Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of miR-1258 contributes to carcinogenesis and progression of liver cancer through targeting CDC28 protein kinase regulatory subunit 1B

Hepatocellular carcinoma (HCC) is the leading cause of cancer related death worldwide. The number of deaths is proportional to the global incidence, which highlights the aggressive tumor biology and lack of effective therapies. Dysregulation of microRNAs has been implicated in carcinogenesis and progression of liver cancer. Here, we identified that miR-1258 was significantly downregulated in HCC and associated with poor patients' survival. Overexpression of miR-1258 significantly inhibits liver cancer cell growth, proliferation and tumorigenicity through increasing cell cycle arrest in G0/G1 phase and promotes cell apoptosis. Interestingly, stable overexpression of miR-1258 suppresses cell migration, stemness and increases sensitivity of HCC cells to chemotherapy drug like doxorubicin. The CDC28 protein kinase regulatory subunit 1B (CKS1B) was identified as a functional downstream target of miR-1258. Re-expression of CKS1B overcomes miR-1258 induced apoptosis and increases stemness of HCC cells, suggesting that loss of miR-1258 contributes to carcinogenesis and progression of liver cancer through targeting CKS1B. Therefore, loss of miR-1258 may be a potential diagnostic and prognostic biomarker and blocking miR-1258-CKS1B axis is a potential therapeutic strategy in HCC.
The complete (3‐D) surface displacement field in the epicentral area of the 1999 MW7.1 Hector Mine Earthquake, California, from space geodetic observations

We use Interferometric Synthetic Aperture Radar (InSAR) data to derive continuous maps for three orthogonal components of the co‐seismic surface displacement field due to the 1999 Mw7.1 Hector Mine earthquake in southern California. Vertical and horizontal displacements are both predominantly antisymmetric with respect to the fault plane, consistent with predictions of linear elastic models of deformation for a strike‐slip fault. Some deviations from symmetry apparent in the surface displacement data may result from complexity in the fault geometry.
Shape-from-Shading under Near Point Lighting and Partial views for Orthopedic Endoscopy

Bone reconstruction using endoscopy is important for computer aided minimally invasive orthopedic surgery. During surgery an endoscope consisting of a camera and one or more light sources is inserted through a small incision into the body and the acquired images are analyzed. Since bone surface is featureless, shading is the primary cue for shape perception. However, due to the small field of view of the endoscope, only a small part of the bone and its occluding contour are visible in any single image. Therefore even human perception of bone shape from such images can be hard. We present a novel technique to reconstruct the surface of the bone by applying shape-from-shading to a sequence of endoscopic images, with partial boundary in each image. We first perform geometric and the photometric calibration for the endoscope. We then extend the classical shape-fromshading algorithm to include a near point light source that is not optically co-located with the camera. By tracking the endoscope we are able to align partial shapes obtained from different images in the global (world) coordinates. An ICP algorithm is then used to improve the matching, resulting in a complete occluding boundary of the bone. Finally, a complete and consistent shape is obtained by imultaneously re-growing surface normals and depths in all views. We demonstrate the accuracy of our technique using simulations and experiments with artificial bones.
Word Recognition based on Two Dimensional Lip Motion Trajectory

The visual information doesn't have an influence with the noise in comparison with the auditory information. However, lip-reading is earlier technology than speech recognition, there is a problem that recognition rate is low. This paper proposes a novel feature to obtain high recognition rate for lip-reading. The proposed lip-reading method is as follows. First, sampled active contour model is applied to detect lip contour during the input image sequences. The lip size is then normalized. Next, a time change of two lip shape features (area and aspect ratio) is expressed as a two-dimensional trajectory of the lip motion. The most similar trajectory in a database which is compared with the target trajectory by DP matching is selected as a result word. We recorded ten Japanese words that uses for the wheelchair control. As a result of comparative experiment with other features in which has already been proposed, trajectory feature obtained the highest recognition rate of 97.2%
Breast cancer mortality in Copenhagen after introduction of mammography screening: cohort study

Abstract Objectives To evaluate the effect on breast cancer mortality during the first 10 years of the mammography service screening programme that was introduced in Copenhagen in 1991. Design Cohort study. Setting The mammography service screening programme in Copenhagen, Denmark. Participants All women ever invited to mammography screening in the first 10 years of the programme. Historical, national, and historical national control groups were used. Main outcome measures The main outcome measure was breast cancer mortality. We compared breast cancer mortality in the study group with rates in the control groups, adjusting for age, time period, and region. Results Breast cancer mortality in the screening period was reduced by 25% (relative risk 0.75, 95% confidence interval 0.63 to 0.89) compared with what we would expect in the absence of screening. For women actually participating in screening, breast cancer mortality was reduced by 37%. Conclusions In the Copenhagen programme, breast cancer mortality was reduced without severe negative side effects for the participants.
Endomorphisms and Modular Theory of 2-Graph C*-Algebras

