Miracle-dz/UniFAR-FTUs huggingface dataset:
The cost impact to Medicare of shifting treatment of worsening heart failure from inpatient to outpatient management settings

Purpose The aim of this study was to quantify the potential cost savings to Medicare of shifting the site of treatment for worsening heart failure (HF) from inpatient to outpatient (OP) settings for a subset of worsening HF episodes among the Medicare fee-for-service (FFS) population. Materials and methods A cross-sectional analysis of a random 5% sample of 2014 FFS Medicare beneficiaries was conducted. Incidence and cost of worsening HF episodes in both inpatient and OP settings were identified. These results were used to calculate cost savings associated with shifting a proportion of worsening HF episodes from the inpatient to OP settings. Results A total of 151,908 HF beneficiaries were identified. The estimated annual cost for the treatment of worsening HF across both inpatient and OP settings ranged from US$9.3 billion to US$17.0 billion or 2.4%–4.3% of total Medicare FFS spend. The cost saving associated with shifting worsening HF treatment from inpatient hospital setting to OP settings was US$667.5 million or 0.17% of total Medicare spend when 10% of HF admissions were targeted and 60% of targeted HF admissions were successfully shifted. The cost savings increased to US$2.098 billion or 0.53% of total Medicare spend when 20% of HF admissions were targeted and 90% of targeted HF admissions were successfully shifted. Conclusion Treatment options that can shift costly hospital admissions for worsening HF treatment to less expensive OP settings potentially lead to significant cost savings to Medicare. Pursuit of OP therapy options for treating worsening HF might be considered a viable alternative.
Inhibition of bacterial attachment and biofilm formation by a novel intravenous catheter material using an in vitro percutaneous catheter insertion model

Introduction Despite sterile barrier precautions and vigorous skin antisepsis, percutaneous insertion of intravenous catheters has been shown to result in attachment to the catheter surface of bacteria residing in the deep structures of the skin. Such attachment poses the risk of biofilm formation and eventual catheter-related bloodstream infection (CRBSI). This study was undertaken to assess whether the non-coated surface treatment of a unique catheter material (ChronoFlex C® with BioGUARD™) could inhibit bacterial attachment and biofilm formation. Methods A novel in vitro model and fluorescence microscopy were used to compare two intravascular catheter materials with respect to bacterial attachment and biofilm formation. The control material was a commonly used polyurethane. The study material was a unique copolymer, treated so as to remove surface additives, alter hydrophobicity and create surface micro-patterning. Outcomes were assessed using both a membrane potential indicator and a cell death reporter with appropriate fluorescent channels. Thus, bacterial cells attached to the catheter surface (living and dead) were imaged without mechanical disruption. Results Both bacterial attachment and biofilm formation are significantly inhibited by the study catheter material. In fact, over 5 times more bacteria were able to attach and grow on the control polyurethane material than on the study material (P=0.0020). Moreover, those few bacteria that were able to attach to the study material had a 1.5 times greater likelihood of dying. Conclusion Using a novel in vitro percutaneous catheter insertion model, ChronoFlex C with BioGUARD is proven to significantly inhibit bacterial attachment and biofilm formation as compared with a commonly used polyurethane catheter material.
Comparison analysis of left-side versus right-side resection in bismuth type III hilar cholangiocarcinoma

