Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the location effect of external markers in respiratory‐gated radiotherapy

In 7 lung and breast cancer patients, we investigated the location effect of external markers on the correlation between the motions of external markers and of an internal target under various breathing patterns. Our department developed a tumor tracking system consisting of two infrared cameras and a medical simulator. Using the system, we monitored the simultaneous motions of tumor and external markers placed at various locations on a patient's skin and saved the results for offline analysis. We then used a cross‐covariance approach to analyze the correlation between the motions of individual markers and of the tumor. Based on the additive model, we evaluated the predictability of tumor motion from the motions of the external markers. The effect of marker location on the correlation between the motions of the tumor and of the external markers varied widely from patient to patient. At no specific marker location did the surrogate signal consistently present superior correlation with tumor motion in 3 breathing sessions with 7 patients. When the composite external signal generated from multiple external motion signals was correlated with tumor motion, the quality of the correlation improved significantly. In most cases, the composite signal provided the best surrogate signal for correlating with tumor motion. Correlation between the motions of external markers and of a tumor may be affected by several factors, including patient characteristics, marker locations, and breathing pattern. A single external marker cannot provide sufficient and reliable tracking information for tumor motion. A composite signal generated from the motions of multiple external makers provides an excellent surrogate signal, which in this study demonstrated superior correlation with tumor motion as compared with the signal provided by an individual marker. A composite signal would be a more reliable way to track tumor motion during respiratory‐gated radiotherapy. PACS numbers: 87.53. Jw
Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes

Video segmentation is an important building block for high level applications, such as scene understanding and interaction analysis. While outstanding results are achieved in this field by the state-of-the-art learning and model-based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provide actual world 3D geometry compared with 2D images. The explicit geometry in RGBD data greatly help in computer vision tasks, but the lack of annotations in this type of data may also hinder the extension of learning-based methods to RGBD. In this paper, we present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry in RGBD. Our proposal is only based on low level features, such as connectivity and compactness. We exploit temporal coherence by representing the rough estimation of objects in a single frame with a hierarchical structure and propagating this hierarchy along time. The hierarchical structure provides an efficient way to establish temporal correspondences at different scales of object-connectivity and to temporally manage the splits and merges of objects. This allows updating the segmentation according to the evidence observed in the history. The proposed method is evaluated on several challenging data sets, with promising results for the presented approach.
Enhancing iris authentication on handheld devices using deep learning derived segmentation techniques

In this paper the increasing use of biometrie authentication on handheld devices is considered and the importance of accurate iris segmentation in implementing an embedded authentication workflow based on iris authentication is explained. A deep learning scheme is then developed and an appropriate augmentation method is presented to solve the problem of the iris segmentation task in handheld devices. Initial comparisons with publicly available iris segmentation algorithms show significant performance improvements, particularly on challenging image datasets designed to mimic the image quality obtained from a handheld device.
A study on development of the blind spot detection system for the IoT-based smart connected car

The Internet of things (IoT) is an emerging topic in a variety of industry fields such as manufacturing, engineering, automobile, etc. Especially, many automobile companies are competitively introducing a smart-connected car in conjunction with the IoT technology. For instance, some recent vehicles support autonomous driving using cameras and radar-based safety features such as pre-collision prevention system, lane departure alert with a steering assistant function, etc. Based on this example of the IoT-based smart connected car, we actually developed the camera-based blind spot assistance system. The power / serializer board and camera board consisting of a number of sensors, processors, and other hardware components were developed by our own system interface design, and we confirmed that the developed system successfully met quantitative evaluation criteria through testing. Furthermore, we are proposing improvement plans and an idea of object detection in the blind spot based on a deep learning methodology.
Robust Shortest Path Problem With Distributional Uncertainty

Routing service considering uncertainty is at the core of intelligent transportation systems and has attracted increasing attention. Existing stochastic shortest path models require the exact probability distributions of travel times and usually assume that they are independent. However, the distributions are often unavailable or inaccurate due to insufficient data, and correlation of travel times over different links has been observed. This paper presents a robust shortest path (RSP) model that only requires partial distribution information of travel times, including the support set, mean, variance, and correlation matrix. We introduce a concept of robust mean-excess travel time to hedge against the risk from both the uncertainty of the random travel times and the uncertainty in their distributions. To solve the RSP problem, an equivalent dual formulation is derived and used to design tight lower and upper bound approximation methods, which adopt the scenario approach and semi-definite programming approach, respectively. To solve large problems, we further propose an efficient primal approximation method, which only needs to solve two deterministic shortest path problems and a mean-standard deviation shortest path problem, and analyze its approximation performance. Experiments validate the tightness of the proposed bounds and demonstrate the impact of uncertainty on the relative benefit and cost of robust paths.
Glycyrrhetinic Acid Antagonizes Pressure-Induced Venous Remodeling in Mice