In this paper, we initiate the study of endomorphisms and modular theory of the graph C*-algebras $\O_{\theta}$of a 2-graph $\Fth$ on a single vertex. We prove that there is a semigroup isomorphism between unital endomorphisms of $\O_{\theta}$ and its unitary pairs with a \textit{twisted property}. We characterize when endomorphisms preserve the fixed point algebra $\fF$ of the gauge automorphisms and its canonical masa $\fD$. Some other properties of endomorphisms are also investigated. As far as the modular theory of $\O_{\theta}$ is concerned, we show that the algebraic *-algebra generated by the generators of $\O_{\theta}$ with the inner product induced from a distinguished state $\omega$ is a modular Hilbert algebra. Consequently, we obtain that the von Neumann algebra $\pi(\O_{\theta})"$ generated by the GNS representation of $\omega$ is an AFD factor of type III$_1$, provided $\frac{\ln m}{\ln n}\not\in\bQ$. Here $m,n$ are the numbers of generators of $\Fth$ of degree $(1,0)$ and $(0,1)$, respectively. This work is a continuation of \cite{DPY1, DPY2} by Davidson-Power-Yang and \cite{DY} by Davidson-Yang.
Conformal Predictions for Information Fusion A Comparative Study of P-Value Combination Methods

The increased availability of a wide range of sensing technologies over the last few decades has resulted in an equivalent increased need for reliable information fusion methods in machine learning applications. While existing theories such as the Dempster-Shafer theory and the possibility theory have been used for several years now, they do not provide guarantees of error calibration in information fusion settings. The Conformal Predictions (CP) framework is a new game-theoretic approach to reliable machine learning, which provides a methodology to obtain error calibration under classification and regression settings. In this work, we present a methodology to extend the Conformal Predictions framework to both classification and regression-based information fusion settings. This methodology is based on applying the CP framework to each data source as an independent hypothesis test, and subsequently using p-value combination methods as a test statistic for the combined hypothesis after fusion. The proposed methodology was studied in classification and regression settings within two real-world application contexts: person recognition using multiple modalities (classification), and head pose estimation using multiple image features (regression). Our experimental results showed that quantile methods of combining p-values (such as the Standard Normal Function and the Non-conformity Aggregation methods) provided the most statistically valid calibration results, and can be considered to extend the CP framework for information fusion settings.
Leakage power reduction in FPGA DSP circuits through algorithmic noise tolerance

We apply algorithmic noise-tolerance (ANT) techniques [1] to improve the energy efficiency of DSP circuits implemented on FPGAs. Our approach leverages the programmable power architectural feature in Altera commercial FPGAs that allows internal logic blocks to operate in two modes [2]: high speed or low (leakage) power. We build a main DSP circuit with high utilization of low-power mode blocks, reducing its speed and introducing errors into its output. The errors are subsequently (partially) corrected by a second (smaller) estimation circuit, producing an overall system with higher performance accuracy and lower power than a baseline system built with high-speed logic on its timing-critical paths. We demonstrate a filter implemented in a 40nm commercial FPGA that incorporates ANT and achieves higher SNR using 15% less static power than a traditional filter (without ANT).
Conversations in the Crowd: Collecting Data for Task-Oriented Dialog Learning

A major challenge in developing dialog systems is obtaining realistic data to train the systems for specific domains. We study the opportunity for using crowdsourcing methods to collect dialog datasets. Specifically, we introduce ChatCollect, a system that allows researchers to collect conversations focused around definable tasks from pairs of workers in the crowd. We demonstrate that varied and in-depth dialogs can be collected using this system, then discuss ongoing work on creating a crowd-powered system for parsing semantic frames. We then discuss research opportunities in using this approach to train and improve automated dialog systems in the future.
Non-monotonic Poisson Likelihood Maximization

This report summarizes the theory and some main applications of a new non-monotonic algorithm for maximizing a Poisson Likelihood, which for Positron Emission Tomography (PET) is equivalent to minimizing the associated Kullback-Leibler Divergence, and for Transmission Tomography is similar to maximizing the dual of a maximum entropy problem. We call our method non-monotonic maximum likelihood (NMML) and show its application to different problems such as tomography and image restoration. We discuss some theoretical properties such as convergence for our algorithm. Our experimental results indicate that speedups obtained via our non-monotonic methods are substantial.
Learning-based adaptive control for nonlinear systems

We present in this paper a preliminary result on learning-based adaptive trajectory tracking control for nonlinear systems. We propose, for the class of nonlinear systems with parametric uncertainties which can be rendered integral Input-to-State stable w.r. t. the parameter estimation errors input, that it is possible to merge together the integral Input-to-State stabilizing feedback controller and a model-free extremum seeking (ES) algorithm to realize a learning-based adaptive controller. We show the efficiency of this approach on a mechatronic example.
Conditional Constraints in a CLP-based HPSG Implementation

