Miracle-dz/UniFAR-FTUs huggingface dataset:
The prognostic value of CXC chemokine receptor 4 in non-small cell lung cancer : a meta-analysis

This study aims to better define the roles of CXCR4 in mediating and/or modulating and prognosis of CXCR4 in NSCLC. 10 publications were included by searching PubMed, Scopus, EMBASE, and CENTRAL databases. Primary outcomes were overall survival (OS) and disease-free survival (DFS). Effect sizes were summarized using Hazard ratios (HRs) and their 95% confidence intervals (CIs). The results revealed CXCR4 expression did not significantly correlate with prognosis according to OS and DFS in NSCLC patients. However, high CXCR4 expression was significantly associated with poor prognosis of OS in ≤ 70% stage I NSCLC (HR, 1.79; 95% CI, 1.36-2.37; P=0.000), while that was reverse in > 70% stage I (HR, 0.49; 95% CI, 0.32-0.75; P=0.001). Additionally, high CXCR4 expression was significantly correlated with poor OS in Asian subgroup (HR, 2.11; 95% CI, 1.57-2.85; P=0.000), and was significantly associated with poor OS in less male group (≤ 70% male; HR, 1.88; 95% CI, 1.28-2.77; P=0.001), but no significant association between high CXCR4 expression and OS was found in follow-up period subgroups. High CXCR4 expression might potentially allow for prediction of OS in ≤ 70% stage I NSCLC, since it was associated with increased survival for > 70% stage I NSCLC. Besides, Asian was a much worse factor for prognosis of advanced NSCLC patients, whose CXCR4 was overexpressed, and gender might be an important factor for the correlation between high CXCR4 expression and prognosis of NSCLC patients. However, larger scale trials with strict design, varied subpopulations, and long-term outcomes are needed.
A MultiView Software Infrastructure for Hybrid Immersive Environments

In the domain of large-scale visualization instruments, Hybrid Reality Environments (HREs) are a recent innovation that combines the best-in-class capabilities of immersive environments, with the best-in-class capabilities of ultra-high-resolution display walls. HREs create a seamless 2D/3D environment that supports both informationrich analysis as well as virtual-reality simulation exploration at a resolution matching human visual acuity. Co-located research groups in HREs tend to work on a variety of tasks during a research session (sometimes in parallel), and these tasks require 2D data views, 3D views, linking between them and the ability to bring in (or hide) data quickly as needed. Addressing these needs requires a matching software infrastructure that fully leverages the technological affordances offered by these new instruments.
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning

To validate the generality of the model, we trained VGG19 (Simonyan & Zisserman, 2014) on the three synthetic data sets with the same configuration as in Section 4. Figures 11 and 12 show the test errors of the four training methods using VGG-19 with varying pair and symmetry noise rates. Again, SELFIE achieved the lowest test error at any noise rate of both noise types on all data sets. SELFIE outperformed the other methods by a larger margin at a higher noise rate. Especially when the noise rate was 40%, SELFIE reduced the absolute test error by 1.8pp– 5.4pp compared with Default, 1.0pp–2.5pp compared with ActiveBias, and 4.2pp–13.2pp compared with Coteaching for the pair noise; by 3.8pp–6.3pp compared with Default, 4.6pp–6.7pp compared with ActiveBias, and 6.3pp–9.6pp compared with Coteaching for the symmetry noise. On the other hand, ActiveBias achieved the test error slightly lower than that of Default, and Coteaching worked well only on CIFAR-10 with symmetry noise, i.e., Figure 12(a). Tables 5 and 6 summarize the test errors of all methods at the noise rate of 40% for the pair and symmetry noises, respectively. B. “ANIMAL-10N” for Realistic Noise
A Passive Fingerprint Technique to Detect Fake Access Points

