Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of Hedgehog Signaling Enhances Delivery of Chemotherapy in a Mouse Model of Pancreatic Cancer

It's All in the Delivery Pancreatic cancer is almost universally associated with a poor prognosis, in part because the tumors are resistant to chemotherapeutic drugs. Working with a mouse tumor model that displays many features of the human disease, Olive et al. (p. 1457, published online 21 May; see the Perspective by Olson and Hanahan) found that the tumors were poorly vascularized, a factor likely to impede drug delivery. Treatment of the mice with the chemotherapeutic drug gemcitabine in combination with a drug that depletes tumor-associated stromal tissue led to an increase in tumor vasculature, enhanced delivery of gemcitabine, and a delay in disease progression. Thus, drugs targeting the tumor stroma may merit investigation as a way to enhance the efficacy of conventional chemotherapy for pancreatic cancer. Pancreatic tumors are unresponsive to chemotherapy because their limited vasculature precludes efficient drug delivery. Pancreatic ductal adenocarcinoma (PDA) is among the most lethal human cancers in part because it is insensitive to many chemotherapeutic drugs. Studying a mouse model of PDA that is refractory to the clinically used drug gemcitabine, we found that the tumors in this model were poorly perfused and poorly vascularized, properties that are shared with human PDA. We tested whether the delivery and efficacy of gemcitabine in the mice could be improved by coadministration of IPI-926, a drug that depletes tumor-associated stromal tissue by inhibition of the Hedgehog cellular signaling pathway. The combination therapy produced a transient increase in intratumoral vascular density and intratumoral concentration of gemcitabine, leading to transient stabilization of disease. Thus, inefficient drug delivery may be an important contributor to chemoresistance in pancreatic cancer.
CPT-Based Data Hiding in Selected Subband Using Combined Transform and Decomposition Method

A digital copyright on an audio needs a special protection to prevent it from illegal distribution. Hiding a copyright into an audio is a solution for securing its ownership. In this paper, we propose an audio watermarking using combination method of Lifting Wavelet Transform (LWT), Discrete Sine Transform (DST), Singular Value Decomposition (SVD), and Cartesian Polar Transform (CPT) using Quantization Index Modulation (QIM). A host audio is first decomposed by LWT. Next, a subband is selected then transformed by DST. Then, DST selected coefficients are decomposed into U, S and V matrices by SVD. S matrix is then selected. S elements in position (1,1) and (2,2) are converted by CPT to magnitude and phase coefficients. The phase coefficient is finally used for embedding a bit of watermark by QIM. The simulation result shows that the proposed method offers very high watermark capacity with good imperceptibility and robustness.
Traj-Cloud: A Trajectory Cloud for enabling Efficient Mobility Services

The advances of location-acquisition technologies and rapid development of GPS equipped devices have generated large spatio-temporal mobility traces fostering the opportunity of discovering inherent knowledge about urban dynamics. This mobility analytics of the movement behaviours of people and vehicles is the key component of building any intelligent transportation system (ITS) application. However, extracting usable knowledge from huge volume of mobility data is both time-consuming and computationally intensive. In this work, we propose a cloud-based spatial trajectory trace management framework, namely, Traj-Cloud to effectively store, retrieve and efficiently process the mobility traces for real-time applications. Traj-Cloud is a SaaS cloud which provides three major mobility services, namely, (i) trajectory-indexing to efficiently handle huge real-time trajectory updates; (ii) geo-tagging, map-matching services in a distributed computing platform (i.e., Google Compute Engine) and (iii) trajectory-processing to resolve mobility based spatio-temporal queries to improve efficiency and reduce response time. Traj-Cloud has been implemented in Google Cloud Platform (GCP) and empirical results on real-life dataset of NYC taxi traces and mobility trajectories of an academic campus yields the effectiveness of the proposed framework.
Reliability and Performance Analysis of a Data Center's Network Architecture

New technologies development has led to a rapid increase of computing and communication capabilities provided by Data Centers (DC). Thus, maximizing the performance and robustness to failures becomes a competitive factor among companies. In this paper, we investigate a combined study of reliability and performance of a DC network using Production Trees, a new modeling methodology for dealing with availability issues of production systems. Our results illustrate the applicability of the proposed approach and include both reliability and performance measures.
Position Estimation Using Stereo Camera Images and Physics Engine Simulation for Robot Control