Backgrounds/Aims Several studies report worse prognosis after left-side compared to right-side liver resection in patients with perihilar cholangiocarcinoma. In this study, we compared outcomes of left-side and right-side resections for Bismuth type III hilar cholangiocarcinoma and analyzed factors affecting survival. Methods From May 1995 to December 2012, 179 patients underwent surgery at Samsung Medical Center for type III hilar cholangiocarcinoma. Among these patients, 138 received hepatectomies for adenocarcinoma with curative intent: 103 had right-side resections (IIIa group) and 35 had left-side resections (IIIb group). Perioperative demographics, morbidity, mortality, and overall and disease-free survival rates were compared between the groups. Results BMI was higher in the IIIa group (24±2.6 kg/m2 versus 22.7±2.8 kg/m2; p=0.012). Preoperative portal vein embolization was done in 23.3% of patients in the IIIa group and none in the IIIb group. R0 rate was 82.5% in the IIIa group and 85.7% in the IIIb group (p=0.796) and 3a complications by Clavien-Dindo classification were significantly different between groups (10.7% for IIIa versus 23.3% for IIIb; p=0.002). The 5-year overall survival rate was 33% in the IIIa group and 35% in the IIIb group (p=0.983). The 5-year disease-free survival rate was 28% in the IIIa group and 29% in the IIIb group (p=0.706). Advanced T-stages 3 and 4 and LN metastasis were independent prognostic factors for survival and recurrence by multivariate analysis. Conclusions No significant differences were seen in outcomes by lesion side in patients receiving curative surgery for Bismuth type III hilar cholangiocarcinoma.
Treatment and clinical outcomes of patients relapsing after allogeneic hematopoietic cell transplantation for myelodysplastic syndrome

Background Although allogeneic hematopoietic cell transplantation (HCT) is the only curative treatment option for myelodysplastic syndrome (MDS), a substantial number of patients experience relapse. We reviewed the clinical outcomes of patients with MDS who relapsed after allogeneic HCT. Methods Thirty patients who experienced relapse or progression after allogeneic HCT for MDS between July 2000 and May 2016 were included in this retrospective analysis. Results The median time from HCT to relapse was 6.6 (range, 0.9–136.3) months. Donor lymphocyte infusions (DLIs) were administered to four patients: one achieved complete remission (CR) and survived disease free, while three did not respond to DLI and died. Hypomethylating agents were administered to seven patients: one who had stable disease continuously received decitabine, while six died without response to treatment. Six patients received AML-like intensive chemotherapy, and three achieved CR: two underwent second HCT and one DLI. One patient receiving second HCT survived without disease, but the other two relapsed and died. Three, four, and eight patients who did not respond to intensive chemotherapy, low-dose cytarabine, and best supportive care, respectively, died. One patient who underwent second HCT following cytogenetic relapse survived disease free. Median overall survival after relapse was 4.4 months, and relapse within 6 months after HCT was associated with shorter survival. Conclusion Outcomes of MDS patients relapsing after allogeneic HCT were disappointing. Some patients could be saved using DLI or second HCT.
Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.
Distributed Economic Dispatch for Energy Internet Based on Multiagent Consensus Control

We consider the economic dispatch (ED) for an Energy Internet composed of energy routers (ERs), interconnected microgrids, and main grid. The microgrid consists of several bus nodes associated with distributed generators (DGs) and intelligent control units (ICUs). We propose a distributed ED algorithm for the grid-connected microgrid, where each ICU iterates the estimated electricity price of the distribution system and the estimation for the average power mismatch of the whole microgrid by leader-following and average-consensus algorithms, respectively. The ER iterates the incremental power exchanged with the distribution system. By constructing an auxiliary consensus system, we prove that if the communication topology of the Energy Internet contains a spanning tree with the ER as the root and there is a path from each ICU to the ER, then the estimated electricity price of the distribution system converges to its real value, the power supply and demand achieves balance and the ED achieves optimal asymptotically. Furthermore, we propose an autonomous distributed ED algorithm covering both grid-connected and isolated modes of the microgrid by feeding back the estimated average power mismatch for updating the incremental costs with penalty factor. It is proved that if the communication topology of the microgrid is connected and there exists an ICU bidirectionally neighboring the ER, then the microgrid can switch between the two modes reliably. The simulation results demonstrate the effectiveness of the proposed algorithms.
Self-Attention Equipped Graph Convolutions for Disease Prediction

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such diverse data gives complementary information about the patient’s condition to make an informed diagnosis. A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction. We propose a graph convolution based deep model which takes into account the distinctiveness of each element of the multi-modal data. We incorporate a novel self-attention layer, which weights every element of the demographic data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and performance when compared to state-of-the-art methods. Our method outperforms other methods with a significant margin.
Efficiently list‐edge coloring multigraphs asymptotically optimally

