Miracle-dz/UniFAR-FTUs huggingface dataset:
MECHANOMEDICINE: applications of mechanobiology to medical sciences and next-generation medical technologies

Mechanical stress underlies most aspects of cell and organismal biology. Mechanomedicine is a field of biology that seeks to understand molecular, cellular, tissue, organ, and individual responses to mechanical stimuli and aims to apply the gained knowledge to improve health. Combining biology and engineering, we explore research areas including mechanosensitive ion channels, heart failure, and regenerative medicine.This review will describe our findings in mechanobiology, our establishment of a joint venture business as we developed devices responding to medical needs, and our alliance with other companies.
Treatment outcomes of ureteral stenting for malignant extrinsic ureteral obstruction: a comparison between polymeric and metallic stents

Purpose To compare treatment outcomes, more specifically patency rate, of polymeric and metallic stents for malignant ureteral obstruction. Patients and methods Between August 2007 and September 2017, we retrospectively analyzed the data of 92 patients (126 ureters) having a diagnosis of malignant extrinsic ureteral obstruction treated with indwelling ureteral stents (polymeric and full-length metallic stents). Of these patients, 35 (54 ureters) were treated with polymeric stents and 57 (72 ureters) with a Resonance® metallic stent. The observation period was censored to 1 year. Survival rate in cases of malignant ureteral obstruction was calculated, and the relationship between the causes of ureteral obstruction, the stent type, and the patency rate was evaluated. Results The median observation period was 145 days, with a median survival time of 258 days. The stent patency rate was 70.9% at 1 year, regardless of stent type. Stent occlusion was observed in 20 patients (33 ureters). According to stent type, occlusion of the polymeric and metallic stents was identified in 12 (22%) and 8 (11%) cases, respectively. The clinical features associated with stent failure were assessed. In univariate analysis, the patency rate was significantly better for the metallic stent than for the polymeric stent (1-year patency rate; 78.4%, 61.1%, respectively, HR, 2.15; 95% CI, 1.07–4.33; p=0.031). However, the patency rate among patients with abdominal dissemination, lymph node metastasis, and direct compression by tumor was not significantly different. Conclusion Indwelling ureteral stents, particularly metallic stents, are effective for the treatment of malignant ureteral obstruction.
Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship

Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm.
Optimal Selection of SRAM Bit-Cell Size for Power Reduction in Video Compression

In mobile multimedia devices with video compression capability, a large amount of power consumption is incurred by storing video data in static random-access memories (SRAMs). The power consumption by SRAM access is reduced by decreasing the supply voltage but the decreased supply voltage may cause data loss stored in the SRAM. To reduce the probability of data loss, previous research attempts to increase the SRAM cell size, which incurs a large area overhead. To minimize the area penalty by increasing the SRAM cell size, this paper proposes a novel method to adaptively select the cell sizes of SRAMs according to their sensitivities to the quality degradation in video compression while maintaining the total SRAM area. In order to decide the optimal size for embedded SRAMs, optimization based on Lagrange multipliers and Karush–Kuhn–Tucker conditions is proposed. The proposed optimization significantly reduces the number of simulations necessary to determine the optimal combination of SRAM bit-cell sizes. By applying the proposed heterogeneous SRAM cell sizing scheme along with the proposed optimization scheme, the best Bjontegaard delta peak signal-to-noise ratio (BDPSNR) improvement is achieved. Simulation results show that the proposed approach remarkably improves the video quality by up to 3.72 dB in BDPSNR compared with the conventional SRAM with an identical cell size. These results imply that the proposed heterogeneous SRAM allows a reduction of the supply voltage while maintaining the video quality.
I/O Characteristics Discovery in Cloud Storage Systems

