Miracle-dz/UniFAR-FTUs huggingface dataset:
Oligogalacturonides: plant damage-associated molecular patterns and regulators of growth and development

Oligogalacturonides (OGs) are oligomers of alpha-1,4-linked galacturonosyl residues released from plant cell walls upon partial degradation of homogalacturonan. OGs are able to elicit defense responses, including accumulation of reactive oxygen species and pathogenesis-related proteins, and protect plants against pathogen infections. Recent studies demonstrated that OGs are perceived by wall-associated kinases and share signaling components with microbe-associated molecular patterns. For this reason OGs are now considered true damage-associated molecular patterns that activate the plant innate immunity and may also be involved in the activation of responses to mechanical wounding. Furthermore, OGs appear to modulate developmental processes, likely through their ability to antagonize auxin responses. Here we review our current knowledge on the role and mode of action of this class of oligosaccharides in plant defense and development.
AWtoolbox: Characterizing Audio Information Using Audio Words

This paper presents the AWtoolbox, an open-source software designed for extracting the audio word (AW) representation of audio signals. The toolbox comes with a graphical user interface that helps a user design custom AW extraction pipelines and various algorithms for feature encoding, dictionary learning, result rectification, pooling, normalization and others. This paper also reports a benchmark comparing eight AW representations computed by the toolbox against state-of-the-art low-level and mid-level timbre, rhythmic and tonal descriptors of music and sound. The evaluation result shows that sparse coding (SC) based AW representation leads to very competitive performances across the three tested sound and music classification tasks. AWtoolbox is available for download at http://mac.citi. sinica.edu. tw/awtoolbox.
Improving Rooftop Detection in Aerial Images Through Machine Learning

Abstract : In this paper, we examine the use of machine learning to improve a rooftop detection process, which is one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing vision system that automates the recognition of buildings in such images. After this, we briefly review two well known learning algorithms, representing different inductive biases, that we selected to improve rooftop detection. An important aspect of this problem is that the data sets are highly skewed and the cost of mistakes differs for the two classes, so we evaluate the algorithms under varying misclassification costs using ROC analysis. We report three sets of experiments designed to illuminate facets of applying machine learning to the image analysis task. One set of studies focuses on within image learning, in which both training and testing data are derived from the same image. Another addresses between image learning, in which training and testing sets come from different images. A final set investigates learning using all available images in an effort to determine the best performing method. Experimental results demonstrate that useful generalization occurs when training and testing on data derived from images that differ in location and in aspect. Furthermore, they demonstrate that, under most conditions and across a range of misclassification costs, a trained naive Bayesian classifier exceeded, by as much as a factor of two, the predictive accuracy of nearest neighbor and a handcrafted linear classifier, the solution currently used in the building detection system. Analysis of learning curves reveals that naive Bayes achieved superiority using as little as 6% of the available training data.
Research Paper: Cross-Topic Learning for Work Prioritization in Systematic Review Creation and Update

OBJECTIVE Machine learning systems can be an aid to experts performing systematic reviews (SRs) by automatically ranking journal articles for work-prioritization. This work investigates whether a topic-specific automated document ranking system for SRs can be improved using a hybrid approach, combining topic-specific training data with data from other SR topics. DESIGN A test collection was built using annotated reference files from 24 systematic drug class reviews. A support vector machine learning algorithm was evaluated with cross-validation, using seven different fractions of topic-specific training data in combination with samples from the other 23 topics. This approach was compared to both a baseline system, which used only topic-specific training data, and to a system using only the nontopic data sampled from the remaining topics. MEASUREMENTS Mean area under the receiver-operating curve (AUC) was used as the measure of comparison. RESULTS On average, the hybrid system improved mean AUC over the baseline system by 20%, when topic-specific training data were scarce. The system performed significantly better than the baseline system at all levels of topic-specific training data. In addition, the system performed better than the nontopic system at all but the two smallest fractions of topic specific training data, and no worse than the nontopic system with these smallest amounts of topic specific training data. CONCLUSIONS Automated literature prioritization could be helpful in assisting experts to organize their time when performing systematic reviews. Future work will focus on extending the algorithm to use additional sources of topic-specific data, and on embedding the algorithm in an interactive system available to systematic reviewers during the literature review process.
Using the Inter- and Intra-Switch Regularity in NoC Switch Testing