The aim of this paper is to detect Rogue Access Points (RAPs) that clone legitimate Access Points (APs) characteristics. A novel passive approach that takes advantage of the characteristics of physical layer fields via the Radiotap length is proposed. This approach is a general fingerprint, thus, it can be used for different purposes such as identification, network monitoring and intrusion detection systems. We utilize the fingerprint to detect RAPs to evaluate its effectiveness. The technique is implemented on a commercially available wireless card to assess its accuracy. The proposed detection algorithm accomplishes 100 percent accuracy to determine the RAPs in a lightly loaded traffic environment. The detection can be recognized in less than 100 ms and is scanned in a real-time setting. The robustness and the effectiveness of the detection algorithm are examined in three locations. Keywords—WLAN, Rogue Access Point, Physical Layer, Beacon Frames
Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects

Humans have an intuitive understanding of physics. They can predict how a physical scene will unfold, and reason about how it came to be. Adults may rely on such a physical representation for visual reasoning and recognition, going beyond visual features and capturing objects in terms of their physical properties. Recently, the use of draped objects in recognition was used to examine adult object representations in the absence of many common visual features. In this paper we examine young children’s reasoning about draped objects in order to examine the develop of physical object representation. In addition, we argue that a better understanding of the development of the concept of cloth as a physical entity is worthwhile in and of itself, as it may form a basic ontological category in intuitive physical reasoning akin to liquids and solids. We use two experiments to investigate young children’s (ages 3–5) reasoning about cloth-covered objects, and find that they perform significantly above chance (though far from perfectly) indicating a representation of physical objects that can interact dynamically with the world. Children’s success and failure pattern is similar across the two experiments, and we compare it to adult behavior. We find a small effect, which suggests the specific features that make reasoning about certain objects more difficult may carry into adulthood.
9 Artificial Development

There is growing interest in the use of analogies of biological development for problem solving in computer science. Nature is extremely intricate when compared to human designs, and incorporates features such as the ability to scale, adapt and self-repair that could be usefully incorporated into human-designed artifacts. In this chapter, we discuss how the metaphor of biological development can be used in artificial systems and highlight some of the challenges of this emerging field.
Device Processor Top Half Bottom Half : Softirqs / Tasklets Bottom Half : Workqueues Application 1 2 3 4 T 1 T 2 Sections analyzed by INT SPECT

Interrupt handling with predictably low latency is a must for systems to respond to external events. System designers of tiny embedded computers to large-scale distributed systems face the challenge of ever-increasing hardware and software complexity. In the absence of precise timing models, measurementbased approaches are required to achieve predictably low latency. In this paper, we present INTSPECT, a tool that systematically evaluates the interrupt latency, at run-time, in the Linux operating system kernel. We apply INTSPECT on two distinct platforms (i.e., ARM and Intel) to measure interrupt latencies, identify jitter causes, and reveal interdependencies between interrupt handlers and user-space workloads. Our tool thus provides valuable insight on interrupt timings and interferences.
RICT at the NTCIR-14 QALab- PoliInfo Task

Our RICT team tackled segmentation and classification subtasks in NTCIR-14 QA Lab-PoliInfo. As our technical characteristic in segmentation task, we regard segments as retrieval objects, and utilize cue-phase-based methods including a nobel semi-supervised learning method to detect segment boundary. Here we have presented 5 methods for formal run. As to classification task, we train our supervised learning model by all utterances without topic effects and utilize outlier detection technologies to alleviate the imbalance training data problem. Here we have submitted 7 runs for formal run. Since evaluation results show that our approach achieves higher scores than average, the main contribution for classification we made is providing a feasible system to deal with a small number of imbalance training data problem especially in the regional assembly minutes. Meanwhile, we also made contribution for segmentation to grasp distinguishing features of regional assembly minutes by our efficient method.
NEWBERRY CRATER LAKES, OREGON: PAULINA LAKE