The data growth from many applications in clouds poses significant challenges to cloud storage systems. To deliver the best storage and I/O performance possible, it is often required to understand and leverage the I/O characteristics based on data accesses. A number of research studies have been carried out on this topic. However, most of them either utilize a limited number of data-access attributes, restricting the general applicability of the method for different applications, or heavily rely on the domain knowledge or expertise about applications' I/O behaviors to select the best representative features, introducing bias for certain workloads. To overcome these limitations, in this study, we present a new I/O characteristic discovery methodology. This method enables capturing data-access features as many as possible to eliminate human bias. It utilizes a machine-learning based strategy to derive the most important set of features automatically, and groups data objects with a clustering algorithm (DBSCAN) to reveal I/O characteristics discovered. These I/O characteristics revealed can direct I/O performance optimizations in numerous scenarios, such as in data prefeteching and data reorganization optimizations in cloud storage systems.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information. However, current relevance computing based on meta-graph only considers the complex structural information, but ignores its embedded meta-paths information. To address this problem, we proposeMEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively. The MEGA model uses normalized relevance or similarity measures that are derived from a meta-graph and its embedded meta-paths between nodes simultaneously, and then leverages tensor decomposition method to perform node embedding. The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta information of a meta-graph. Extensive experiments on two real datasets demonstrate that MEGA and MEGA++ are more effective than state-of-the-art approaches.
Low-Level Nighttime Light Therapy for Age-Related Macular Degeneration: A Randomized Clinical Trial.

Purpose To investigate the safety, acceptability, and effectiveness of light therapy on the progression of AMD over 12 months. Methods This was a phase I/IIa, prospective, proof-of-concept, single-center, unmasked randomized controlled trial. Sixty participants (55 to 88 years) with early AMD in the study eye and neovascular AMD (nAMD) in the fellow eye were recruited from a hospital nAMD clinic. Eligible participants were randomized (ratio 1:1) to receive light therapy or to an untreated control group. Light therapy was delivered via a light-emitting mask (peak 505 nm, 23 scotopic Td), which was worn each night for 12 months. Co-primary outcome measures were disease progression (onset of nAMD or increased drusen volume beyond test-retest limits) and change in time constant of cone dark adaptation. Other main outcomes included adverse events, compliance, and subjective sleep quality data. Results Disease progression over 12 months was seen in 38.1% (18.1%-61.6% confidence interval [CI]) of intervention participants and 48.3% (29.4%-67.5% CI) of controls (Mantel-Haenszel test, common odds ratio = 0.763, P = 0.495). A significantly larger delay in cone adaptation was observed in the intervention group (1.66 ± 0.61 minutes) than in the control group (0.66 ± 0.49 minutes) over the follow-up period. No reported adverse events were deemed to be associated with the intervention. Conclusions Although acceptable to the patients, light therapy did not have a substantial effect on the progression of early AMD over 12 months. Further investigation is necessary to discover the permanency and cause of the adverse effect of light therapy on dark adaptation.
Pose Aware Fine-Grained Visual Classification Using Pose Experts

We focus on the problem of fine-grained visual classification (FGVC). We posit that unreasonable effectiveness of the state-of-the-art in this area is because of similar object categories present in the ImageNet dataset, which allows such models to be pretrained on a much larger set of samples and learn generic features for those object categories. We observe an important and often ignored additional structure present in an FGVC problem: the objects are captured from a small set of viewing angles only. We notice that subtle differences between object categories are difficult to pick from an arbitrary angle but easier to identify from a similar pose. We show in this paper that training specialized pose experts, focusing on classification from a single, fixed pose, and combining them in an ensemble style framework successfully exploits the structure in the problem. We demonstrate the effectiveness of the proposed approach on the benchmark Stanford Cars, FGVC-Aircrafts, and DeepFashion datasets. To highlight the contribution when the target category features may not be available in a pretrained network, we test on footwear class. We contribute a new 1000 object, 12 category footwear dataset, each object captured from 4 different poses and show significant improvement on this dataset.
Clinical prediction of thrombectomy eligibility: A systematic review and 4-item decision tree