This paper proposes an efficient test methodology to test switches in a network-on-chip (NoC) architecture. A switch in a NoC consists of a number of ports and a router. Using the intra-switch regularity among ports of a switch and inter-switch regularity among routers of switches, the proposed method decreases the test application time and test data volume of NoC testing. Using a test source to generate test vectors and scan-based testing, this methodology broadcasts test vectors through the minimum spanning tree of the NoC and concurrently tests its switches. In addition, a possible fault is detected by comparing test results using inter- or intra- switch comparisons. The logic and memory parts of a switch are tested by appropriate memory and logic testing methods. Experimental results show less test application time and test power consumption, as compared with other methods in the literature
Structure of the Cerebral Cortex in Men and Women

Expanding previous studies of human cerebral cortical sexual dimorphism showing higher neuronal densities in males, we investigated whether gender differences also exist in the extent of neuropil, size of neuronal somata, and volumes of astrocytes. This histo-morphometric study includes select autopsy brains of 6 males and 5 females, 12 to 24 yr old. In each brain, 86 defined loci were analyzed for cortical thickness, neuronal and astrocytic (8 loci) density (stereological counts), and neuronal and astrocytic (8 loci) soma size, enabling calculations of neuropil and astrocytic volumes. The female group showed significantly larger neuropil volumes than males, whereas neuronal soma size and astrocytic volumes did not differ. The expanded data confirmed higher neuronal densities in males than in females without a gender difference in cortical thickness. These findings indicate that fundamental gender differences exist in the structure of the human cerebral cortex, with more numerous, smaller neuronal units in men and fewer, larger ones in women; they may underlie gender-specific abilities and susceptibilities to disease affecting the neocortex. Laterality differences between the sexes were restricted to neuronal soma size showing significantly larger values in the female group in the left hemisphere. This gender difference may support female's right-handedness, language advantage, and tendency for bilateral activation patterns.
Expressive interaction for conceptual design of shape-An empirical study on methods and their effectiveness

This paper describes a study of human interaction methods to communicate shape ideas. The aim of the study is to get insight in the way people exteriorize shape if they are not bound to the limitations of certain CAD systems. The study is started because current CAD systems generally are not able to support conceptual design in a convenient way. Instead of forcing the designer to speak the language of CAD systems, our goal is to build a system for Computer Aided Conceptual Design (CACD) that is able to interpret a way of interaction that connects closer to the methods designers normally use to express shape. We launched an empirical study to find out how people exteriorize shape, and how effective they do this. The goals of the study are (1) to explore the knowledge that is needed to implement a more effective user interface; (2) to offer opportunities for the designers to express shape concepts in a more natural way; and (3) to find a link between the thinking units of the designer and the modeling units of the CACD system. The gained insights will be used in a later phase to improve humancomputer interaction for shape conceptualization.
Obstacle detection over rails using hough transform

Autonomous systems can assist humans in the important task of safe driving. Such systems can warn people about possible risks, take actions to avoid accidents or guide the vehicle without human supervision. Whether in cars or trains or ships the artificial vision algorithms offer an alternative for the design and implementation of autonomous driving systems. In railway scenarios cameras in front of the train can assist drivers with the identification of obstacles or strange objects on the rails. Multiple factors add huge complexity to this task. The changing conditions create a scene where background is hard to detect, lighting varies and process speed must be fast. This article describes a first approximation to the problem where using the Hough transform, the rails and area of interest are detected. On this area a systematic search is done for finding and delimiting possible obstacles. Our system accomplished a real time performance when employed in the analysis of recorded videos from the driver perspective. Using digital added obstacles our algorithm detects mostly all of them and warns if the objects over the rail can create a danger to the safety travel of the train.
A comparative analysis of Secret Sharing Schemes with special reference to e-commerce applications

