Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION TECHNIQUES FOR ZERO-DIMENSIONAL PRIMARY DECOMPOSITION EXTENDED ABSTRACT

In this talk, we will present an algorithm that computes the local algebra of the roots of a zero-dimensional polynomial equations system, whose cost is polynomial in the number of variables, in the evaluation cost of the equations and in the Bézout number of the input system. Let K be a field of characteristic zero, and let f1, . , fs, g be polynomials in K[x1, . , xn] such that the system f1 = · · · = fs = 0 with g 6= 0 has a finite set of solutions over the algebraic closure K̄ of K. We give an algorithm that computes the roots of the system together with the structure of their multiplicities. More precisely, our purpose is to compute the primary decomposition of the zerodimensional ideal (f1, . . . , fs) : g , where for any ideal J in K[x1, . , xn], J : g ∞ denotes the saturation of J with respect to g, that is, the ideal {f | ∃m ≥ 0, gf ∈ J }. Main Result. For any root p = (p1, . . . , pn) ∈ K̄ n of (f1, . . . , fs) : g , let K̄[[x1− p1, . , xn − pn]] denote the ring of formal series in x1 − p1, . , xn − pn over the algebraic closure K̄ of K. The local algebra of p as a root of (f1, . . . , fs) : g ∞ is the K̄-algebra Dp = K̄[[x1 − p1, . , xn − pn]]/((f1, . . . , fs) : g )p, where for any ideal J in K[x1, . , xn], the notation Jp stands for the ideal J extended to K̄[[x1 − p1, . , xn − pn]]. The multiplicity μp of the root p is the dimension of the K̄-algebra Dp. The matrices of the morphism of multiplication by the variables in some basis of Dp allow all computations in Dp. Here we propose a new algorithm for computing such matrices. The improvement of the exponents involved in the complexity is still in progress. Theorem 1 ([2]). Let K be a field of characteristic zero and let f1, . , fs, g be polynomials in K[x1, . , xn] of degree at most d, given by straight-line programs of size at most L. Let us assume that the system f1 = · · · = fs with g 6= 0 has only a finite set of solutions over the algebraic closure K̄ of K. The roots of the system together with the matrices of multiplication by the variables with respect to a basis of their local algebra can be computed with a number of arithmetic operations in K which is polynomial in n, L and d. We give a probabilistic algorithm performing these computations. Its probability of returning the correct result relies on choices of elements in K. Choices for which the result is not correct are enclosed in strict
Classification of malware using structured control flow

Malware is a pervasive problem in distributed computer and network systems. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in flowgraph based malware classification. Static analysis is widely used for the classification but can be ineffective if malware undergoes a code packing transformation to hide its real content. This paper proposes a novel algorithm for constructing a control flow graph signature using the decompilation technique of structuring. Similarity between structured graphs can be quickly determined using string edit distances. To reverse the code packing transformation, a fast application level emulator is proposed. To demonstrate the effectiveness of the automated unpacking and flowgraph based classification, we implement a complete system and evaluate it using synthetic and real malware. The evaluation shows our system is highly effective in terms of accuracy in revealing all the hidden code, execution time for unpacking, and accuracy in classification.
Intelligent Model Traffic Light for the City of Bogota

This article details the design of a traffic system model for vehicles, which examines the traffic traveling through a series of traffic lights on a main road in Bogota. The Adaptive Network Based Fuzzy Inference System is used to synchronize the time of duration and phase angle of the traffic lights, and also tries to maintain the maximum possible velocity of the vehicles traveling on the road. The model is simulated in Matlab software and the results are evaluated at macroscopic level with the fixed time model currently operating in Bogota Colombia.
Dramatic Changes in the Outburst Properties in V503 Cygni

We examined the VSNET light curve of the unusual SU UMa-type dwarf nova V503 Cyg, which is known to show a short (89 d) supercycle length and exceptionally small (a few) normal outbursts within a supercycle. In 1999–2000, V503 Cyg displayed frequent normal outbursts with typical recurrence times of 7–9d. The behavior during this period is characteristic to an usual SU UMa-type dwarf nova with a short supercycle length. On the other hand, V503 Cyg showed very infrequent normal outbursts in 2001–2002. Some of the superoutbursts during this period were observed to be shorter than usual. The remarkable alternations of the outbursting states in V503 Cyg support the presence of mechanisms of suppressing normal outbursts and prematurely quenching superoutbursts, which have been proposed to explain some unusual SU UMa-type outbursts. The observed temporal variability of the suppressing/quenching mechanisms in the same object suggests that these mechanisms are not primarily governed by a fixed system parameter, but more reflect the state changes in the accretion disk.
Visual tracking in the presence of motion blur