For two lakes separated only by a narrow ridge of obsidian, Paulina and East Lake could not be more different. The chemical and physical differences that distinguish these two bodies also inform a steady state model for Paulina Lake that requires extensive injection from a currently unidentified source, which we propose is a hydrothermal vent. Major and trace element concentrations, in tandem with water properties like conductivity and alkalinity, prove an area of high temperatures in deep water may be the location of a lake-bottom vent. A pluton emplaced around 4,200 years ago beneath the surface of Newberry’s caldera may divide these two lakes (Jensen & Chitwood, 2000).
Implementing user and application specific algorithms within IP-methodology: a coarse-grain-approach

The method for designing application specific algorithms on IP-methodology is presented. Two well known VLSI design methods, IP-based design methodology and coarse grain computing, are combined making it possible to design a complete chip (i.e. system-on-a-chip) from IP blocks and to change the functionality of the chip after manufacturing even dynamically. In addition the actual implementation of the presented method is shown and a modern computation intensive algorithm is mapped on it. In the results it is shown that modern silicon technologies already make it realistic to use the presented programmable and configurable IP block on SoC.
LOCALISATION OF A MOVING TARGET WITH A MIMO RADAR SYSTEM

The aim of this thesis is to estimate parameters of the moving target using different radar configurations. First the Doppler effect and the time delay of the received signal is examined. The investigation of these phenomenons is made for radial and non-radial movement of simple monostatic radar configuration and after that for the bistatic and multistatic (MIMO) configuration. The ambiguity function is presented for all these configurations. The ambiguity function is then used to estimate target parameters from the received signal. The received signal model and ambiguity function for the monostatic radar configuration is developed using Matlab software. Finally the practical use of these implementations is presented and compared.
A Perceptually-Motivated Harmonic Compatibility Method for Music Mixing

We present a method for assisting users in the process of music mashup creation. Our main contribution is a harmonic compatibility metric between musical audio samples which combines existing perceptual relatedness (i.e., chroma vectors or key a nity) and consonance approaches. Our harmonic compatibility metric is derived from Tonal Interval Space indicators, which we adapt to robustly describe the harmonic content of musical audio. Additional attributes of key, rhythmic (density), and spectral content are computed from musical audio to enhance the compatibility representation of a sample collection in an interactive visualization. An evaluation of our harmonic compatibility method shows that it complies with the principles embodied in Western tonal harmony to a greater extent than previous approaches.
Hardware Systems for Neural Acceleration

For years the development of new neural networks and better training algorithms has been largely orthogonal to what kind of hardware might drive their usage. While neural networks have been around since the 1980s they were considered far too computationally intense and impractical compared to canonical computer vision techniques. In 2012 a neural net dubbed AlexNet handily broke the world record for image recognition tasks using a deep convolution neural network trained on commodity GPUs. This showed for the first time that deep convolutional neural networks can be trained on a low cost system in a reasonable time, one week, while producing state-of-the-art accuracy. Since then the exponential growth of even deeper neural network research has been enabled through high performance CPU/GPU devices and an awareness of the underlying hardware. This has opened a door for application specific integrated circuits, neural datacenters, and highly optimized compute kernels for putting them all together. This has ushered in a new era of hardware research that requires an appreciation of the complex neural learning problem coupled with hardware design experience to improve throughput, power, and in some cases accuracy in neural algorithms. This survey presents an overview of the hardware and software systems designed for neural networks with a focus in convolutional neural networks. A new taxonomy for research in neural acceleration is also presented that represent the three angels most commonly taken to optimize this algorithm technology enabled acceleration (TEA), system enabled acceleration (SEA) and algorithm enabled acceleration (AEA).
Prognostic roles of high TIMP-1 expression in patients with solid tumors : a meta-analysis

