Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining multiple decisions: applications to bioinformatics

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.
On oscillatory integral operators with folding canonical relations

1.Introduction Let X and Y be open sets in R d and let X Y be a bounded open set whose closure is contained in X Y. We consider oscillatory integral operators Here 1 and is a parameter in a manifold ? We assume that a ;; 2 C 1 0 (() It may sometimes be convenient to admit some growth in for the (x; y) derivatives of the amplitude. Let 0 1. We shall say that the family of amplitudes
A Novel Shot Boundary Detection Method Based on Genetic Algorithm-Support Vector Machine

Shot boundary detection (SBD) plays an important role in content-based video retrieval. In this paper, a novel algorithm for SBD based on support vector machine (SVM) and genetic algorithm (GA) is proposed. First of all, features of pixel domain and compressed domain are synthetically extracted, and then organized into a multi-dimension vector by using the method of sliding window. Following that, the genetic algorithm is utilized to implement the simulation and iterative optimization towards parameters of SVM kernel function, then the model trained by the approximately optimal parameters is applied to judge and classify the frames of video, thus SBD is completed. The proposed algorithm solves the difficulty in parameter selection of SVM, and experimental results on the TREC-2001 video data set indicate the effectiveness and robustness of our algorithm.
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation

Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers. In this paper, we propose to apply a novel method, namely Hybrid Orthogonal Projection and Estimation (HOPE), to CNNs in order to introduce orthogonality into the CNN structure. The HOPE model can be viewed as a hybrid model to combine feature extraction using orthogonal linear projection with mixture models. It is an effective model to extract useful information from the original high-dimension feature vectors and meanwhile filter out irrelevant noises. In this work, we present three different ways to apply the HOPE models to CNNs, i.e., {\em HOPE-Input}, {\em single-HOPE-Block} and {\em multi-HOPE-Blocks}. For {\em HOPE-Input} CNNs, a HOPE layer is directly used right after the input to de-correlate high-dimension input feature vectors. Alternatively, in {\em single-HOPE-Block} and {\em multi-HOPE-Blocks} CNNs, we consider to use HOPE layers to replace one or more blocks in the CNNs, where one block may include several convolutional layers and one pooling layer. The experimental results on both Cifar-10 and Cifar-100 data sets have shown that the orthogonal constraints imposed by the HOPE layers can significantly improve the performance of CNNs in these image classification tasks (we have achieved one of the best performance when image augmentation has not been applied, and top 5 performance with image augmentation).
University of Birmingham PrefMiner: Mining User’s Preferences for Intelligent Mobile Notification Management

Mobile notiﬁcations are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notiﬁcations are neither useful nor relevant to users’ interests and, also for this reason, they are considered disruptive and potentially annoying. In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solu-tion that learns users’ preferences for receiving notiﬁcations based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a “black-box” solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notiﬁcation dataset and its effectiveness is evaluated by means of an in-the-wild deployment.
Threat of DoS by interest flooding attack in content-centric networking

Content-Centric Networking (CCN) is one of promising architecture based on ICN concept for Future Internet. In contrast to current IP network, CCN can overcome inefficient content distribution caused by the IP architecture in the situation of today's traffic flooding of internet. However, CCN is exposed to Denial of Service (DoS) attack by sending large number of Interest rapidly, called Interest flooding attack since content name in Interest cannot be verified in CCN. In this paper, we prove that Interest flooding attack can be applied for DoS in CCN based on the simulation results which can affect quality of service. We expect that it contributes to give a security issue about potential threats of DoS in CCN.
The Impact of Interpixel Capacitance on WFIRST PSFs

Unlike optical CCDs, near-infrared detectors, which are based on CMOS hybrid readout technology, typically suffer from electrical crosstalk between the pixels. The interpixel capacitance (IPC) responsible for the crosstalk affects the point-spread function (PSF) of the telescope, increasing the size and modifying the shape of all objects in the images while correlating the Poisson noise. Upcoming weak lensing surveys that use these detectors, such as WFIRST, place stringent requirements on the PSF size and shape (and the level at which these are known), which in turn must be translated into requirements on IPC. To facilitate this process, we present a first study of the effect of IPC on WFIRST PSF sizes and shapes. Realistic PSFs are forward-simulated from physical principles for each WFIRST bandpass. We explore how the PSF size and shape depends on the range of IPC coupling with pixels that are connected along an edge or corner; for the expected level of IPC in WFIRST, IPC increases the PSF sizes by $\sim$5\%. We present a linear fitting formula that describes the uncertainty in the PSF size or shape due to uncertainty in the IPC, which could arise for example due to unknown time evolution of IPC as the detectors age or due to spatial variation of IPC across the detector. We also study of the effect of a small anisotropy in the IPC, which further modifies the PSF shapes. Our results are a first, critical step in determining the hardware and characterization requirements for the detectors used in the WFIRST survey.
On combining Laplacian and optimization-based mesh smoothing techniques