We consider the problem of visual tracking of regions of interest in a sequence of motion blurred images. Traditional methods couple tracking with deblurring in order to correctly account for the effects of motion blur. Such coupling is usually appropriate, but computationally wasteful when visual tracking is the lone objective. Instead of deblurring images, we propose to match regions by blurring them. The matching score for two image regions is governed by a cost function that only involves the region deformation parameters and two motion blur vectors. We present an efficient algorithm to minimize the proposed cost function and demonstrate it on sequences of real blurred images.
Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a 'maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS_GM), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.
The Structuralist Perspective on Real Exchange Rate, Share Price Level and Employment Path: What Room is Left for Money?

The current sluggish performance of the US economy follows one of the more remarkable booms in modern history. The late 1990s was a period of simultaneous output and productivity growth, low unemployment and stable inflation, culminating in an unemployment rate of only 3.9% in the fourth quarter of the year 2000. The absence of rising inflation during this period came as a surprise to many since the level of the natural rate of unemployment was commonly estimated to be in the range of 5-6% by the mid 1990s. The non-inflationary boom, however, reminds one of another episode where non-monetary forces were strongly at work, namely, the non-deflationary slump in Europe and elsewhere in the 1980s and 90s, which appeared to signal a move to a higher natural rate of unemployment. The modeling of such structural slumps and booms is the task that we have tackled in a number of papers in recent years, the book Structural Slumps being a major milestone.
Feature based simultaneous localization and semi-dense mapping with monocular camera

Recently, Simultaneous Localization And Mapping (SLAM) has been getting more and more popular on the applications of mobile robot and unmanned aerial vehicles. If combined with semi-dense mapping, monocular SLAM will have a good prospect for multi-applications such as visual navigation. In addition, robust tracking is a pivotal role in complex and volatile situation and also it determines the quality of whole SLAM system's performance and accuracy. Because feature based tracking method has better error-tolerance on variety of light intensity and dynamic scene, this paper using feature-based tracking method plus depth update and propagation's mechanism achieved a higher-quality tracking and semi-dense mapping in real time. Then the accuracy of tracking trajectory was measured and evaluated in TUM RGB-D benchmark. As a result, the new system can not only rebuild semi-dense 3D scene in real time but also has a higher accuracy.
ects of Multi-core Memory Concurrency Limits on Multi-threaded Applications

Memory access is becoming an increasingly significant impediment to extracting performance out of multi-core systems. More than ever, the effectiveness of memory system use by an application is becoming a critical determinant of performance. In previous work, we demonstrated how explicit consideration of memory concurrency provides a better model for memory performance on multi-socket, multi-core systems than just using best case latency and bandwidth. This paper investigates some of the implications of this on application structure and compiler optimization. We developed a methodology to use hardware performance counters in a performance reflection tool, RCRTool, to measure achieved memory concurrency. We applied this to several important memory-bound scientific applications and kernels compiled with varying levels of optimization. We convolve the observed application concurrency with available system memory concurrency to derive insights for compilers and application tuners. The models provide compilers and runtimes with information about how load on the memory sub-system changes the effectiveness of various optimizations. As the number of hardware cores/threads increases, and as off-chip memory bandwidth per core remains constant or decreases, these measurements and analysis can provide insights to compiler and application writers. For example, on highly-threaded systems the system can be saturated if each software thread offers only 2 or 3 concurrent memory references. The implication is that optimizations to improve cache usage are more important than ever, while program transformations designed to increase memory concurrency may lose their utility.
Using social network analysis for software project management

Software projects are complex endeavors where the application of processes, knowledge, skills and tools is necessary, in order to increase the probability of successful completion. Traditionally software projects are analyzed controlled and monitored by controlling project scope, time, cost and quality. In this paper we present how social network analysis can be used in order to improve software project control. The presented model is based on meta-networks where basic project entities are combined for representing communication, collaboration, and knowledge. The analysis of the model is illustrated with sample data extracted from three software projects.
Link Quality and MAC-Overhead aware Predictive Preemptive Routing Protocol for Mobile Ad hoc Network