Development of spider veins is caused by the remodeling of veins located in the upper dermis and promoted by risk factors such as obesity or pregnancy that chronically increase venous pressure. We have repeatedly shown that the pressure-induced increase in biomechanical wall stress is sufficient to evoke the formation of enlarged corkscrew-like superficial veins in mice. Subsequent experimental approaches revealed that interference with endothelial- and/or smooth muscle cell (SMC) activation counteracts this remodeling process. Here, we investigate whether the herbal agent glycyrrhetinic acid (GA) is a suitable candidate for that purpose given its anti-proliferative as well as anti-oxidative properties. While basic abilities of cultured venous SMCs such as migration and proliferation were not influenced by GA, it inhibited proliferation but not angiogenic sprouting of human venous endothelial cells (ECs). Further analyses of biomechanically stimulated ECs revealed that GA inhibits the DNA binding capacity of the mechanosensitive transcription factor activator protein-1 (AP-1) which, however, had only a minor impact on the endothelial transcriptome. Nevertheless, by decreasing gelatinase activity in ECs or mouse veins exposed to biomechanical stress, GA diminished a crucial cellular response in the context of venous remodeling. In line with the observed inhibitory effects, local transdermal application of GA attenuated pressure-mediated enlargement of veins in the mouse auricle. In summary, our data identifies GA as an inhibitor of EC proliferation, gelatinase activity and venous remodeling. It may thus have the capacity to attenuate spider vein formation and remodeling in humans.
Primary extremity soft tissue sarcomas: outcome improvement over time at a single institution.

BACKGROUND To assess changes in survival over time of extremity soft tissue sarcoma (ESTS) patients treated at a single reference institution. PATIENTS AND METHODS Patients with primary localized adult-type ESTS surgically treated at our institution between 1987 and 2007 were retrospectively reviewed. Patients were categorized into four 5-year groups according to the timing of their first operation. Crude cumulative incidence (CCI) of sarcoma-specific mortality (SSM), local recurrence (LR), and distant metastases (DMs) were calculated for each time period. RESULTS A total of 1094 patients were identified. Median follow-up was 81 months. CCI of SSM and LR were significantly better in period 4 in comparison to periods 1-3 (P < 0.001 for both end points), dropping, respectively, from 15% to 6% and from 23% to 9%. An overall improvement of DMs-free survival at 5 years could be detected in the latter period, as well as a better postmetastasis survival. CONCLUSIONS Reference institutions for sarcomas may have improved their outcome in the last years. Although biases of retrospective analyses as well as the effect of institutional learning curves need to be discounted, it is possible that optimal exploitation of a series of subtle improvements in sarcoma treatment may make a difference in results currently achievable.
Sensitivity analysis of physics and planning SmartArc parameters for single and partial arc VMAT planning

We investigate the sensitivity of various physics and planning SmartArc parameters to generate single and partial arc VMAT plans with equivalent or better plan quality as IMRT. Patients previously treated with step‐and‐shoot IMRT for several treatment sites were replanned using SmartArc. These treatment sites included head and neck, prostate, lung, and spine. Effect of various physics and planning SmartArc parameters, such as continuous vs. binned dose rate, dynamic leaf gap, leaf speed, maximum delivery time, number of arcs, and control point spacing, were investigated for Elekta Axesse and Synergy linacs. Absolute dose distribution was measured by using the ArcCHECK 3D cylindrical diode array. For all cases investigated, plan metrics such as conformity indices and dose homogeneity indices increased, while plan QA decreased with increasing leaf speed. Leaf speed had a significant impact on the segment size for low dose per fractionation cases. Constraining leaf motion to a lower speed not only avoids tiny large leaf travel and low‐dose rate value, but also achieves better PTV coverage (defined as the volume receiving prescription dose) with less total MUs. Maximum delivery time, the number of arcs, and the spacing of control points all had similar effects as the leaf motion constraint on dose rate and segment size. The maximum delivery time had a significant effect on the optimization, acting as a hard constraint. Increasing the control point spacing from 2 to 6 degrees increased the PTV coverage, but reduced the absolute dose gamma passing rate. Plans generated using continuous and binned dose rate modes did not show any difference in the quality and the delivery for the Elekta machines. Dosimetric analysis with a 3D cylindrical QA phantom resulted in 93.6%–99.3% of detectors with a gamma index 3%/2 mm <1 for all cases. PACS number: 80
A Novel Low Power G m-C Continuous-Time Analog Filter with Wide Tuning Range