For a robot to be autonomous it must be able to assess its estimate and situation and the decide what action to take. The ROBO-KEN competition offers a chance for robot developers to test the autonomy of their robot by having them fight Kendo matches against a rival robot. In this paper, we report of on an image processing algorithm for a ROBO-KEN robot. That has two USB cameras which give Stereo-Vision. The algorithm estimates the distance to an object, identifies the object and calculates its center of gravity. The coordinates obtained from the algorithm are then our to decide the next action for the robot. 3D model created on the simulator which based on the actual robot. This moves in the same way as the 3D model. We will try to control the robot based on the coordinates obtained by Stereo-Vision.
Variable-Node-Based Belief-Propagation Decoding With Message Pre-Processing for NAND Flash Memory

With the fast development of non-volatile storage technology, NAND flash memory faces more and more challenges such as data reliability and lifetime. To overcome the issue of the reliability, low-density parity-check (LDPC) codes are considered as a main candidate of error-correction-codes (ECCs) for NAND flash storages. However, conventional soft decoding algorithms for LDPC codes suffer from the drawback of slow convergence speed, which increases the decoding latency. For achieving fast convergence speed, a variable-node-based belief-propagation with message pre-processing (VNBP-MP) decoding algorithm for binary LDPC codes and a non-binary VNBP-MP (NVNBP-MP) decoding algorithm for non-binary LDPC codes are proposed. Both of the algorithms utilize the characteristics of the NAND flash channel to perform the message pre-processing operations. In theory, the propagation of unreliable messages can be effectively prevented and the propagation of reliable messages can be speeded up. To further improve the decoding convergence, the treatment for oscillating variable nodes is considered after the message pre-processing operations. The simulation results also show that the proposed algorithms both have a noticeable improvement in convergence speed and latency, without compromising error-correction performance, compared with the existing soft decoding algorithms.
Algebro-topological invariants in network theory

The dynamical evolution of a network is strongly associated with its pattern of internal connections [2, 5]. The lack of periodic patterns in the vast majority of biological networks, and in recurrent neural networks in particular, makes it difficult to determine this correlation from a theoretical and formal approach, other than relying on descriptive statistical parameters when trying to describe the network dynamics [3]. Algebraic topology provides us with invariants that are a powerful tool to understand the structure of abstract spaces and can also defined for graphs. We propose to apply those invariants to network science. Even simple invariants such as the Euler characteristic or the Betti numbers can provide us with valuable insight about the structure of a network and its connectivity, complementing measures such as centrality of nodes. We introduce certain topological invariants and use them to apply methods from algebraic topology in an attempt to characterize and classify networks and reveal their internal structure, looking in particular at recurrent neuronal networks, where we can use such invariants to detect the topological structure of the network and its activation patterns.
Process-Induced Power-Performance Variability in Sub-5-nm III–V Tunnel FETs

We examine the power-performance variability of a projected sub-5-nm GaAsSb/InGaAs vertical tunnel FET considering various process control tolerances in the state-of-the-art device integration and propose countermeasures in device design. Nominal and three-sigma-corner device characteristics generated in quantum-mechanical/TCAD simulations are used to calibrate a semiempirical compact model, based on which the nominal and variability-inclusive energy-delay landscapes are extracted from ring-oscillator circuit simulations at sub-500-mV supply voltages. Variations in four parameters are identified as of major impact on the worst-case speed loss and iso-speed energy penalty: dopant pocket thickness, gate work function, hetero-band offset, and body thickness (in descending order). Variability-resilient device options are explored against pocket thickness variation, including: 1) pocket desensitization with increased thickness and reduced doping concentration and 2) broken-gap tunnel FET with a negative effective band gap. Reengineered devices achieve <inline-formula> <tex-math notation="LaTeX">$< 18\times $ </tex-math></inline-formula> speed loss and <inline-formula> <tex-math notation="LaTeX">$< 3\times $ </tex-math></inline-formula> energy penalty for (0.1–1) ns gate delay with respect to the nominal corner.
Computation of Three-Finger Grasping Plane

Empowering a robot with ability to grasp has been a long dream in robotics. Despite serious research effort over many decades, even the most advanced robots still struggle in the area of grasping. The main challenge is how to deal with errors and uncertainty. In grasp planning, traditional methods such as ICR and caging deal with the uncertainty problem at the contact level and suffer the poor performances. This paper explores a more global outlook by aiming at finding a promising grasping plane (instead of individual points) to place the three contact points. The paper presents a heuristic method to find a plane through the object to place the three finger contact. The input of the method is the input mesh model of the object. Physical experiment is also presented to show the effectiveness of the method.
Automatic data placement for CPU-FPGA heterogeneous multiprocessor System-on-Chips