Background A clinical large anterior vessel occlusion (LAVO)-prediction scale could reduce treatment delays by allocating intra-arterial thrombectomy (IAT)-eligible patients directly to a comprehensive stroke center. Aim To subtract, validate and compare existing LAVO-prediction scales, and develop a straightforward decision support tool to assess IAT-eligibility. Methods We performed a systematic literature search to identify LAVO-prediction scales. Performance was compared in a prospective, multicenter validation cohort of the Dutch acute Stroke study (DUST) by calculating area under the receiver operating curves (AUROC). With group lasso regression analysis, we constructed a prediction model, incorporating patient characteristics next to National Institutes of Health Stroke Scale (NIHSS) items. Finally, we developed a decision tree algorithm based on dichotomized NIHSS items. Results We identified seven LAVO-prediction scales. From DUST, 1316 patients (35.8% LAVO-rate) from 14 centers were available for validation. FAST-ED and RACE had the highest AUROC (both >0.81, p < 0.01 for comparison with other scales). Group lasso analysis revealed a LAVO-prediction model containing seven NIHSS items (AUROC 0.84). With the GACE (Gaze, facial Asymmetry, level of Consciousness, Extinction/inattention) decision tree, LAVO is predicted (AUROC 0.76) for 61% of patients with assessment of only two dichotomized NIHSS items, and for all patients with four items. Conclusion External validation of seven LAVO-prediction scales showed AUROCs between 0.75 and 0.83. Most scales, however, appear too complex for Emergency Medical Services use with prehospital validation generally lacking. GACE is the first LAVO-prediction scale using a simple decision tree as such increasing feasibility, while maintaining high accuracy. Prehospital prospective validation is planned.
Face Recognition In Unconstrained Environment

Face recognition has been a challenging research problem to date due to its applications. Researchers are currently focusing on a rare number of images, various face poses, and illuminations. This paper proposes a new promising approach to cope with the face recognition in unconstrained environment. It uses a single face image in various poses (non-frontal face) and various illuminations. It applied Histogram Equalization (HE) to get rid of illuminations and using mirrored images to augment the number of face images. In addition, to cope with non-frontal face with various poses, the face model using Active Appearance Model (AAM) is implemented. The result of AAM is then oriented to the frontal face for feature extraction using Histograms of Oriented Gradients (HOG). For classification, Support Vector Machines (SVM) is implemented. In simulation on FERET benchmark database, the proposed approach provided the outstanding results at 90.12% in accuracy on average superior to the compared method.
Dynamic Edge Caching with Popularity Drifting

Caching at the network edge devices such as wireless caching stations (WCS) is a key technology in the 5G network. The spatial-temporal diversity of content popularity requires different content to be cached in different WCSs and periodically updated to adapt to temporal changes. In this paper, we study how the popularity drifting speed affects the number of required broadcast transmissions by the MBS and then design coded transmission schemes by leveraging the broadcast advantage under the index coding framework. The key idea is that files already cached in WCSs, which although may be currently unpopular, can serve as side information to facilitate coded broadcast transmission for cache updating. Our algorithm extends existing index coding-based schemes from a single-request scenario to a multiple-request scenario via a “dynamic coloring” approach. Simulation results indicate that a significant bandwidth saving can be achieved by adopting our scheme.
Challenges in using Cylindrical Antenna Array in Modern MIMO Applications

The article discusses the functioning of the radio direction-finding methods in the system of cylindrical antenna arrays formed of the designed radiators, directional factor which is not equal to 1. The accuracy of determining the angular azimuth coordinates with the direction-finding method with super-resolution MUSIC is estimated there in azimuth plane. In addition, this article discusses the possibility of using a cylindrical antenna array with directional emitters for using in modern MIMO applications as well as for digital beamforming and direction-of-arrival estimation. The problems such as false peaks are considered in consideration of directive factor.
Adaptive Trip Recommendation System: Balancing Travelers among POIs with MapReduce

Travel recommendation systems provide suggestions to the users based on different information, such as user preferences, needs, or constraints. The recommendation may also take into account some characteristics of the points of interest (POIs) to be visited, such as the opening hours, or the peak hours. Although a number of studies have been proposed on the topic, most of them tailor the recommendation considering the user viewpoint, without evaluating the impact of the suggestions on the system as a whole. This may lead to oscillatory dynamics, where the choices made by the system generate new peak hours. This paper considers the trip planning problem that takes into account the balancing of users among the different POIs. To this aim, we consider the estimate of the level of crowding at POIs, including both the historical data and the effects of the recommendation. We formulate the problem as a multi-objective optimization problem, and we design a recommendation engine that explores the solution space in near real-time, through a distributed version of the Simulated Annealing approach. Through an experimental evaluation on a real dataset, we show that our solution is able to provide high quality recommendations, yet maintaining that the attractions are not overcrowded.
“It’s a disease of families”: Neurologists’ insights on how to improve communication and quality of life for families of Parkinson’s disease patients