We give polynomial time algorithms for the seminal results of Kahn, who showed that the Goldberg–Seymour and list‐coloring conjectures for (list‐)edge coloring multigraphs hold asymptotically. Kahn's arguments are based on the probabilistic method and are non‐constructive. Our key insight is that we can combine sophisticated techniques due to Achlioptas, Iliopoulos, and Kolmogorov for the analysis of local search algorithms with correlation decay properties of the probability spaces on matchings used by Kahn in order to construct efficient edge‐coloring algorithms.
Exploratory Data Analysis of a Network Telescope Traffic and Prediction of Port Probing Rates

Understanding the properties exhibited by large scale network probing traffic would improve cyber threat intelligence. In addition, the prediction of probing rates is a key feature for security practitioners in their endeavors for making better operational decisions and for enhancing their defense strategy skills. In this work, we study different aspects of the traffic captured by a /20 network telescope. First, we perform an exploratory data analysis of the collected probing activities. The investigation includes probing rates at the port level, services interesting top network probers and the distribution of probing rates by geolocation. Second, we extract the network probers exploration patterns. We model these behaviors using transition graphs decorated with probabilities of switching from a port to another. Finally, we assess the capacity of Non-stationary Autoregressive and Vector Autoregressive models in predicting port probing rates as a first step towards using more robust models for better forecasting performance.
Portable, Data-Driven Malware Detection using Language Processing and Machine Learning Techniques on Behavioral Analysis Reports

In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing binary samples to produce reports that summarise their runtime behaviors. The investigator uses these reports to detect malware and attribute threat type leveraging manually chosen features. However, the diversity of malware and the execution environments makes manual approaches not scalable because the investigator needs to manually engineer fingerprinting features for new environments. In this paper, we propose, MalDy (mal~die), a portable (plug and play) malware detection and family threat attribution framework using supervised machine learning techniques. The key idea of MalDy portability is the modeling of the behavioral reports into a sequence of words, along with advanced natural language processing (NLP) and machine learning (ML) techniques for automatic engineering of relevant security features to detect and attribute malware without the investigator intervention. More precisely, we propose to use bag-of-words (BoW) NLP model to formulate the behavioral reports. Afterward, we build ML ensembles on top of BoW features. We extensively evaluate MalDy on various datasets from different platforms (Android and Win32) and execution environments. The evaluation shows the effectiveness and the portability MalDy across the spectrum of the analyses and settings.
EgoReID: Person re-identification in Egocentric Videos Acquired by Mobile Devices with First-Person Point-of-View

Widespread use of wearable cameras and recording devices such as cellphones have opened the door to a lot of interesting research in first-person Point-of-view (POV) videos (egocentric videos). In recent years, we have seen the performance of video-based person Re-Identification (ReID) methods improve considerably. However, with the influx of varying video domains, such as egocentric videos, it has become apparent that there are still many open challenges to be faced. These challenges are a result of factors such as poor video quality due to ego-motion, blurriness, severe changes in lighting conditions and perspective distortions. To facilitate the research towards conquering these challenges, this paper contributes a new, first-of-its-kind dataset called EgoReID. The dataset is captured using 3 mobile cellphones with non-overlapping field-of-view. It contains 900 IDs and around 10,200 tracks with a total of 176,000 detections. Moreover, for each video we also provide 12-sensor meta data. Directly applying current approaches to our dataset results in poor performance. Considering the unique nature of our dataset, we propose a new framework which takes advantage of both visual and sensor meta data to successfully perform Person ReID. In this paper, we propose to adopt human body region parsing to extract local features from different body regions and then employ 3D convolution to better encode temporal information of each sequence of body parts. In addition, we also employ sensor meta data to determine target's next camera and their estimated time of arrival, such that the search is only performed among tracks present in the predicted next camera around the estimated time. This considerably improves our ReID performance as it significantly reduces our search space.
Functional importance of noise in neuronal information processing

