experts.
Comprehensive Examination of Therapies for Pain in Parkinson’s Disease: A Systematic Review and Meta-Analysis

Pain in Parkinson’s disease (PD) is a debilitating symptom with a prevalence of 68%, yet is untreated 50% of the time. What is unclear, however, is which treatment is optimal for minimizing pain severity in PD. Thus, the objective of this systematic review and meta-analysis was to investigate the efficacy of a variety of novel, complimentary, and conventional treatments for pain in PD and elucidate which therapy is the most effective. A systematic search was performed using MEDLINE, PsycINFO, Embase, CINAHL, and CENTRAL databases. To identify additional articles, manual searches of reference lists of included trials were also searched. Major neurology conference proceedings occurring between January 2014 and February 2018 were also searched to identify unpublished studies that may be potentially eligible. Twenty-five randomized controlled trials that encompassed medical, surgical, and complementary therapies met our inclusion criteria and exhibited moderate quality evidence. Two reviewers conducted assessments for study eligibility, risk of bias, data extraction, and quality of evidence rating. A conservative random-effects model was used to pool effect estimates of pain severity. The greatest reductions in pain were found with safinamide (Standardized mean difference = –4.83, 95% CI [–5.07 to –4.59], p < 0.0001), followed by cannabinoids and opioids, multidisciplinary team care, catechol-O-methyltransferase inhibitors, and electrical and Chinese therapies. Moderate effects in reducing pain were in pardoprunox and surgery, while the weakest effects were in dopaminergic agonists and miscellaneous therapies. Safinamide is an important adjunct to standard parkinsonian medication for alleviating pain in PD.
Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO with One-Bit ADCs

In a cell-free cloud radio access network (C-RAN) architecture, uplink channel estimation is carried out by a centralized baseband processing unit (BBU) connected to distributed remote radio heads (RRHs). When the RRHs have multiple antennas and limited radio front-end resources, the design of uplink channel estimation is faced with the challenges posed by reduced radio frequency (RF) chains and one-bit analog-to-digital converters (ADCs) at the RRHs. This work tackles the problem of jointly optimizing the pilot sequences and the pre-RF chains analog combiners with the goal of minimizing the sum of mean squared errors (MSEs) of the estimated channel vectors at the BBU. The problem formulation models the impact of the ADC operation by leveraging Bussgang's theorem. An efficient solution is developed by means of an iterative alternating optimization algorithm. Numerical results validate the advantages of the proposed joint design compared to baseline schemes that randomly choose either pilots or analog combiners.
Reciprocity Among Preschoolers in Relation to Resource Allocation Toward Siblings, Friends, and Strangers

Children at age 6 years differentially treat kin, friends, and strangers in resource allocation games by being more altruistic toward kin. However, it is unknown how previous allocation experience as a recipient influences the potential kinship effect in subsequent resource allocations. The present study investigated how 4- to 6-year-old children allocated resources between themselves and a sibling, a friend, or a stranger in three allocation tasks after the recipient had previously shared or nonshared with the participant. Results showed that, when a share would induce cost on the self, 6-year-old children were likely to share with a sibling whether the sibling had previously shared or not, but they would share only with friends or strangers who had previously shared. When a share would induce no cost, participants across ages were likely to share with a recipient who had previously shared. When the decision option was between sharing equally and sharing altruistically, participants would allow the recipient to have more only when the recipient was a sibling or friend who had previously allocated altruistically. These findings suggest that kin altruism in resource allocation emerges at around 6 years of age and that reciprocity partly overrides and partly reinforces kin altruism.
Limited Complexity Optimization of the Uplink Performance in Cloud Cellular Networks

We consider a cooperative cellular network where the signal from a mobile is detected jointly by base stations close to it using a minimum-mean-square-error (MMSE) estimator. We assume a network where different numbers of cooperating base stations can be used for the detection of each mobile. We also assume that all signals are delivered to a processing center which has limited computational capabilities. We present an upper bound on the achievable weighted sum rate for a given computational complexity. We also derive a simple and practical optimization scheme that is based on asymptotic results for the spectral efficiency of a typical link in a Poison point process model. The derived scheme results in a computational savings over schemes that use a fixed number of cooperating base stations for each mobile. The suggested scheme also enables an easy tuning of the computational complexity, according to system needs and energy requirements.
Designing of a kind of Impedance Controller for Industrial Robots to Assemble Satellites

Traditional satellite assembly process mainly relies on manpower, so that there is an urgent need to introduce the robot system which is able to assist workers to assembling satellites. The system draws on the application of technology and process specifications of industrial robots and other manipulators in the relevant industries. In this paper, the impedance controller is used to control the movement of the robot in response to human's traction. The influence of the impedance parameters on the operation process is analyzed by simulation. Results of the experiment shows that the robot has good compliance to the external force and is able to assist people in accomplishing the satellite assembly.
Emerin Deregulation Links Nuclear Shape Instability to Metastatic Potential.

