Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet-Based Weighted LASSO and Screening Approaches in Functional Linear Regression

One useful approach for fitting linear models with scalar outcomes and functional predictors involves transforming the functional data to wavelet domain and converting the data-fitting problem to a variable selection problem. Applying the LASSO procedure in this situation has been shown to be efficient and powerful. In this article, we explore two potential directions for improvements to this method: techniques for prescreening and methods for weighting the LASSO-type penalty. We consider several strategies for each of these directions which have never been investigated, either numerically or theoretically, in a functional linear regression context. We compare the finite-sample performance of the proposed methods through both simulations and real-data applications with both 1D signals and 2D image predictors. We also discuss asymptotic aspects. We show that applying these procedures can lead to improved estimation and prediction as well as better stability. Supplementary materials for this article are available online.
Nonlinear image recovery with half-quadratic regularization

One popular method for the recovery of an ideal intensity image from corrupted or indirect measurements is regularization: minimize an objective function that enforces a roughness penalty in addition to coherence with the data. Linear estimates are relatively easy to compute but generally introduce systematic errors; for example, they are incapable of recovering discontinuities and other important image attributes. In contrast, nonlinear estimates are more accurate but are often far less accessible. This is particularly true when the objective function is nonconvex, and the distribution of each data component depends on many image components through a linear operator with broad support. Our approach is based on an auxiliary array and an extended objective function in which the original variables appear quadratically and the auxiliary variables are decoupled. Minimizing over the auxiliary array alone yields the original function so that the original image estimate can be obtained by joint minimization. This can be done efficiently by Monte Carlo methods, for example by FFT-based annealing using a Markov chain that alternates between (global) transitions from one array to the other. Experiments are reported in optical astronomy, with space telescope data, and computed tomography.
Daip1, a Dictyostelium Homologue of the Yeast Actin-Interacting Protein 1, Is Involved in Endocytosis, Cytokinesis, and Motility

The 64-kD protein DAip1 from Dictyostelium contains nine WD40-repeats and is homologous to the actin-interacting protein 1, Aip1p, from Saccharomyces cerevisiae, and to related proteins from Caenorhabditis, Physarum, and higher eukaryotes. We show that DAip1 is localized to dynamic regions of the cell cortex that are enriched in filamentous actin: phagocytic cups, macropinosomes, lamellipodia, and other pseudopodia. In cells expressing green fluorescent protein (GFP)-tagged DAip1, the protein rapidly redistributes into newly formed cortical protrusions. Functions of DAip1 in vivo were assessed using null mutants generated by gene replacement, and by overexpressing DAip1. DAip1-null cells are impaired in growth and their rates of fluid-phase uptake, phagocytosis, and movement are reduced in comparison to wild-type rates. Cytokinesis is prolonged in DAip1-null cells and they tend to become multinucleate. On the basis of similar results obtained by DAip1 overexpression and effects of latrunculin-A treatment, we propose a function for DAip1 in the control of actin depolymerization in vivo, probably through interaction with cofilin. Our data suggest that DAip1 plays an important regulatory role in the rapid remodeling of the cortical actin meshwork.
Lifetime estimation of events from Dynamic Vision Sensors

We propose an algorithm to estimate the “lifetime” of events from retinal cameras, such as a Dynamic Vision Sensor (DVS). Unlike standard CMOS cameras, a DVS only transmits pixel-level brightness changes (“events”) at the time they occur with micro-second resolution. Due to its low latency and sparse output, this sensor is very promising for high-speed mobile robotic applications. We develop an algorithm that augments each event with its lifetime, which is computed from the event's velocity on the image plane. The generated stream of augmented events gives a continuous representation of events in time, hence enabling the design of new algorithms that outperform those based on the accumulation of events over fixed, artificially-chosen time intervals. A direct application of this augmented stream is the construction of sharp gradient (edge-like) images at any time instant. We successfully demonstrate our method in different scenarios, including high-speed quadrotor flips, and compare it to standard visualization methods.
Learning attack strategies through mining and correlation of security alarms

Huge volume of security data from different security devices can overwhelm security managers and keep them from performing effective analysis and initiating timely response. Therefore, it is important to develop an advanced alert correlation system that can reduce alert redundancy, intelligently correlate security alerts and detect attack strategies. In this paper, we proposed a new method of mining multi-stage attack behaviors pattern in order to recognize attacker's high-level strategies and predict upcoming attack intentions. We apply a reformative Apriori algorithm to mine frequent attack sequence patterns from history alert data. We use correlativity between two contextual elements in the attack sequence to correlate attack behaviors and identify potential attack intentions. The idea is easy to implement and it can be used to detect novel multi-stage attack strategies compared with other techniques. Experiments show that our approach can effectively learn high level attack strategies and can accordingly predict next possible attack behavior.
Active dependency formation in islands: How grammatical resumption affects sentence processing