Background: In recent years, many studies have indicated that tissue inhibitor of metalloproteinases-1 (TIMP-1) protein has a close relationship with prognosis in cancer patients. However, other studies have not drawn the same conclusion. Methods: The current systematic review and meta-analysis was conducted to assess the prognostic effects of TIMP1 in cancer patients. Eleven studies were involved in the analysis, including four that evaluated breast cancer, two that evaluated colon cancer, and one each that evaluated epithelial ovarian cancer, lung cancer, gastric cancer, hepatocellular cancer, and laryngeal squamous cell cancer. Results: Results indicate that increased TIMP1 protein levels yield worse overall survival (OS) (hazard ratio (HR) = 1.58, 95% confidence intervals (CI) 1.25-2.01). Subgroup analysis by tumor type showed that high expression of TIMP-1 was associated with poor OS in colon cancer (HR = 2.40, 95% CI 1.35-4.25), rather than in breast cancer (HR = 1.21, 95% CI 0.83-1.74). Subgroup analysis by ethnicity showed that high expression of TIMP-1 was associated with poor OS in Asians (HR = 2.24, 95% CI 1.64-3.06) and Caucasians (HR = 1.32, 95% CI 1.02-1.75). Conclusion: In summary, TIMP-1 is a poor prognostic maker for solid tumors.
Ecosystem and Nutrient Dynamics in the Seto Inland Sea , Japan

We studied the ecosystem and nutrient dynamics in the Seto Inland Sea from both field observations and numerical modeling. We investigated spatiotemporal variations in the group composition of phytoplankton and in the nutrient concentrations in Iyo-Nada, Hoyo Strait and Bungo Channel (parts of the Seto Inland Sea) with monthly field observations in 2009. From spring to early summer, nanoand pico-phytoplankton dominated in all three regions. From late summer to autumn, micro-phytoplankton (diatoms) bloomed in IyoNada and Hoyo Strait, while a bloom was not observed in Bungo Channel. This autumn diatom bloom was probably caused by nutrient supply associated with a breakdown of the bottom cold water dome. For understanding the mechanism of the nutrient cycle and plankton dynamics, we developed a plankton functional types model eNEMURO (4 Nutrient, 4 Phytoplankton, 4 Zooplankton, 3 Detritus compartments), which was an extended version of NEMURO (a standard lower-trophic-level marine ecosystem model of PICES) by introducing the microbial food web and phosphorous cycle and dividing diatoms into two compartments according to temperature dependency. The model successfully reproduced the nutrient and phytoplankton dynamics observed in both IyoNada and Bungo Channel.
VR Enhanced Interactive Intubation Simulation

This paper introduces a new haptic rendering pipeline for neonatal endotracheal intubation simulation. The challenging procedure involves multiple models of different material properties, it is performed in the narrow oral cavity involving multiple contacts with tongue, lips, and laryngoscope. The simulation first builds tetrahedron FEM for each model in the environment from medical images. Second, collision detection is performed in a simple adaptive inner sphere tree structure for deformable tissues. Then a collision response is handled with a novel force aware projective position correction. Finally, HMD VR display is incorporated for immersive user experience. The proposed method is proved to be effective for heterogeneous simulating environment with complicated haptic virtual geometry, hence can be applied to other surgical simulations of difficult settings, and can be easily adapted to less critical simulations.
Comparative Study of Load Balancing Algorithms in Cloud Computing Environment

Background: Cloud computing is an emerging technology in a business. It is used to access the application or services and infrastructures at anywhere any time. Load balance means that to share the work across the multiple computing resources to serve higher for the user and utilize the resource with efficiency for reach the good performance of the application. These ideas are enforced with software system, hardware or each. Statistical Analysis: The various load balancing algorithms are compared with quality of service parameters in a cloud network. This analysis helps to identify the effective load balancing algorithm for optimizes resource use, maximizes throughput, minimizes response time, and avoids overload. Findings: The load balancer is indorsed in all situations for to supply service continuity and handling additional traffic. Therefore the effective load balancing algorithms needed to form economical resource utilization by provisioning of resources to cloud user's on-demand basis. Application: This paper discusses numerous load balancing algorithms so as to improve resource utilization and quality of services in cloud computing environment.
Culture and the Incidental Self- and Mother-Reference Effect in Memory