The term cryptography which means “secret writing” has become the foundation primitive in providing security for many communication applications. In many applications, particularly in Group Communication, there is a need to hide secret data like passwords, encryption key, recipes etc. Here, efficient key management protocols are required to provide security for group's secret data, because it is very challenging to provide security especially when group of more members and those members are present in different locations with diverse mechanisms of protection. One among them is Secret Sharing Schemes (SSS) which allow to split and share the group's secret among group members. The secret can be retrived once, when some threshold member combines their share value in a defined way. Across the years, several schemes on Secret Sharing have been presented. In this view, this paper mainly focuses on three different Secret Sharing schemes, such as Shamir's Secret Sharing based on Polynomial Interpolation, Asmuth-Bloom Secret Sharing Scheme based on Chinese Remainder Theorem and Verifiable Secret Sharing Scheme based on Elliptic Curves. All these three schemes have been studied, algorithms are programmed using MATLAB 7.0 tool and executed. It has been observed that all the three schemes works satisfactorily according to the properties defined by the inventors of the schemes.
Distributed Trust Based Access Control Architecture for Pervasive Computing

As the demands of the handheld devices and pervasive services are increasing, the issues of security have alarmingly risen. One can wonder about the pervasive services to be free of every security flaw. The paper suggests a mechanism for a trust based ad-hoc authentication with a distributed trust based mechanism and defined security policies related to access control for pervasive devices. The openness of a true pervasive environment has been encountered with the interaction history and recommendations. The architecture is designed to provide secure autonomous services for an assumed pervasive based scenario. The nature of pervasiveness is satisfied by allowing the novel user to proceed in the system but with the minimum trust initially, which changes dynamically depending on the actions which the user performs. It secures the privacy of the user by the implementation of security policies in the Security Manger(SM) in order to gain final access to the service.
Investigation of Haptic Feedback in the Driver Seat

We explore the impact of tilting the driver's seat according to the relative distance and velocity to objects outside the car using a haptic feedback chair in a driving simulator. We found that drivers perform best when (1) the seat tilts according to relative distance (vs. velocity) to objects outside the car and (2) the seat tilts forward (vs. backward) when the driver gets closer to a car in front of them. We also found that when visually and cognitively distracted, drivers perform better using haptic feedback than without. Our results suggest that adding haptic feedback to the car seat may improve driving safety and enjoyment by enhancing the driving experience
The point of view notion for multiple inheritance

We examine several problems related to the preservation of the Independence Principle inheritance. This principle states that all the characteristics of independent superclasses must be inherited by subclasses, even if there are name conflicts. In this context, a conventional approach is to use explicit class selection. We show that this mechanism suffers from serious limitations, and leads to inhibition of refinement and genericity. Our experimental object-oriented language ROME introduces the “Point of View” notion (using an “as-expressions” mechanism) which solves these problems.
Real Convergence and the Determinants of Growth in EU Candidate and Potential Candidate Countries - A Panel Data Approach

The EU candidate and potential candidate countries have made considerable progress in economic transition and integration into the world economy within less than two decades. Nevertheless, gaps in terms of income per capita relative to the euro area remain large. This suggests that the challenges of real convergence will remain relevant for the region even in the medium and long term. This paper therefore focuses on real convergence and its determinants in the candidate and potential candidate countries. The analysis reveals that total factor productivity growth has been the main driver of convergence, followed by capital deepening, whereas labour has contributed only marginally to economic growth. There is evidence of conditional convergence in the transition countries of central, eastern and south-eastern Europe. More specifi cally, controlling for the quality of institutions, the extent of market reforms and macroeconomic policies, there is a significant and negative link between the initial level of GDP and subsequent growth. Labour productivity has improved in most countries, while employment and participation rates have been falling. Structural changes have resulted in, at least temporarily, increasing labour market mismatches. Investment rates have been rising rapidly in recent years, and foreign direct investment has been found to have a positive impact on total investment. Investment in human capital is still at a relatively low level compared with the euro area average. Thus, in order to sustain the positive developments observed in the past, further improvements are needed in terms of labour productivity and utilisation, as well as in terms of physical and human capital accumulation. JEL Classification: F15, F43, O16, O43, O47, O52.
Force mapping

In this paper, a new technique, force mapping, is developed for the representation of surfaces at different levels of detail. A force map records the applied forces and their distributions. By applying these forces on a relatively simple surface and deforming it, a detailed surface is created. This technique can be used for surface compression, reconstruction and editing. As an alternative to displacement mapping, our method benefits from the fact that physical properties are integrated into a geometric surface explicitly
The conformal deformation detour complex for the obstruction tensor

On pseudo-Riemannian manifolds of even dimension n > 4, with everywhere vanishing (Fefferman-Graham) obstruction tensor, we construct a complex of conformally invariant differential operators. The complex controls the infinitesimal deformations of obstruction-flat structures, and, in the case of Riemannian signature the complex is elliptic.
On the criteria for prioritizing code anomalies to identify architectural problems