Objectives Parkinson’s disease presents an evolving challenge for patients and families due to an unpredictable disease trajectory and symptoms that complicate social interactions. In this study, we explore neurologists’ perspectives on the challenges Parkinson’s disease presents for families and the strategies they use to improve communication and quality of life. Methods We conducted hour-long semi-structured interviews with 16 neurologists at 4 care delivery institutions in the San Francisco Bay Area, focusing on techniques neurologists use to support families through the Parkinson’s disease journey. Results Neurologists identified strategies for addressing caregiver–patient disagreements around symptom accuracy and negotiating driving safety. Family education is needed to contextualize patient symptoms and to identify psychosocial support resources. Unmet caregiver needs remain, particularly in the form of psychosocial support, respite care and support for unequal gender dynamics in the Parkinson’s disease caregiving experience. Discussion Family members of Parkinson’s disease patients face unique caregiving and interpersonal challenges due to the nature of the disease. Targeted education and structural support are needed to alleviate current burdens and allow for improved patient- and family-centered care.
Adaptive Event-Triggered Transmission Scheme and $H_{\infty}$ Filtering Co-Design Over a Filtering Network With Switching Topology

This paper addresses the distributed adaptive event-triggered ${H}_{\infty}$ filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining ${H}_{\infty}$ filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.
Psychiatric adverse events in oseltamivir prophylaxis trials: Novel comparative analysis using data obtained from clinical study reports

Estimating the rate of adverse events (AEs) caused by a treatment in clinical trials typically involves comparing the proportions of patients experiencing AEs in intervention and control groups. However, potentially important information, including duration, recurrence, and intensity of events, is lost. In this study, we illustrate how the additional information can be obtained and incorporated into analyses of AEs.
A Cross-Virtual Machine Network Channel Attack via Mirroring and TAP Impersonation

Data privacy and security is a leading concern for providers and customers of cloud computing, where Virtual Machines (VMs) can co-reside within the same underlying physical machine. Side channel attacks within multi-tenant virtualized cloud environments are an established problem, where attackers are able to monitor and exfiltrate data from co-resident VMs. Virtualization services have attempted to mitigate such attacks by preventing VM-to-VM interference on shared hardware by providing logical resource isolation between co-located VMs via an internal virtual network. However, such approaches are also insecure, with attackers capable of performing network channel attacks which bypass mitigation strategies using vectors such as ARP Spoofing, TCP/IP steganography, and DNS poisoning. In this paper we identify a new vulnerability within the internal cloud virtual network, showing that through a combination of TAP impersonation and mirroring, a malicious VM can successfully redirect and monitor network traffic of VMs co-located within the same physical machine. We demonstrate the feasibility of this attack in a prominent cloud platform - OpenStack - under various security requirements and system conditions, and propose countermeasures for mitigation.
A Novel Automatic Sentiment Summarization from Aspect-based Customer Reviews

Online reviews play an important role in helping companies or governments to improve product quality and services. However, these reviews are increasing day by day. It is difficult to go through the amount of these reviews and to summarize the important information manually. We proposed a novel Automatic Sentiment Summarization (ASS) system. This system has two phases. The first phase is the aspect-based representation used to represent ranked knowledge on aspect opinion calculated by using frequencies, polarity, and opinion strength. The second phase is the review summary generation used to automatically produce review summary by ranking aspect based on information of the aspect. The generated summary is more coherent by applying natural language generation technique. Furthermore, the proposed ASS system allows users to add new reviews in the same domain in order to update the generated summary. The experiments used the sentiment aspect dataset benchmarks such as customer product/service reviews for Canon, Nikon, and Laptop. The generated summaries from the proposed ASS system are well performed compared with other systems extractive summarization and abstractive summarization.