Co-presenting an item with self-relevant vs. other-relevant information at encoding can produce a self-memory advantage in the absence of any task demand to process the item’s self-relevancy. The present study examined (a) whether this “incidental” self-memory advantage would extend beyond highly self-relevant information such as one’s own name to include information relevant to one’s family member, specifically one’s mother, and (b) if and to what extend individualistic vs. collectivistic cultures differentially affect the magnitude of the potential mother-memory advantage. During encoding, American and Moroccan participants were presented their own name, their mother’s name, or another person’s name simultaneously with the to-be-processed target words to which they made location judgments in relation to the name in the middle. In a subsequent memory test, we found that both Moroccan and American participants showed a significant memory advantage for target words and their associated source feature (i.e., the name each word was presented with at encoding) that were presented with their own name or their mother’s name compared with those presented with another person’s name. There was no significant effect of culture on the magnitude of a mother-memory advantage. Our findings provide evidence for the presence of an incidental mother-reference effect and further suggest that future studies are necessary to fully determine the effect of culture on the emergence of this incidental mother-memory advantage.
Market Size Growth Survival in Multi-Generation Technology Environment: A Predictive Review of the Indian Air-Conditioner and Refrigerator Industry

The ensuing paper aims to explore the future growth pattern of the market size of air-conditioner and refrigerator industry in India. Though this industry has witnessed phenomenal growth in the past, with multi-generation technology products driving it, its growth has remained erratic in nature. This paper also ratifies if the industry would survive the existing market size growth trend. In this predictive assessment, univariate time series data of net sales, collected from CMIE, is used. The data, spreading across 14 years, have 56 observations and exhibit both trend and seasonality. Forecast of market size is made using the best model derived from comparative approaches that include SARIMA, triple exponential smoothing and neural network. SARIMA model is found to best fit the historical data for predictive purpose and the study outcome suggests market size to grow till 2020. Finally, Weibull’s function is used to analyze reliability of the forecast results which indicates diminishing trend of the market size growth. Finally, it is concluded that the current erratic nature of market size growth would disappear.
Hardware-Accelerated Neural Spike Sorting

In a traditional neural recording system, raw data is sent outside the body through wires to the rest of the data-acquisition hardware. Spike sorting, the process of classifying each recorded action potential according to its putative neuron, is performed offline, in software. There are several problems with this approach. First, offline spike sorting is prohibitively time-consuming to the researcher, especially for many-channel recordings. Second, more and more applications are arising that require wireless recording systems, and wireless transmission of the broadband data for many channels cannot be achieved under the strict power limitations to which implantable electronics are subject. Hardware spike sorting provides a solution to each of these problems by accelerating offline processing and by providing the data reduction necessary for wireless transmission in real-time applications. In order to implement hardware spike sorting, it is crucial to determine the algorithms with the best accuracy–complexity tradeoff. In preliminary work, we have evaluated several published spike-sorting algorithms to determine which are most appropriate for use in a real-time, implantable neural-recording system. Whereas many independent groups have evaluated individual algorithms using different, often biological, datasets, we used a neural-signal simulator to develop synthetic datasets in order to obtain an accurate, unbiased comparison between algorithms. Additionally, we evaluated algorithms over a wider range of SNRs, from very high (∼20 dB) to very low (∼ −10 dB). This work allowed us to choose the optimal algorithms to implement in two digital spike-sorting chips, which will eventually be used in a fully integrated wireless-recording system. We next plan to build further on this work by building an FPGA-based spikesorting platform that can speed up processing by a factor of 100,000. This comprehensive tool will provide a compromise between speed and flexibility. Features of this FPGA-based processing platform will include configurable digital filters and a library of algorithms for spike detection, alignment, feature extraction, dimensionality reduction, and clustering. The processing platform will also include a graphical user interface (GUI) to provide maximum ease of use for scientists. We plan to host the tool online so that it may be utilized by researchers around the world.