Local mesh smoothing algorithms have been shown to be effective in repairing distorted elements in automatically generated meshes. The simplest such algorithm is Laplacian smoothing, which moves grid points to the geometric center of incident vertices. Unfortunately, this method operates heuristically and can create invalid meshes or elements of worse quality than those contained in the original mesh. In contrast, optimization-based methods are designed to maximize some measure of mesh quality and are very effective at eliminating extremal angles in the mesh. These improvements come at a higher computational cost, however. In this article the author proposes three smoothing techniques that combine a smart variant of Laplacian smoothing with an optimization-based approach. Several numerical experiments are performed that compare the mesh quality and computational cost for each of the methods in two and three dimensions. The author finds that the combined approaches are very cost effective and yield high-quality meshes.
Chapter 9 k-clique Percolation and Clustering

We summarise recent results connected to the concept of k-clique percolation. This approach can be considered as a generalisation of edge percolation with a great potential as a community finding method in real-world graphs. We present a detailed study of the critical point for the appearance of a giant kclique percolation cluster in the Erdős–Rényi-graph. The observed transition is continuous and at the transition point the scaling of the giant component with the number of vertices is highly non-trivial. The concept is extended to weighted and directed graphs as well. Finally, we demonstrate the effectiveness of k-clique percolation as a community finding method via a series of real-world applications.
Lossless clustering of histories in decentralized POMDPs

Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute a generic and expressive framework for multiagent planning under uncertainty. However, planning optimally is difficult because solutions map local observation histories to actions, and the number of such histories grows exponentially in the planning horizon. In this work, we identify a criterion that allows for lossless clustering of observation histories: i.e., we prove that when two histories satisfy the criterion, they have the same optimal value and thus can be treated as one. We show how this result can be exploited in optimal policy search and demonstrate empirically that it can provide a speed-up of multiple orders of magnitude, allowing the optimal solution of significantly larger problems. We also perform an empirical analysis of the generality of our clustering method, which suggests that it may also be useful in other (approximate) Dec-POMDP solution methods.
Ground subsidece monitoring along the levee of Huangpu River by multi-platform high-resolution SAR images

Persistent scatterer interferometry(PSI) technology has been applied to ground subsidence monitoring in urban area and achieved good results. However, only displacement along the direction of radar line of sight can be obtained by single platform SAR images, so the deformation results can't reflect the real settlement in the vertical direction. With the increasing of current space-borne SAR satellites, more measurements make it possible to reconstruct the vertical subsidence by the least squares solution using SAR images from different side looking angles. This paper outlines the technical process of extracting vertical deformation rate by using multi-platform PSI method. 43 km levee of Huangpu River, which passes through the Shanghai central urban area, is chosen as a test for monitoring their ground subsidence. 35 scenes of COSMO-SkyMed ascending images, 35 scenes of COSMO-SkyMed descending images and 16 scenes of TerraSAR-X images obtained from 2009 to 2010 are processed to extract ground subsidence of 1km width zone along Huangpu River. The deformation rates obtained by different platform show strong consistency and the vertical deformation rates derived by the multi-platform after comparing with existing leveling measurements show high accuracy.
Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks

Drivers' fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can continuously monitor and estimate the alertness level of drivers. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper for continuously monitoring driver's drowsiness level with concurrent changes in the alertness level. A novel adaptive feature selection mechanism is developed for automatically selecting effective frequency bands of ICA components for realizing an on-line alertness monitoring system based on the correlation analysis between the time-frequency power spectra of ICA components and the driving errors defined as the deviation between the center of the vehicle and the cruising lane in the virtual-reality driving environment. The mechanism also provides effective and efficient features that can be fed into ICA-mixture-model-based self-constructing FNN to indirectly estimate driver's drowsiness level expressed by approximately and predicting the driving error
Recovering photometric properties of multiple strongly-reflective, partially-transparent surfaces from a single image