Abnormalities in nuclear shape are a well-known feature of cancer, but their contribution to malignant progression remains poorly understood. Here, we show that depletion of the cytoskeletal regulator, Diaphanous-related formin 3 (DIAPH3), or the nuclear membrane-associated proteins, lamin A/C, in prostate and breast cancer cells, induces nuclear shape instability, with a corresponding gain in malignant properties, including secretion of extracellular vesicles that contain genomic material. This transformation is characterized by a reduction and/or mislocalization of the inner nuclear membrane protein, emerin. Consistent with this, depletion of emerin evokes nuclear shape instability and promotes metastasis. By visualizing emerin localization, evidence for nuclear shape instability was observed in cultured tumor cells, in experimental models of prostate cancer, in human prostate cancer tissues, and in circulating tumor cells from patients with metastatic disease. Quantitation of emerin mislocalization discriminated cancer from benign tissue and correlated with disease progression in a prostate cancer cohort. Taken together, these results identify emerin as a mediator of nuclear shape stability in cancer and show that destabilization of emerin can promote metastasis.Significance: This study identifies a novel mechanism integrating the control of nuclear structure with the metastatic phenotype, and our inclusion of two types of human specimens (cancer tissues and circulating tumor cells) demonstrates direct relevance to human cancer.Graphical Abstract: http://cancerres.aacrjournals. org/content/canres/78/21/6086/F1.large. jpg Cancer Res; 78(21); 6086-97. ©2018 AACR.
Allosteric modulation of the catalytic VYD loop in Slingshot by its N-terminal domain underlies both Slingshot auto-inhibition and activation

Slingshots are phosphatases that modulate cytoskeleton dynamics, and their activities are tightly regulated in different physiological contexts. Recently, abnormally elevated Slingshot activity has been implicated in many human diseases, such as cancer, Alzheimer's disease, and vascular diseases. Therefore, Slingshot-specific inhibitors have therapeutic potential. However, an enzymological understanding of the catalytic mechanism of Slingshots and of their activation by actin is lacking. Here, we report that the N-terminal region of human Slingshot2 auto-inhibits its phosphatase activity in a noncompetitive manner. pH-dependent phosphatase assays and leaving-group dependence studies suggested that the N-terminal domain of Slingshot2 regulates the stability of the leaving group of the product during catalysis by modulating the general acid Asp361 in the catalytic VYD loop. F-actin binding relieved this auto-inhibition and restored the function of the general acid. Limited tryptic digestion and biophysical studies identified large conformational changes in Slingshot2 after the F-actin binding. The dissociation of N-terminal structural elements, including Leu63, and the exposure of the loop between α-helix-2 and β-sheet-3 of the phosphatase domain served as the structural basis for Slingshot activation via F-actin binding in vitro and via neuregulin stimulation in cells. Moreover, we designed a FlAsH-BRET–based Slingshot2 biosensor whose readout was highly correlated with the in vivo phosphatase activities of Slingshot2. Our results reveal the auto-inhibitory mechanism and allosteric activation mechanisms of a human Slingshot phosphatase. They also contribute to the design of new strategies to study Slingshot regulation in various cellular contexts and to screen for new activators/inhibitors of Slingshot activity.
Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ’s high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent’s capability of adapting to a changing environment is tested.
Investigating Human + Machine Complementarity for Recidivism Predictions

When might human input help (or not) when assessing risk in fairness domains? Dressel and Farid (2018) asked Mechanical Turk workers to evaluate a subset of defendants in the ProPublica COMPAS data for risk of recidivism, and concluded that COMPAS predictions were no more accurate or fair than predictions made by humans. We delve deeper into this claim to explore differences in human and algorithmic decision making. We construct a Human Risk Score based on the predictions made by multiple Turk workers, characterize the features that determine agreement and disagreement between COMPAS and Human Scores, and construct hybrid Human+Machine models to predict recidivism. Our key finding is that on this data set, Human and COMPAS decision making differed, but not in ways that could be leveraged to significantly improve ground-truth prediction. We present the results of our analyses and suggestions for data collection best practices to leverage complementary strengths of human and machines in the fairness domain.
Automatic Document Summarization based on Statistical Information