Noise is an inherent part of neuronal dynamics, and thus of the brain. It can be observed in neuronal activity at different spatiotemporal scales, including in neuronal membrane potentials, local field potentials, electroencephalography, and magnetoencephalography. A central research topic in contemporary neuroscience is to elucidate the functional role of noise in neuronal information processing. Experimental studies have shown that a suitable level of noise may enhance the detection of weak neuronal signals by means of stochastic resonance. In response, theoretical research, based on the theory of stochastic processes, nonlinear dynamics, and statistical physics, has made great strides in elucidating the mechanism and the many benefits of stochastic resonance in neuronal systems. In this perspective, we review recent research dedicated to neuronal stochastic resonance in biophysical mathematical models. We also explore the regulation of neuronal stochastic resonance, and we outline important open questions and directions for future research. A deeper understanding of neuronal stochastic resonance may afford us new insights into the highly impressive information processing in the brain.
Applying Predicate Detection to the Constrained Optimization Problems

We present a method to design parallel algorithms for constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem. These three problems are solved in the literature using Gale-Shapley algorithm, Dijkstra's algorithm, and Demange, Gale, Sotomayor algorithm. Our method solves all these problems by casting them as searching for an element that satisfies an appropriate predicate in a distributive lattice. Moreover, it solves generalizations of all these problems - namely finding the optimal solution satisfying additional constraints called {\em lattice-linear} predicates. For stable marriage problems, an example of such a constraint is that Peter's regret is less than that of Paul. For shortest path problems, an example of such a constraint is that cost of reaching vertex $v_1$ is at least the cost of reaching vertex $v_2$. For the market clearing price problem, an example of such a constraint is that $item_1$ is priced at least as much as $item_2$. In addition to finding the optimal solution, our method is useful in enumerating all constrained stable matchings, and all constrained market clearing price vectors.
Similarity R-C3D for Few-shot Temporal Activity Detection

Many activities of interest are rare events, with only a few labeled examples available. Therefore models for temporal activity detection which are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video. Our model is end-to-end trainable and can benefit from more few-shot examples. At test time, each proposal is assigned the label of the few-shot activity class corresponding to the maximum similarity score. Our Similarity R-C3D method outperforms previous work on three large-scale benchmarks for temporal activity detection (THUMOS14, ActivityNet1.2, and ActivityNet1.3 datasets) in the few-shot setting. Our code will be made available.
Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement

In recent years, Long Short-Term Memory (LSTM) has become a popular choice for speech separation and speech enhancement task. The capability of LSTM network can be enhanced by widening and adding more layers. However, this would introduce millions of parameters in the network and also increase the requirement of computational resources. These limitations hinders the efficient implementation of RNN models in low-end devices such as mobile phones and embedded systems with limited memory. To overcome these issues, we proposed to use an efficient alternative approach of reducing parameters by representing the weight matrix parameters of LSTM based on Tensor-Train (TT) format. We called this Tensor-Train factorized LSTM as TT-LSTM model. Based on this TT-LSTM units, we proposed a deep TensorNet model for single-channel speech enhancement task. Experimental results in various test conditions and in terms of standard speech quality and intelligibility metrics, demonstrated that the proposed deep TT-LSTM based speech enhancement framework can achieve competitive performances with the state-of-the-art uncompressed RNN model, even though the proposed model architecture is orders of magnitude less complex.
Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems.
Texture Deformation Based Generative Adversarial Networks for Face Editing

Despite the significant success in image-to-image translation and latent representation based facial attribute editing and expression synthesis, the existing approaches still have limitations in the sharpness of details, distinct image translation and identity preservation. To address these issues, we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle texture from original image and transfers domains based on the extracted texture. The approach utilizes the texture to transfer facial attributes and expressions without the consideration of the object pose. This leads to shaper details and more distinct visual effect of the synthesized faces. In addition, it brings the faster convergence during training. The effectiveness of the proposed method is validated through extensive ablation studies. We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis. The results on both the CelebA and RaFD datasets suggest that Texture Deformation Based GAN achieves better performance.