This paper introduces a method to recover photometric parameters of a set of 3D surfaces from a single image with significant global-illumination effects such as inter-reflections and transparencies. Since this problem is ambiguous for arbitrary unknown scenes, our formulation assumes that the scene consists of a small set of photometrically homogeneous surfaces with known 3D shapes, illuminated by known light sources. We show that under these conditions, the system of nonlinear equations that defines how the image is formed may be factorized into a vector composed only of products of some photometric parameters, and a matrix, whose elements depend non-linearly on both the known illumination, the known 3D shapes and the remaining photometric parameters. This factorization leads to an efficient optimization-based algorithm to compute all unknown photometric parameters from a single input image. Experiments with real data show that this algorithm is more stable and efficient than simpler alternatives
Moral Hazard, Income Taxation and Prospect Theory

The standard theory of optimal income taxation under uncertainty has been developed under the assumption that individuals maximise expected utility. However, prospect theory has now been established as an alternative model of individual behaviour, with empirical support. This paper explores the theory of optimal income taxation under uncertainty when individuals behave according to the tenets of prospect theory. It is seen that many of the standard results are modified in interesting ways. The first-order approach for solving the optimisation problem is not valid over the domain of losses, and the marginal tax schedule offers full insurance around the reference consumption level. The implications of non-welfarist objectives under income uncertainty are also examined.
cJ: enhancing java with safe type conditions

cJ is an extension of Java that allows supertypes, fields, and methods of a class or interface to be provided only under some static subtyping condition. For instance, a cJ generic class, C<P>, may provide a member method m only when the type provided for parameter P is a subtype of a specific type Q.From a practical standpoint, cJ adds to generic Java classes and interfaces the ability to express case-specific code. Unlike conditional compilation techniques (e.g., the C/C++ "#ifdef" construct) cJ is statically type safe and maintains the modular type-checking properties of Java generic classes: a cJ generic class can be checked independently of the code that uses it. Just like regular Java, checking a cJ class implies that all uses are safe, under the contract for type parameters specified in the class's signature.As a specific application, cJ addresses the well-known shortcomings of the Java Collections Framework (JCF). JCF data structures often throw run-time errors when an "optional" method is called upon an object that does not support it. Within the constraints of standard Java, the authors of the JCF had to either sacrifice static type safety or suffer a combinatorial explosion of the number of types involved. cJ avoids both problems, maintaining both static safety and conciseness.
AWGR-based large port count data center architecture with extended frequency range

Arrayed Waveguide Grating Router (AWGR) is currently one of the most promising components in data center interconnections for its passive and non-blocking feature. However, the scalability is restricted by the limited channel count. In this paper, by using multiple free spectral ranges (FSRs) of AWGR and couplers, we report a novel data center architecture with extended frequency range. By simulation, we prove that, with limited FSRs, tens of thousands port count data center can be realized using a single 32 × 32 AWGR with less than 1 ms average delay and less than 0.1% blocking rate.
A new header compression scheme for TCP streams in IP based wireless networks

The current trend in the telecommunication world is the proposal of a universal telecommunication system deriving from the convergence between mobile telephony and data transmission. To achieve such a convergence in an efficient way, it is presumably necessary to utilize a network platform totally based on TCP/IP architecture (an ‘All-IP Network’). However, the encapsulation process of TCP/IP wastes a substantial part of the radio channel bandwidth for the transmission of control information (header) that does not have any specific function at radio level. Since this bandwidth is generally the most expensive and limited resource for wireless networks, it is necessary to adopt a header compression scheme that can reduce the protocol overhead while working efficiently and robustly under noisy links with time-variant BER conditions. In this paper, a new header compression scheme for TCP streams as a specific header compression profile within the IETF ROHC (robust header compression) framework is proposed and analyzed through simulations exploiting network simulator (ns-2). The proposed scheme, compliant with IETF requirements for TCP/IP header compression, is based on the distinct management of data and acknowledgment (ACK) streams associated with the TCP connection and exploits a robust header encoding technique for reducing the effect of error propagation. The results obtained, in terms of throughput, overhead and goodput, are reported as a function of bandwidth and BER. A comparison with the results obtained without compression allows us to evaluate the performance of the proposed compression scheme in a realistic and dynamic environment. Copyright © 2004 AEI
Low Cost Fpga-Based Highly Accurate Face Recognition System using Combined Wavelets with Subspace Methods