This paper proposes a CMOS Operational Transconductance Amplifier (OTA) for wide tuning range filter applications with reduced power dissipation. The tuning range can be extended by operating the OTA in weak inversion region as well as in strong inversion region. This can be attained by tuning the bias current, which results in the linear variation of transconductance (gm). A fifth order elliptical low pass filter is simulated with cutoff frequency tuning from 1.5 kHz to 1.6 MHz. Wide tuning range filters can be employed for multimode applications, which in-turns help in saving silicon area. A novel tunable resistor is simulated using twin well technology and its value ranges from 40 kΩ to 30 MΩ. The proposed circuit is simulated in SCL 180 nm standard CMOS technology with 1.8 V as supply voltage and achieves a dynamic range (DR) of 47 dB. Total power consumption of the circuit is 670 μW at 1.6 MHz.
Link Prediction with Spatial and Temporal Consistency in Dynamic Networks

A computer-implemented method executed by at least one processor for performing link prediction with spatial and temporal consistency by employing a time-dependent matrix factorization technique is presented. The method includes developing, at a plurality of timestamps, relational data of a sequence of network structures each including a plurality of nodes and learning, by the at least one processor, a feature vector of each node of the plurality of nodes of the sequence of network structures by concurrently optimizing a temporal fitting constraint and a network propagation constraint. The method further includes determining a network structure at each timestamp, determining evolutionary patterns at each timestamp, and predicting links in a future network structure based on an evolution of the sequence of network structures within a user-defined sliding window by reducing time complexities of finding neighbors of each node of the plurality of nodes of the sequence of network structures.
A New Age-Structured Multiscale Model of the Hepatitis C Virus Life-Cycle During Infection and Therapy With Direct-Acting Antiviral Agents

The dynamics of hepatitis C virus (HCV) RNA during translation and replication within infected cells were added to a previous age-structured multiscale mathematical model of HCV infection and treatment. The model allows the study of the dynamics of HCV RNA inside infected cells as well as the release of virus from infected cells and the dynamics of subsequent new cell infections. The model was used to fit in vitro data and estimate parameters characterizing HCV replication. This is the first model to our knowledge to consider both positive and negative strands of HCV RNA with an age-structured multiscale modeling approach. Using this model we also studied the effects of direct-acting antiviral agents (DAAs) in blocking HCV RNA intracellular replication and the release of new virions and fit the model to in vivo data obtained from HCV-infected subjects under therapy.
Event-Triggered Composite Control of a Two Time Scale System

This brief proposes an event-triggered composite control of a two time scale system. A periodic sampling requirement is relaxed and both slow and fast states of the system decide independently when transmitting their current measurements based on a time-dependent triggering rule. The distinct feature of this scheme is that it does not require synchronized measurement updates of its slow and fast dynamics. Further, the theory of singular perturbation is used to decouple the system into slow and fast subsystems, and stability of the system is established. The proposed control strategy guarantees convergence of system states to an adjustable region around origin excluding the Zeno behavior. Simulation results manifest the effectiveness of the proposed approach.
A time delay convolutional neural network for acoustic scene classification

In recent years, demands for more natural interaction between human and machine through speech have been increasing. In order to accomplish this mission, it becomes more significant for machine to understand the human's contextual status. This paper proposes a novel neural network framework that can be applied to commercial smart devices with microphones to recognize acoustic contextual information. Our approach takes into consideration the fact that an acoustic signal has more local connectivity on the time axis than the frequency axis. Experimental results show that the proposed method outperforms two conventional approaches, which are Gaussian Mixture Models (GMMs) and Multi-Layer Perceptron (MLP), by 8.6% and 7.8% respectively in overall accuracy.
IoT based indoor environment data modelling and prediction