Abstract: Previous studies concluded that despite the parser’s eagerness to resolve filler-gap dependencies, in island configurations it prefers to posit late grammatical gaps over early ungrammatical ones. This study investigates the possibility of resolving filler-gap dependencies inside Hebrew islands. We investigated the acceptability of resumptive pronouns (RPs) in two islands and the sensitivity of on-line dependency formation to the status of those RPs. Results revealed a filled-gap effect inside the island that allows RPs but not inside the one that prohibits them. This suggests that active dependency formation can proceed inside islands, and that when processing dependencies with islands, the Hebrew parser prefers an early RP over a later gap.
Option Pricing on Cash Mergers

When a cash merger is announced but not completed, there are two main sources of uncertainty related to the target company: the probability of success and the price conditional on the deal failing. We propose an arbitrage-free option pricing formula that focuses on these sources of uncertainty. We test our formula in a study of all cash mergers between 1996 and 2008 which have sufficiently liquid options traded on the target company. The estimated success probability is a good predictor of the deal outcome. Our option formula for cash mergers does significantly better than the Black– Scholes formula and produces a volatility smile close to the one observed in practice. In particular, we provide an explanation for the kink in the volatility smile and show that the kink increases with the probability of deal success.
FAST MOTION ESTIMATION BASED ON MOTION CLASSIFICATION AND DIRECTIONAL SEARCH PATTERNS 1

1. OVERVIEW In real world video sequences the motion field is very structured and there exists a strong correlation among neighboring blocks in spatial and temporal domain. This suggests that significant computational gain can be achieved by taking advantage of strong spatio-temporal dependencies that exist between the motion vectors. The main advantage of prediction based algorithms is that it can help alleviate the local minimum problem to some extent. As the new initial/predicted search center is usually closer to the global minimum candidate, the chances of getting trapped in local minimum decreases. It is possible that fast prediction based initial search may generate more sensible initial motion vectors that work better for the next stage of refinement search. Also motion vectors estimated in prediction algorithm are more realistic since it reflects the real physical phenomenon i.e. real motion, where as FS just finds the minimum SAD which does not necessarily give the realistic motion vectors (and is sometimes a bad choice) because of the complexity and randomness of the real world imagery.
Approximate Subdivision Surface Evaluation in the Language of Linear Algebra

We present an interpretation of approximate subdivision surface evaluation in the language of linear algebra. Specifically, vertices in the refined mesh can be computed by left-multiplying the vector of control vertices by a sparse matrix we call the subdivision operator. This interpretation is rather general: it applies to any level of subdivision, it holds for many common subdivision schemes (including Catmull-Clark and Loop), it can be extended to support hierarchical edit operations, and it subsumes sharpness and feature-adaptive schemes. Furthermore, our interpretation encourages high-performance implementations built on numerical linear algebra libraries. It is most applicable to subdivision of static control meshes undergoing deformation, i.e. animation, in which case it allows users to trade-off time-to-first-frame and framerate. We implemented our strategy as an extension to Pixar’s production subdivision code and observed speedups of 2x to 14x using both multicore CPUs and GPUs. CR Categories: I.3. 5 [Computer Graphics]: Picture/Image Generation—Display algorithms; I.3. 7 [Computer Graphics]: Computational Geometry and Object Modelling—Curve, surface, solid, and object representation
A proactive and responsive bio-psychosocial approach to managing challenging behaviour in mainstream primary classrooms

ABSTRACT Research in the area of social, emotional and behavioural difficulties, and teacher’s struggle to respond to challenging behaviour in mainstream classrooms, has consistently highlighted the need for proventative and responsive approaches within a continuum of support. This study aims to, firstly, investigate what proactive and reactive strategies Irish primary school teachers are currently utilising in mainstream classrooms. Secondly, it aims to ascertain a rating of the perceived effectiveness of these strategies within an overarching bio-psychosocial framework. A total of 274 mainstream teachers were surveyed online, whereby quantitative and qualitative data was collected and analysed. Results suggest that teachers are drawing upon research in the area of bio-physiological, cognitive, behavioural, person-centred, humanistic and eco-social psychology. ‘Building a rapport with the child’ was rated as the Most Effective proactive strategy by 39% of teachers and ‘Acknowledging the child’s feelings’ was rated as the Most Effective responsive strategies by 25% of teachers.
Continuous real-world inputs can open up alternative accelerator designs