In Ad Hoc networks, route failure may occur due to less received power, mobility, congestion and node failures. Many approaches have been proposed in literature to solve this problem, where a node predicts pre-emptively the route failure that occurs with the less received power. However, this approach encounters some difficulties, especially in scenario without mobility where route failures may arise. In this paper, we propose an improvement of AODV protocol called LO-PPAODV (Link Quality and MAC-Overhead aware Predictive Preemptive AODV). This protocol is based on new metric combine more routing metrics (Link Quality, MAC Overhead) between each node and one hop neighbor. Also we propose a cross-layer networking mechanism to distinguish between both situations, failures due to congestion or mobility, and consequently avoiding unnecessary route repair process. The LO-PPAODV was implemented using NS-2. The simulation results show that our approach improves the overall performance of the network. It reduces the average end to end delay, the routing overhead, MAC errors and route errors, and increases the packet delivery fraction of the network.
Comparative analysis of two configurations of aerial robot

The aim of the research in this paper is to define the aerial robot (Cable-suspended Parallel Robot-CPR). CPR is hanged on four points and it is able to follow and record a moving object with high precision wherever the object moves in its workspace. Each contribution is a step closer to the achievement of highly automated systems which would precisely lead camera in the area with as less involvement of human interference as possible. Two new CPR structures are proposed. The mathematical model (kinematic and dynamic) is defined for both versions of the solution. It is assumed that all the parameters of systems are the same and therefore the comparison of these systems will be easier. It is evident that the choice of construction of CPR significantly affects the response of this system.
Characterizing webpage load from the perspective of TCP connections

Over the last years websites have evolved rapidly incorporating new content types and becoming more and more dynamic. Users today are able to access a wide variety of content and services through their web browsers. As a consequence, web traffic has become increasingly complex and, from a network perspective it can be difficult to ascertain which websites are being visited by a user, let alone which part of the user's traffic each of them is responsible for.
Idle Times Analysis in a CSMA/CA Network

Carrier Sense Multiple Access with Collision avoidance (CSMA/CA) is a well known and distributed Medium Access Control (MAC) protocol that enables wireless stations to communicate with one another using a single medium. In this paper an analysis of the times between data transmission in this protocol has been performed and results obtained. Whilst neither analytically nor numerically complex these results lead to an insight of the behavior of a CSMA/CA system that enable a robust and efficient traffic analysis of these systems.
A sliding-mode based guidance law for intercepting missile with passive ranging law

This paper proposes the development and implementation of the sliding-mode based guidance law with a passive 3-D ranging law by using angle-only measurements (e.g. IR image sensor data). The passive ranging law applies the Modified Spherical Coordinates (MSC) based method and the well-known Extended Kalman filter technique to conduct further analysis for the tracking. In addition, The Interacting Multiple Model (IMM) technique coupled with multiple sub-filters of ballistic missile motion is proposed in order to minimize the estimation errors of the range and range rates incurred by the target's maneuvers. Considering the estimation errors, the guidance law is designed applying sliding-mode technique in order to deal with the non- maneuvering target and the maneuvering target with imperfect information. Various simulation results can verify the reliability and stability of our designed system.
Cellular Localization of the Herpes Simplex Virus ICP0 Protein Dictates Its Ability to Block IRF3-Mediated Innate Immune Responses

Interferon regulatory factor 3 (IRF3) is important for innate antiviral responses; accordingly, many viruses target and inactivate IRF3. The ability of the Herpes simplex virus type 1 (HSV-1) immediate early protein ICP0 to inhibit IRF3 is controversial and has not been studied solely in the context of a wild type HSV-1 infection. Discrepancies in the literature surround the mechanism by which ICP0 antagonizes the IRF3 pathway, the cellular localization of ICP0 inhibitory activity and the ability of ICP0 to interfere with interferon and interferon-stimulated gene induction. In this study, we set out to investigate the role of ICP0 localization and the requirement of the proteasome during the inhibition of IRF3 activation within the context of an HSV-1 infection. Collectively, the results presented herein demonstrate that incoming wild type HSV-1 activates IRF3 and that de novo produced ICP0 prevents sustained IRF3 activation following its translocation from the nucleus to the cytoplasm. While previous studies implicate the E3 ubiquitin ligase domain of ICP0 in mediating its biological functions, including the inhibition of IRF3, we show that cytoplasmic ICP0 does not require the proteasome for this activity. Instead, proteasome function is required to localize ICP0 to the cytoplasm where it mediates its inhibitory effect independent of E3 ubiquitin ligase activity. The importance of these findings is discussed within the context of an HSV-1 infection.
Chronic optical access through a polished and reinforced thinned skull