In present scenario of the world, controlling air pollution is one of the leading challenges. Most often the educational institutes and organizations in developing countries suffer from polluted environment due to improper planning and poor infrastructure. Students and faculties in a classroom could suffer from health issues due to prolonged exposure to such environment. In this work, we have built low cost environment monitoring devices which detect different pollutant gasses like CO, CO2, NO2, particulate matters (PM10/PM2.5/PM1) with two meteorological parameters relative humidity and temperature. We have observed that the same type of sensors for the same gases give different values although the sensitivity of sensors is acceptable, so we have also tried to perform calibration of the sensors using machine learning technique. We have also detected the class duration for which a classroom environment can be considered healthy for a given number of students using our low cost environment monitoring device. We are also trying to develop a predictive model which predicts the indoor environment with given outdoor meteorological data and structure of the room.
Impact of rapid, culture-independent diagnosis of candidaemia and invasive candidiasis in a community health system

Background Delay in treatment of candidaemia and invasive candidiasis remains a cause of significant morbidity and mortality in high-risk patients. Widespread empirical utilization of antifungal therapy often occurs in an effort to minimize this risk. Objectives This study assessed the impact of the T2Candida Panel in a multi-hospital community health system on time to initiation of antifungal therapy in candidaemic patients as well as the utilization of micafungin. Methods Outcomes were compared between those patients with candidaemia prior to T2Candida implementation and those after implementation. Micafungin utilization for patients with suspected candidaemia/invasive candidiasis was compared with that for patients with a negative T2Candida Panel post-implementation. Results There was a significant decrease in time to appropriate therapy in the post-T2Candida group (34 versus 6 h, P = 0.0147). Empirical antifungal therapy was avoided in 58.4% of T2Candida-negative patients. Conclusions These results support the implementation of T2Candida to improve time to appropriate therapy for candidaemic patients while simultaneously expanding antimicrobial stewardship efforts to appropriately utilize antifungals.
Efficient SIMD implementation of binarized convolutional neural network

This paper presents the efficient implementation of the binarized convolutional neural network (CNN). The conventional CNN model is modified by including the binarization processes so that each of the parameters and the feature elements can be represented by a single bit, and trained maintaining the analysis performance. Multiple parameters and feature elements are packed into a single word and the inner-product operations are implemented efficiently by employing the bit-wise XNOR followed by the bit-count operations. By such SIMD optimization, the proposed implementation achieves a 9.5x speed-up in terms of the inference time for AlexNet, when compared to the straight-forward implementation. In addition, its memory footprint is 9.5% of that required in the straightforward implementation.
Analysis of RapidArc optimization strategies using objective function values and dose‐volume histograms

RapidArc is a novel treatment planning and delivery system that has recently been made available for clinical use. Included within the Eclipse treatment planning system are a number of different optimization strategies that can be employed to improve the quality of the final treatment plan. The purpose of this study is to systematically assess three categories of strategies for four phantoms, and then apply proven strategies to clinical head and neck cases. Four phantoms were created within Eclipse with varying shapes and locations for the planning target volumes and organs at risk. A baseline optimization consisting of a single 359.8° arc with collimator at 45° was applied to all phantoms. Three categories of strategies were assessed and compared to the baseline strategy. They include changing the initialization parameters, increasing the total number of control points, and increasing the total optimization time. Optimization log files were extracted from the treatment planning system along with final dose‐volume histograms for plan assessment. Treatment plans were also generated for four head and neck patients to determine whether the results for phantom plans can be extended to clinical plans. The strategies that resulted in a significant difference from baseline were: changing the maximum leaf speed prior to optimization (p<0.05), increasing the total number of segments by adding an arc (p<0.05), and increasing the total optimization time by either continuing the optimization (p<0.01) or adding time to the optimization by pausing the optimization (p<0.01). The reductions in objective function values correlated with improvements in the dose‐volume histogram (DVH). The addition of arcs and pausing strategies were applied to head and neck cancer cases, which demonstrated similar benefits with respect to the final objective function value and DVH. Analysis of the optimization log files is a useful way to intercompare treatment plans that have the same dose‐volume objectives and importance values. The results for clinical head and neck plans were consistent with phantom plans. PACS number: 87.55. x, 87.55. D, 87.55. de 87.55. dk