Motivated by energy constraints, future heterogeneous multi-cores may contain a variety of accelerators, each targeting a subset of the application spectrum. Beyond energy, the growing number of faults steers accelerator research towards fault-tolerant accelerators. In this article, we investigate a fault-tolerant and energy-efficient accelerator for signal processing applications. We depart from traditional designs by introducing an accelerator which relies on unary coding, a concept which is well adapted to the continuous real-world inputs of signal processing applications. Unary coding enables a number of atypical micro-architecture choices which bring down area cost and energy; moreover, unary coding provides graceful output degradation as the amount of transient faults increases. We introduce a configurable hybrid digital/analog micro-architecture capable of implementing a broad set of signal processing applications based on these concepts, together with a back-end optimizer which takes advantage of the special nature of these applications. For a set of five signal applications, we explore the different design tradeoffs and obtain an accelerator with an area cost of 1.63mm2. On average, this accelerator requires only 2.3% of the energy of an Atom-like core to implement similar tasks. We then evaluate the accelerator resilience to transient faults, and its ability to trade accuracy for energy savings.
A Distributed, Reconfigurable, and Reusable BIST Infrastructure for Test and Diagnosis of 3-D-Stacked ICs

We present an end-to-end design of a built-in self-test (BIST) and BIST-based diagnosis infrastructure for 3-D-stacked integrated circuits (ICs) that facilitates the use of BIST at multiple stages of 3-D integration. The proposed BIST design is distributed, reusable, and reconfigurable, hence it is attractive for both prebond and post-bond testing. We also provide support for translating a static BIST schedule into a set of BIST control instructions. The BIST design is validated using detailed simulations of the various operating modes. A framework for fault diagnosis using the BIST infrastructure for 3-D-stacked ICs is also proposed. We present results on synthetic stacks created from ITC'99 and Open-Core benchmark circuits and assess the impact of inserting BIST in these designs in terms of area, timing, and power overhead. Results show that the overhead due to BIST is negligible. We also formulate a test-scheduling problem that aims at minimizing test time under BIST-resource and power constraints, and use two algorithms based on bin packing for solving the problem.
Person entity attributes association in list pages

Person entity attributes association is a basic task in WePS, but it's still a challenging task because of heterogeneous web pages which can be classified into two categories, list page and detail page. In this paper, we study the problem of associating person entity attributes in various list pages. Our target is to find a consolidated solution as general as possible to deal with list pages in person entity attributes association. In contrast to most existing methods, we extract visual features and leverage Markov logic networks (MLNs) to effectively integrate all useful evidence by learning there importance automatically. The experimental results show a very encouraging performance.
Comparison of acoustic modeling techniques for Vietnamese and Khmer ASR

This paper presents a comparison of some different acoustic modeling strategies for under-resourced languages. When only limited speech data are available for under-resourced languages, we propose some crosslingual acoustic modeling techniques. We apply and compare these techniques in Vietnamese ASR. Since there is no pronunciation dictionary for some underresourced languages, we investigate grapheme-based acoustic modeling. Some initialization techniques for context independent modeling and some question generation techniques for context dependent modeling are applied and compared for Khmer ASR.
A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes

Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle’s Theorem, all monadic second-order (“in a certain logic”) properties of graphs of bounded tree width (“structured in a certain way”) can be solved in linear time (“with a certain amount of resources”). Such theorems have become valuable tools in algorithmics: if a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems. This paper is intended as a gentle introduction to the ideas behind algorithmic metatheorems, especially behind some recent results concerning space and circuit classes, and tries to give a flavor of the range of their applications.
International Conference on Information Fusion ( FUSION ) Tracking Ground Targets Using State Vector Fusion

There are various algorithms in state vector fusion for tracking ground targets. The good situation picture established from tracking these ground targets will help operators to achieve their mission objectives better. The state vector fusion approach involves combining tracks, where a track is the state vector of a target. These algorithms include the simple convex combination, the Bar-Shalom/Campo state vector combination and the covariance intersection (CI). This paper will describe the main state vector fusion algorithms in detail and also present the comparison results ofthese algorithms based on simulation data using two and three sensors on aparticular platform.
RF MEMS Passives on High-Resistivity Silicon Substrates

Diverse RF passive devices and microelectro- mechanical systems (MEMS) can be monolithically integrated on a high-resistivity silicon (HR-Si) substrate. However, parasitic surface conduction (PSC) at the interface of HR-Si and a silicon dioxide (SiO2) passivation layer reduces the effective substrate resistivity, which in turn results in deterioration of the device quality factor (Q) and non-linearity. Trap-rich HR-Si has been proposed as a low substrate loss alternative, eliminating the problems associated with PSC. However, the full potential of trap-rich HR-Si as a common platform for implementing MEMS passives is not fully explored. In this letter, we evaluate the effectiveness of the trap-rich layer by comparing the frequency response of a number of RF passive devices fabricated on standard and trap-rich HR-Si substrates. In addition, we suggest an electromagnetic (EM) simulation setup that can be used to efficiently and accurately simulate the device performance.
SDN-like: The Next Generation of Pub/Sub