This paper presents a comparative perspective in the field of automatic text summarization algorithms. The main contribution is the implementation of well-known algorithms and the comparison of different summarization techniques on corpora of news articles parsed from the web. The work compares three summarization techniques based on TextRank algorithm, namely: General TextRank, BM25, LongestCommonSubstring. For experiments, we used corpora based on news articles written in Russian and Kazakh. We implemented and experimented well-known algorithms, but we evaluated them differently from previous work in summary evaluation. In this research, we propose a summary evaluation method based on keywords extracted from the corpora. We describe the application of statistical information, show results of summarization processes and provide their comparison.
Top-K Similar Access Behavior Based On Structural Equivalence

The number of online business transactions and Social networks on World Wide Web are rapidly increasing and producing enormous amounts of data. Mining and analyzing relevant data from the web log data for user's navigational behavior has a great significance in the area of data mining. In this paper, we aim to find top-k similar access behavior for an IP_address-Product network in the context of object-to-object relationship. We define similar behavior of users through distinct IP addresses about different categories in the context of node behavior of a bipartite network. As a part of user navigational behavior, we present (i) Top-k similar (Toks) algorithm which is the baseline to mine at most top-k IP addresses for each product category, and an efficient algorithm, (ii) A Recursive Approach for Top-k Similar IP addresses (RAFTS), which mines top-k IP addresses for each and every product category based on method of early associates. We run both the algorithms on four distinct networks and compare performance in terms of running time and memory. The algorithm RAFTS performs better than the baseline algorithm regarding running time and memory consumption. The above algorithms are useful to forecast the similar visiting behavior of the users through distinct IP addresses about different categories. This leads to improve the quality of products by identifying less influence able products.
A Review of Blood Vessel Segmentation Techniques

Due to the increasing demand for competent vessel segmentation techniques, it is important to review some of the available blood vessel segmentation techniques. This paper presents a survey of state-of-art vessel segmentation techniques. In this paper, blood vessel segmentation techniques are categorized into three classes. These are; model based segmentation approaches, tracking based segmentation approaches and pattern recognition approaches. The three categories identified are never independent. Some of the methods are combined with others to aid segmentation. A contextual analysis is done exploring techniques in-terms of strengths and weaknesses. The summary highlights research gaps that need attention.
A Machine Learning Approach for Real-time Battery Optimal Operation Mode Prediction and Control

This paper introduces a machine learning approach for real-time battery optimal operation mode prediction in residential PV applications. First, from the historical data, the optimal battery operation mode for each operation interval is derived. Then, a best performing algorithm for the prediction of the optimal modes is obtained. Performances are tested with different number of features in the training test and different training lengths. Then, the features will be used to predict future operation mode in real-time operations. A comparison on bill savings is made with the model-predictive control approach using the residential load and PV data from the Pecan Street project website under the Hawaiian electricity rate. Simulation results show a 9 points improvement in performance.
Optimal Power Dispatching for Local Area Packetized Power Network

DC power packet transmission enables a large penetration of distributed energy resources and reliable end-to-end power dispatching. In this paper, we consider a local area packetized power network (LAPPN), where a multi-channel power router links all the subscribers and allows multiple demander-supplier pairs to interchange power packets simultaneously. To realize an efficient power dispatching in a dc packetized power network, effective subscriber matching, and transmission scheduling need to be developed. We design a packetized power dispatching protocol to regulate the LAPPN operation, where the involved subscribers are matched into demander-supplier pairs first, and the matched pairs’ transmission tasks are then orderly scheduled onto multiple power channels of the router. Based on the matching theory, we formulate the subscriber matching as a standard matching problem to maximize each subscriber’s benefit and develop a deferred acceptance algorithm to solve the problem. We also formulate the transmission scheduling problem and propose a heuristic solution to obtain the real-time balance between load and generation within an LAPPN, taking into account the urgency of power transmission and the fairness among the subscribers. Simulation results confirm the effectiveness of the proposed protocol in realizing a highly utilized and efficient LAPPN power dispatching.