We present a CLP-based method for the direct implementation of HPSG. We interpret uniication of typed feature structures under the restrictions of principled constraints as constraint solving in the CLP paradigm. Principles of grammar, viewed as licensing conditions applying to every node of a feature structure, take the form of conditional constraints. The constraint solving mechanism, implemented in a variant of SICStus Prolog allowing for uniication extensions, takes care of delaying the enforcement of conditional constraints on insuuciently instantiated feature structures and thus provides a general means to specify delay conditions. Finally, conditionals allow for reformulation of disjunctions into a conjunction of implications, thereby reducing the search space.
Capacity and Delay of the Wireless Network with Special Relay Nodes

The seminal work by Grossglause and David Tse showed that the mobility can increase the capacity of the wireless ad hoc network. Then many works started to characterize the capacity-delay relationship and discuss the trade-off of them in a general model. The motivation of this paper is to focus on a kind of practical wireless networks, where some special relay nodes (SRNs) move in a fixed pattern.To answer the question how we can benefit from this case, we design a simple relay algorithm to employ the SRNs relaying the packets with the short route, and analyze the capacity and average delay under the proposed algorithm. For a slow mobility situation, the network can guarantee the O(radicN) transmission delay bound, while the capacity (per node) is maintained at a constant, where N is the network node number.
A Comparative Analysis of Different Classification Techniques for Intrusion Detection System

Intrusion Detection Systems are the network security mechanism that monitors network and system activities for malicious actions. It becomes indispensable tool to keep information system safe and reliable. The primary goal of intrusion detection is to model usual application behaviour, so that we can recognize attacks by their peculiar effects without raising too many false alarms. In this work data mining techniques are used for intrusion detection to identify normal and malicious actions on the system. The whole work considered Intrusion detection as a data analysis process. The Weka tool is used for analysis on KDD Cup [1] dataset. Algorithm REPTree & VFI(Voting Feature Interval) are chosen in this work with full training set and percentage split in which dataset can be divided into two ratio, and then one part is used as training set and the other part is applied as test set. The ROC curve is implemented for the comparison of classification algorithms. General Terms Intrusion detection, data mining, KDD cup dataset
2016 Ieee International Conference on Big Data (big Data) Accelerating Range Queries for Large-scale Unstructured Meshes

—Scientific datasets are steadily growing in size, due to increasing resolution and scale. Unstructured meshes are essential to certain fields of engineering and science, but they present special challenges for efficient access and processing. The work described in this paper accelerates range queries for very large unstructured meshes using the GPU. Prior work in the area introduced a preprocessing phase that partitions large unstructured meshes in order to improve locality in storage and memory. Here, we apply the computational power and bandwidth of GPUs to the partitioning problem, significantly reducing prepro-cessing time. In order to keep the GPU busy, we have to overcome the poor locality of the original unstructured mesh. Toward this end, we developed our own approach to unstructured mesh I/O, called Direct Load. We show that Direct Load significantly outperforms a typical LRU cache. Our ultimate goal is to accelerate range queries. Our pre-processing steps allow us to parallelize range query processing with relatively simple GPU code. Experimental results show that our implementation outperforms the serial implementations by 4× for preprocessing and over 100× for range queries.
Embedded-optimization-based loudspeaker compensation using a generic Hammerstein loudspeaker model

This paper presents an embedded-optimization-based algorithm for loudspeaker compensation using a generic Hammerstein loudspeaker model, i.e. a cascade of a memoryless nonlinearity and a linear finite impulse response filter. An optimization procedure is embedded into the algorithm to carry out the loudspeaker compensation on a frame-by-frame basis. In order to minimize the perceptible distortion incurred in the loudspeaker, a perceptually meaningful optimization criterion is constructed by using a psychoacoustic model. The resulting per-frame optimization problems are solved efficiently using a gradient optimization method. Objective evaluation experiments show that the proposed loudspeaker compensation algorithm provides a significant audio quality improvement, and this for all considered amplitude levels.
LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing

Languages, compilers, and computer-aided design tools will be essential for scalable quantum computing, which promises an exponential leap in our ability to execute complex tasks. LIQUi|> is a modular software architecture designed to control quantum hardware. It enables easy programming, compilation, and simulation of quantum algorithms and circuits, and is independent of a specific quantum architecture. LIQUi|> contains an embedded, domain-specific language designed for programming quantum algorithms, with F# as the host language. It also allows the extraction of a circuit data structure that can be used for optimization, rendering, or translation. The circuit can also be exported to external hardware and software environments. Two different simulation environments are available to the user which allow a trade-off between number of qubits and class of operations. LIQUi|> has been implemented on a wide range of runtimes as back-ends with a single user front-end. We describe the significant components of the design architecture and how to express any given quantum algorithm.
Complement Set Reference after Implicitly Small Quantities: An Event-Related Potentials Study