Architectural problems constantly affect evolving software projects. When not properly addressed, those problems can hinder the longevity of a software system. Some studies revealed that a wide range of architectural problems are reflected in source code through code anomalies. However, a software project often contains thousands of code anomalies and many of them have no relation to architectural problems. As a consequence, developers struggle to effectively determine which (groups of) anomalies are architecturally relevant. This work proposes criteria for prioritizing groups of code anomalies as indicators of architectural problems in evolving systems.
Kernel Fusion: An Effective Method for Better Power Efficiency on Multithreaded GPU

As one of the most popular accelerators, Graphics Processing Unit (GPU) has demonstrated high computing power in several application fields. On the other hand, GPU also produces high power consumption and has been one of the most largest power consumers in desktop and supercomputer systems. However, software power optimization method targeted for GPU has not been well studied. In this work, we propose kernel fusion method to reduce energy consumption and improve power efficiency on GPU architecture. Through fusing two or more independent kernels, kernel fusion method achieves higher utilization and much more balanced demand for hardware resources, which provides much more potential for power optimization, such as dynamic voltage and frequency scaling (DVFS). Basing on the CUDA programming model, this paper also gives several different fusion methods targeted for different situations. In order to make judicious fusion strategy, we deduce the process of fusing multiple independent kernels as a dynamic programming problem, which could be well solved with many existing tools and be simply embedded into compiler or runtime system. To reduce the overhead introduced by kernel fusion, we also propose effective method to reduce the usage of shared memory and coordinate the thread space of the kernels to be fused. Detailed experimental evaluation validates that the proposed kernel fusion method could reduce energy consumption without performance loss for several typical kernels.
Trilemmas and Trade-Offs: Living with Financial Globalisation

This paper evaluates the capacity of emerging market economies (EMEs) to moderate the domestic impact of global financial and monetary forces through their own monetary policies. Those EMEs that are able to exploit a flexible exchange rate are far better positioned than those that devote monetary policy to fixing the rate - a reflection of the classical monetary policy trilemma. However, exchange rate changes alone do not insulate economies from foreign financial and monetary shocks. While potentially a potent source of economic benefits, financial globalisation does have a downside for economic management. It worsens the trade-offs monetary policy faces in navigating among multiple domestic objectives. This drawback of globalisation raises the marginal value of additional tools of macroeconomic and financial policy. Unfortunately, the availability of such tools is constrained by a financial policy trilemma that is distinct from the monetary trilemma. This second trilemma posits the incompatibility of national responsibility for financial policy, international financial integration and financial stability.
Theory of Pixel Lensing toward M31. II. The Velocity Anisotropy and Flattening of the MACHO Distribution

The POINT-AGAPE collaboration is currently searching for massive compact halo objects (MACHOs) toward the Andromeda galaxy (M31). The survey aims to exploit the high inclination of the M31 disk, which causes an asymmetry in the spatial distribution of M31 MACHOs. Here, we investigate the effects of halo velocity anisotropy and flattening on the asymmetry signal using simple halo models. For a spherically symmetric and isotropic halo, we find that the underlying pixel lensing rate in far-disk M31 MACHOs is more than 5 times the rate of near-disk events. We find that the asymmetry is further increased by about 30% if the MACHOs occupy radial orbits rather than tangential orbits, but it is substantially reduced if the MACHOs lie in a flattened halo. However, even for halos with a minor- to major-axis ratio of q = 0.3, the number of M31 MACHOs in the far side outnumber those in the near side by a factor of ~2. There is also a distance asymmetry, in that the events on the far side are typically farther from the major axis. We show that, if this positional information is exploited in addition to number counts, then the number of candidate events required to confirm asymmetry for a range of flattened and anisotropic halo models is achievable, even with significant contamination by variable stars and foreground microlensing events. For pixel lensing surveys that probe a representative portion of the M31 disk, a sample of around 50 candidates is likely to be sufficient to detect asymmetry within spherical halos, even if half the sample is contaminated, or to detect asymmetry in halos as flat as q = 0.3, provided less than a third of the sample comprises contaminants. We also argue that, provided its mass-to-light ratio is less than 100, the recently observed stellar stream around M31 is not problematic for the detection of asymmetry.