Efficient utilization of restrained memory resources is of paramount importance in CPU-FPGA heterogeneous multiprocessor system-on-chip (HMPSoC) based system design for memory-intensive applications. State-of-the-art high level synthesis (HLS) tools rely on the system programmers to manually determine the data placement within the complex memory hierarchy. In this paper, we propose an automatic data placement framework which can be seamlessly integrated with the commercial Vivado HLS. We first show counter-intuitive results that traditional frequency and locality based data placement strategy designed for CPU architecture leads to non-optimal system performance in CPU-FPGA HMPSoCs. Built on top of our memory latency analysis model, the proposed integer linear programming (ILP) based framework determines whether each array object should be access via the on-chip BRAM, shared CPU L2-cache, or DDR memory directly. Experimental results on the Zedboard platform show an average 1.39X performance speedup compared with a greedy-based allocation strategy.
Prediction-Based Task Migration on S-NUCA Many-Cores

Performance of a task running on a many-core with distributed shared Last-Level Cache (LLC) strongly depends on two factors: the power budget needed to guarantee thermally safe operation and the LLC latency. The task’s thread-to-core mapping determines both the factors. Arrival and departure of tasks on a many-core deployed in an open system can change its state significantly in terms of available cores and power budget. Task migrations can thereupon be used as a tool to keep the many-core operating at the peak performance. Furthermore, the relative impacts of power budget and LLC latency on a task’s performance can change with its different execution phases mandating its migration on-the-fly.We propose the first run-time algorithm PCMig that increases the performance of a many-core with distributed shared LLC by migrating tasks based on their phases and the many-core’s state. PCMig is based on a performance-prediction model that predicts the performance impact of migrations. PCMig results in up to 16 % reduction in the average response time compared to the state-of-the-art.
On-the-fly and DAG-aware: Rewriting Boolean Networks with Exact Synthesis

The paper presents a generalization of DAG-aware AIG rewriting for k-feasible Boolean networks, whose nodes are k-input lookup tables (k-LUTs). We introduce a high-effort DAG-aware rewriting algorithm, called cut rewriting, which uses exact synthesis to compute replacements on the fly, with support for Boolean don’t cares. Cut rewriting pre-computes a large number of possible replacement candidates, but instead of eagerly rewriting the Boolean network, stores the replacements in a conflict graph. Heuristic optimization is used to derive a best, maximal subset of replacements that can be simultaneously applied to the Boolean network from the conflict graph. We optimize LUT mapped Boolean networks obtained from the ISCAS and EPFL combinational benchmark suites. For 3-LUT networks, experiments show that we achieve an average size improvement of 5.58% and up to 40.19% after state-of-the-art Boolean rewriting techniques were applied until saturation. Similarly, for 4-LUT networks, we obtain an average improvement of 4.04% and up to 12.60%.
Towards a Collaborative Repository for the Documentation of Service-Based Antipatterns and Bad Smells

While the concepts of object-oriented antipatterns and code smells are prevalent in scientific literature and have been popularized by tools like SonarQube, the research field for service-based antipatterns and bad smells is not as cohesive and organized. The description of these antipatterns is distributed across several publications with no holistic schema or taxonomy. Furthermore, there is currently little synergy between documented antipatterns for the architectural styles SOA and Microservices, even though several antipatterns may hold value for both. We therefore conducted a Systematic Literature Review (SLR) that identified 14 primary studies. 36 service-based antipatterns were extracted from these studies and documented with a holistic data model. We also categorized the antipatterns with a taxonomy and implemented relationships between them. Lastly, we developed a web application for convenient browsing and implemented a GitHub-based repository and workflow for the collaborative evolution of the collection. Researchers and practitioners can use the repository as a reference, for training and education, or for quality assurance.
A Design for a Programmable Swept Frequency Module for Eddy Current Testing

This paper presents and tests a new design concept for a swept frequency module for improved eddy current testing. The module consists of a programmable swept frequency device and an excitation coil probe. The graphical user interface software is developed in MATLAB and can display the sensor data using thousands of graphical functions. Testing shows that the module performs well at both sweeping frequencies and recording the impedance data, and unlike other available systems, this design combines these two functions into a single module that is more convenient and more portable. Therefore this new module provides improved convenience and efficiency as an eddy current testing solution.
Propose a Method of Requirements Review Considering Adverse Conditions

In a product development project, it may lead to extra costs if defects caused by the requirement definition process are discovered during the post-process. Therefore, it is important to find defects in requirements by considering details not described. However, there is no inspection method established for adequately grasping the boundary conditions of the review target and considering the range that is not described in the requirements specifications, and it depends largely on the experience and skills of reviewers. To solve this problem, we propose an analytical framework to organize the boundary conditions of the review target and propose a review method that considers adverse conditions which are beyond the boundary range. We confirmed the applicability of the method with case studies and organized the analytical framework to derive adverse conditions based on the results.
Mermaid&#8212;A front-end to distributed heterogeneous databases