ABSTRACT An anaphoric reference to the complement-set is a reference to the set that does not fulfil the predicate of the preceding sentence. Preferred reference to the complement-set has been found in eye movements when a character’s implicit desire for a high amount has been denied using a negative emotion. We recorded event-related potentials to examine if, when a character’s desire is denied with a negative emotion, the complement-set is immediately available for reference. Analysis of the N400 over posterior regions showed that although readers favored the reference-set after a positive emotion, there was no difference in responses between complement-set and reference-set references after a negative emotion. Processing of a complement-set reference did lead to an overall increase in negativity of the N400, suggesting that interpreting a complement-set reference incurred a general processing cost. This study provides novel data on the range of circumstances under which the complement-set is available.
Parental communication and collaboration in schools with special educational needs (SEN) programmes in Bhutan

ABSTRACT This study explored the experiences of communication and collaboration of parents with the teachers of their children with special educational needs (SEN). In this investigation in the Bhutanese context, 26 parents (13 fathers and 13 mothers) of children with SEN, either fully or partially included in 3 schools with SEN programmes in Bhutan located in urban, semi-urban, and rural regions in Bhutan were individually interviewed. The interview data were recorded, transcribed, and analysed using both manual thematic coding semantic mapping and text mining analysis (Leximancer). The majority of parents (n = 21/26) were found to have either minimal or no communication or collaboration with the school. It was mainly a few mothers (n = 4), and no fathers, who reported consistent communication and collaboration with the school, whose children were partially included in the schools and had high support needs. The findings have implications for building channels of communication and collaboration between parents and school staff in order to support their children with SEN in Bhutan.
Increased interoperability: Evolution of 6LoWPAN-based web application

IPv6 over Low-Power Wireless Personal Area Networks, as known as 6LoWPAN, has become a main topic of Wireless Sensor Network (WSN) researches. Because of the address space and openness of grand scale, more and more powerful applications have been introduced using 6LoWPAN. Our aim was to investigate the interoperability of 6LoWPAN-based web application. Furthermore, we implemented a system prototype of dynamically configurable IP-based WSN, based on ATAVRRZRAVEN evaluation kit and Contiki operating system. The prototype is important for the configuration and deployment of 6LoWPAN devices in WSN projects.
Modularity of a Cambrian ptychoparioid trilobite cranidium

SUMMARY Modularity of the cranidium of Crassifimbra? metalaspis, a Cambrian ptychoparioid trilobite, is investigated using landmark‐based geometric morphometric methods to gain insight into the integration among morphogenetic processes responsible for shaping the head of an ancient arthropod. Of particular interest is the extent to which the structure of phenotypic integration was governed by direct interactions among developmental pathways, because these interactions may generate long‐term constraints on evolutionary innovation. A modified two‐way ANOVA decomposes cranidial shape variation into components representing symmetric variation among individuals and fluctuating asymmetry (FA). The structure of integration of each of these components is inferred from correlated deviations in shape among nine partitions of the cranidium. Significant correlation among partitions in FA indicates direct interactions among their respective developmental pathways. An a priori hypothesis that modularity was determined by functional association among partitions is not well supported by the among‐partition correlation structure for either component of variation. Instead, exploratory analyses reveal that phenotypic integration was strongly influenced by spatially localized morphogenetic controls. Comparison of the structures of the Individuals and FA components of variation reveals that the two share relatively few commonalities: the structure of phenotypic integration was only weakly influenced by direct interactions. The large contribution of parallel variation to phenotypic integration suggests that modularity was unlikely to have imposed a long‐term constraint on evolutionary innovation in these early trilobites.
Analysis of Classification Algorithm on Hypergraph

Classification learning problem on hypergraph is an extension of multi-label classification problem on normal graph, which divides vertices on hypergraph into several classes. In this paper, we focus on the semi-supervised learning framework, and give theoretic analysis for spectral based hypergraph vertex classification semi-supervised learning algo- rithm. The generalization bound for such algorithm is determined by using the notations of zero-cut, non-zero-cut and pure component. Furthermore, we derive a generalization performance bound for near-zero-cut partition with optimal pa- rameter￻.
Supporting Direction Relations in Spatial Database systems

Despite the attention that direction relations have attracted in domains such as Spatial Query Languages, Image and Multimedia Databases, Reasoning and Geographic Applications, they have not been extensively applied in spatial access methods. In this paper we define direction relations between two-dimensional objects in different levels of qualitative resolution and we show how these relations can be efficiently retrieved in existing DBMSs using several indexing techniques. Then we describe our implementations and provide experimental results on Band Rtree-based data structures to support direction relations efficiently.