Low cost implementation of an accurate face recognition system is useful in many applications. A face recognition algorithm based on Daubechies wavelets is proposed. The algorithm uses each of ICA or PCA for dimensionality reduction. Wavelets are employed to increase the discriminative power of subspace methods. Effectiveness of using wavelets is shown by performing comparison between proposed algorithm and other solutions. Complete hardware implementation of the algorithm is then proposed. It is shown that the entire face recognition system can be implemented on a low cost 300 K gates Spartan-II FPGA. The system works with high frame per second processing rate and only half of the logic resources on FPGA are consumed by face recognition logic.
Importance and Limitations of Modeling Parasitic Capacitance between Package and PCB for Power Bus Noise and Radiation

Power and ground planes on multilayer PCBs can effectively radiate electromagnetic fields excited by the IC simultaneous switching noise. The high frequency electromagnetic radiation is often calculated from the electric field along the edge of the PCB, which can be estimated with a cavity model using magnetic walls. The excitation of the cavity modes is related to the via current passing through the power bus planes at the interconnection between IC package and PCB. Usually the attention is focused on the differential-mode current of the package pins, but in the present paper it is shown that the common-mode current flowing out from package pins plays a very important role in the excitation of cavity modes, and its neglect implies a fatal underestimation of the electromagnetic radiation from the power bus planes in some circumstances. A second important contribute to the radiation is given by the common mode current on the pins, together with the current flowing on the PCB ground plane. With the proposed equivalent circuit, the effectiveness of decoupling inductors depending on their location and on the value of the parasitic capacitance is studied.
Osteopontin-Enhanced Hepatic Metastasis of Colorectal Cancer Cells

Liver metastasis is a major cause of mortality from colorectal cancer (CRC). However, mechanisms underlying this process are largely unknown. Osteopontin (OPN) is a secreted phosphorylated glycoprotein that is involved in tumor migration and metastasis. The role of OPN in cancer is currently unclear. In this study, OPN mRNA was examined in tissues from CRC, adjacent normal mucosa, and liver metastatic lesions using quantitative real-time PCR analysis. The protein expression of OPN and its receptors (integrin αv and CD44 v6) was detected by using an immunohistochemical (IHC) method. The role of OPN in liver metastasis was studied in established colon cancer Colo-205 and SW-480 cell lines transfected with sense- or antisense-OPN eukaryotic expression plasmids by flow cytometry and cell adhesion assay. Florescence redistribution after photobleaching (FRAP) was used to study gap functional intercellular communication (GJIC) among OPN-transfected cells. It was found that OPN was highly expressed in metastatic hepatic lesions from CRC compared to primary CRC tissue and adjacent normal mucosa. The expression of OPN mRNA in tumor tissues was significantly related with the CRC stages. OPN expression was also detected in normal hepatocytes surrounding CRC metastatic lesions. Two known receptors of OPN, integrin αv and CD44v6 proteins, were strongly expressed in hepatocytes from normal liver. CRC cells with forced OPN expression exhibited increased heterotypic adhesion with endothelial cells and weakened intercellular communication. OPN plays a significant role in CRC metastasis to liver through interaction with its receptors in hepatocytes, decreased homotypic adhesion, and enhanced heterotypic adhesion.
A 2-Gb/s 0.5-/spl mu/m CMOS parallel optical transceiver with fast power-on capability

This paper describes an optical transceiver designed for power-efficient connections within high-speed digital systems, specifically for board- and backplane-level interconnections. A 2-Gb/s, four-channel, dc-coupled differential optical transceiver was fabricated in a 0.5-/spl mu/m complementary metal-oxide-semiconductor (CMOS) silicon-on-sapphire (SoS) process and incorporates fast individual-channel power-down and power-on functions. A dynamic sleep transistor technique is used to turn off transceiver circuits and optical devices during power-down. Differential signaling (using two optical channels per signal) enables self-thresholding and allows the transceiver to quickly return from power-down to normal operation. A free-space optical link system was built to evaluate transceiver performance. Experimental results show power-down and power-on transition times to be within a few nanoseconds. Crosstalk measurements show that these transitions do not significantly impact signal integrity of adjacent active channels.