Software-Defined Networking (SDN) has raised the boundaries of cloud computing by offering unparalleled levels of control and flexibility to system administrators over their virtualized environments. To properly embrace this new era of SDN-driven network architectures, the research community must not only consider the impact of SDN over the protocol stack, but also on its overlying networked applications. In this big ideas paper, we study the impact of SDN on the design of future message-oriented middleware, specifically pub/sub systems. We argue that key concepts put forth by SDN can be applied in a meaningful fashion to the next generation of pub/sub systems. First, pub/sub can adopt a logically centralized controller model for maintenance, monitoring, and control of the overlay network. We establish a parallel with existing work on centralized pub/sub routing and discuss how the logically centralized controller model can be implemented in a distributed manner. Second, we investigate the separation of the control and data plane, which is integral to SDN, which can be adopted to raise the level of decoupling in pub/sub. We introduce a new model of pub/sub which separates the traditional publisher and subscriber roles into flow regulators and producer/consumers of data. We then present use cases that benefit from this approach and study the impact of decoupling for performance.
Cudagrind: A Valgrind Extension for CUDA

Valgrind, and specifically the included tool Memcheck, offers an easy and reliable way for checking the correctness of memory operations in programs. This works in an unintrusive way where Valgrind translates the program into intermediate code and executes it on an emulated CPU. The heavy weight tool Memcheck uses this to keep a full shadow copy of the memory used by a program and tracking accesses to it. This allows the detection of memory leaks and checking the validity of accesses. Though suited for a wide variety of programs, this approach still fails when accelerator based programming models are involved. The code running on these devices is separate from the code running on the host. Access to memory on the device and starting of kernels is being handled by an API provided by the driver being used. Hence Valgrind is unable to understand and instrument operations being run on the device. To circumvent this limitation a new set of wrapper functions have been introduced. These wrap a subset of the CUDA Driver API function that is responsible for (de-)allocation memory regions on the device and the respective memory copy operations. This allows to check whether memory is fully allocated during a transfer and, through the functionality provided by Valgrind, whether the memory transfered to the device from the host is defined and addressable. Through this technique it is possible to detect a number of common programming mistakes, which are very difficult to debug by other means. The combination of these wrappers together with the Valgrind tool Memcheck is being called Cudagrind.
Automatic Algorithm Configuration Based on Local Search

The determination of appropriate values for free algorithm parameters is a challenging and tedious task in the design of effective algorithms for hard problems. Such parameters include categorical choices (e.g., neighborhood structure in local search or variable/value ordering heuristics in tree search), as well as numerical parameters (e.g., noise or restart timing). In practice, tuning of these parameters is largely carried out manually by applying rules of thumb and crude heuristics, while more principled approaches are only rarely used. In this paper, we present a local search approach for algorithm configuration and prove its convergence to the globally optimal parameter configuration. Our approach is very versatile: it can, e.g., be used for minimising run-time in decision problems or for maximising solution quality in optimisation problems. It further applies to arbitrary algorithms, including heuristic tree search and local search algorithms, with no limitation on the number of parameters. Experiments in four algorithm configuration scenarios demonstrate that our automatically determined parameter settings always outperform the algorithm defaults, sometimes by several orders of magnitude. Our approach also shows better performance and greater flexibility than the recent CALIBRA system. Our ParamILS code, along with instructions on how to use it for tuning your own algorithms, is available on-line at http://www.cs. ubc.ca/labs/beta/Projects/ParamILS.
DANCE: Distributed application-aware node configuration engine in shared reconfigurable sensor networks

Wireless sensor networks (WSNs) are often primarily tailored to single applications to achieve one specific mission. Considering that the same physical phenomenon can be used by multiple applications, the benefit of sharing the WSN infrastructure is obvious in terms of development and deployment cost. However, allocating the tasks to the WSNs to meet the requirements of all applications while keeping the energy efficiency is very challenging. Introducing reconfigurable nodes in the shared sensor networks can improve the performance, the energy efficiency and the flexibility but it increases the system complexity. In this paper, we propose a biologically inspired node configuration scheme in shared reconfigurable sensor network named DANCE, which can adapt to the changing environment and efficiently utilize WSN resources. Our experiments show that our scheme reduces the energy consumption by up to 76%.