The Pennsylvania State University, University Park, Pennsylvania, USA. 3Department of Neurosurgery, The Pennsylvania State University, University Park, Pennsylvania, USA. 4Gladstone Institute of Neurological Disease, University of California, San Francisco, San Francisco, California, USA. 5Department of Neurology, University of California, San Francisco, San Francisco, California, USA. 6Graduate Program in Neurosciences, University of California at San Diego, San Diego, California, USA. 7Center for Neural Circuits and Behavior, University of California at San Diego, San Diego, California, USA. Correspondence should be addressed to D.K. (dk@physics.ucsd.edu).
QbS: searching for known images using user-drawn sketches

With the increasingly growing size of digital image collections, known image search is gaining more and more importance. Especially when the objects in such collections do not possess appropriate metadata (e.g., tags, annotations), content-based image retrieval (CBIR) is a promising approach. However, the application of CBIR to known item search usually suffers from the unavailability of query images that are good enough to express the user's information need. In order to improve this situation, we propose the QbS system which provides an approach to content-based search in large image collections based on user-drawn sketches. By exploiting novel devices for human-computer interaction like interactive paper, tablet PCs, or graphic tablets, users are able to draw a sketch that reflects their information need and start a content-based search using this sketch. The QbS system provides query support and offers several invariances that allow the user-generated sketch to slightly deviate from the searched image in terms of rotation, translation, relative size, and/or unknown objects in the background. To illustrate the benefits of the approach, we show search results from the evaluation of QbS on the basis of the MIRFLICKR collection with 25'000 objects.
Chaotic Time-series Prediction Using Resource-allocating Rbf Networks

One of the main problems associated with artiicial neural networks on-line learning methods is the estimation of model order. In this paper, we report about a new approach to constructing a resource-allocating network exploiting weights adaptation using QRD-based recursive least-squares technique. Further, we studied the performance of Dynamic Cell Structures algorithm for on-line adaptation of centers positions. The proposed method was tested on the task of Mackey-Glass time-series prediction. Order of resulting networks and their prediction abilities were superior to those previously reported by Platt 6].
High-Performance Distributed Indexing and Retrieval for Large Volume Traffic Log Datasets on the Cloud

In this paper, we present a high-performance distributed system for storage, indexing and retrieval for large volume web traffic log datasets. This system is Based on the open source Map Reduce framework Hadoop and extends the functionality of Hadoop. We mainly focus on three noteworthy aspects of our work: the approach of large datasets storage on the Hadoop Distributed File System (HDFS), the appropriate indexing algorithm for large distributed datasets, a distributed retrieval architecture built on Hadoop. It has been proved that our system is efficient and the query response latency approach real time compared with HBase, a distributed, sparse, NoSQL database.
Neural shrinkage for wavelet-based SAR despeckling

The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new type of Neural Shrinkage (NS) is presented with a new class of shrinkage architecture for speckle reduction in Synthetic Aperture Radar (SAR) images. The numerical results indicate that the new method outperforms the standard filters, the standard wavelet shrinkage despeckling method, and previous NS.
BEHAVIOR-DRIVEN DESIGN PATTERN RECOVERY

In this paper, we present an approach for enhancing program understanding and reusability through a behaviordriven design pattern recovery process. In this context, incorporating behavioral features would characterize the approach as a goal-driven and scalable pattern recovery process. The proposed technique consists of a featureoriented dynamic analysis and a two-phase design pattern detection process. The dynamic analysis operates on the system’s scenario-driven execution traces and produces a mapping between features and their implementation at class level. For the two-phase design pattern detection, we employ approximate matching and structural matching algorithms to identify the instances of the target design pattern that is described using our proposed Pattern Description Language (PDL). The correspondence between system features and identified design pattern instances can facilitate the construction of more reusable and configurable software components to be maintained. We have implemented an Eclipse plug-in toolkit and have conducted experimentation on three versions of JHotDraw systems to evaluate our approach.
Scalable Symbolic Execution For Verification

In previous work, we presented a symbolic execution method which starts with a concrete model of the program but progressively abstrac ts away details only when these are known to be irrelevant using interpolation. In this pa per, we extend the technique to handle unbounded loops. The central idea is to p rogressively discover the strongest invariants through a process of loo p unr lling. The key feature of this technique, called the minimax algorithm, is intelligent backtrackingwhich directs the search for the next invariant. We then present an analysis of the main differences between our symbolic execution method a nd mainstream techniques mainly based on abstract refinement ( CEGAR). Finally, we evaluate our technique against available state-of-the-art systems.