Mermaid is a system that allows the user of multiple databases stored under various relational DBMSs running on different machines to manipulate the data using a common language, either ARIEL or SQL. It makes the complexity of this distributed, heterogeneous data processing transparent to the user. In this paper, we describe the architecture, system control, user interface, language and schema translation, query optimization, and network operation of the Mermaid system. Future research issues are also addressed.
Assertion Detection in Clinical Natural Language Processing: A Knowledge-Poor Machine Learning Approach

Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on. Incorrect assertion assignment could cause inaccurate diagnosis of patients’ condition or negatively influence following study like disease modeling. Thus, clinical NLP systems which can detect assertion status of given target medical findings (e.g. disease, symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding, RNN and attention mechanism (more specifically: Attention-based Bidirectional Long Short-Term Memory networks) for assertion detection in clinical notes. Unlike previous state-of-art methods which require knowledge input or feature engineering, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems.
Microstructural Investigation Of GYP/Al Surface Composites Fabricated By friction Stir Processing

Strengthening the mechanical properties of AA 1100 is important especially for its possible application in bone tissue engineering (BTE). Gypsum is a non-ferrous material already used in the fabrication of scaffolds needed in BTE. A composite emanating from these two materials may provide a mechanically and biocompatible viable option to gypsum (GYP) alone in the fabrication of BTE components. In this study, surface composites with gypsum powder particles as reinforcement in AA 1100 matrix were developed by means of friction stir processing (FS P). Surface analysis of the composite showed well-distributed gypsum particles in the matrix. The average surface roughness measured (Ra = 1.97 μm) is comparable to those found in literature for similar application. The hardness acquired via a Vickers hardness machine showed an improvement (Up to 36.62HV) when compared to as received AA 1100 without reinforcement (23HV). The determined coefficient of friction (COF) for 1 and 10 N were, 1.048 and 0.838 respectively which are sufficiently high and seen as an indication of the roughness of the surface composite. It is found that gypsum powder particles bound well in the AA 1100 matrix.
Decoupled Control Strategy for Electric Springs: Dual Functionality Feature

Smart grid philosophy recommends a wide spread of residential roof-top installed grid-connected photovoltaic (PV) systems as a benchmark for renewable energy (RE) emphasis to future power generation. These systems must adopt power electronic-based converters to perform grid integration function. On the other side, concerns about voltage fluctuations, which are directly related to power system stability, have arisen from the increasing use of intermittent renewable energy sources (RES) distributed in the grid. Hence, a power electronic-based converter topology, known as Electric Spring (ES), has been developed to be additively connected in series with non-critical residential loads. It has the ability to regulate the mains voltage via reactive power compensation. In this paper, a novel control scheme is proposed for ESs operation. The presented control technique offers conventional ES a decoupled dual functionality. Besides, its inherent ability of feeder voltage regulation, the ES can, under the proposed control technique, simultaneously inject locally available PV power into the grid via the same power electronic converter. The system analysis and mathematical modeling of the proposed control scheme are demonstrated in detail. To verify the proposed technique effectiveness, both simulation and experimental investigation are carried under various bus voltage levels’ cases: normal, sag, and swell. Moreover, the system performance is attested under irradiance changes. The results from simulations and experimental setup prove the applicability of the ES dual functionality even under severe disturbances.
Threshold-less Zero-Velocity Detection Algorithm for Pedestrian Dead Reckoning

GPS (Global Positioning System)/GNSS (Global Navigation Satellite System) and wireless positioning systems, which provide reliable location information only in a fully constructed infrastructure environment, are not suitable navigation methods for multimodal pedestrians or those who are moving through gait where the infrastructure is not built. To solve this problem, research on PDR (Pedestrian Dead Reckoning) using inertial sensors has been going on for the last two decades. Recently, a method of calculating the position through the INS algorithm using the inertial sensors mounted on the shoe and correcting the error through ZUPT (Zero-velocity UPdaTe) algorithm by detecting the zero-velocity at the moment when the foot is stepped on the ground is widely used. Most of the zero-velocity detection algorithms use threshold values after manipulating the sensor outputs. However, these threshold values should be set differently depending on the pedestrian, the type of walking, etc. In this paper, we propose a zero-velocity detection algorithm that does not require any threshold value. After processing the gyro and accelerometer outputs based on an appropriate algorithm, it detects the moment when the foot touches the ground without any threshold value. The performance of the proposed algorithm is verified experimentally.