Miracle-dz/UniFAR-FTUs huggingface dataset:
Capital Market Campaigning: The Impact of NGOs on Companies, Shareholder Value and Reputational Risk

1. Setting the Scene Introduction Overview of NGO CM campaigns Scope, approach and objectives Structure of the book 2. Background What are NGOs and why do they exist? Why do NGOs target companies? A taxonomy of different campaign behaviours The capital market as a campaign target Is NGO capital-market campaigning legitimate Chapter summary 3. NGO Capital Market Campaign Strategy Introduction A typology of capital-market campaigning The source of Economic Influence How NGOs shape Economic Influence The source of Investor Advocacy Influence How NGOs shape Investor Advocacy Influence Which capital market campaign strategies raise the most significant risks? Checks and balances on capital-market campaigning Chapter summary 4. The History of Capital-market campaigns Introduction Have the number of capital-market campaigns increased? Have NGOs explored a range of strategies? Has NGO strategy evolved over time? Does the relative success improve over time? Initial lessons for companies and investors Chapter summary 5. Case Studies Introduction The Ethics for the Universities Superannuation Scheme Campaign WWF-UK's Arctic Refuge Campaign against BP plc 'Capital Punishment': Friends of the Earth's campaign against Norwich Union "Cut the Cost": Oxfam's campaign against GlaxoSmithKline plc 6. Analysis and Questions Introduction Is NGO capital-market campaigning effective and efficient? Other findings Lessons for companies and investors 7. Conclusion Appendix 1: The NGO CM Intervention Chronology Appendix 2: The Collevecchio Declaration Appendix 3: The "Equator Principles"
A Tutorial on the MILOS Multimedia Content Management System

In this paper we present the MILOS Multimedia Content Management System. MILOS supports the storage and content based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in XML. It provides developers of digital library applications with functionalities for dealing with heterogeneous digital documents, heterogeneous metadata, and metadata schema mapping. This paper shows how to configure and use all MILOS components.
Gly482Ser PGC-1α Gene Polymorphism and Exercise-Related Oxidative Stress in Amyotrophic Lateral Sclerosis Patients

The role of exercise in Amyotrophic lateral sclerosis (ALS) pathogenesis is controversial and unclear. Exercise induces a pleiotropic adaptive response in skeletal muscle, largely through the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a transcriptional coactivator that regulates mitochondrial biogenesis and antioxidant defense mechanisms. It has been suggested that a Gly482Ser substitution in PGC-1α has functional relevance in human disorders and in athletic performance. To test this hypothesis, we examined the genotype distribution of PGC-1α Gly482Ser (1444 G > A) in ALS patients to evaluate whether or not the minor serine-encoding allele 482Ser is involved in oxidative stress responses during physical exercise. We genotyped 197 sporadic ALS patients and 197 healthy controls in order to detect differences in allelic frequencies and genotype distribution between the two groups. A total of 74 ALS patients and 65 controls were then comparatively assessed for plasmatic levels of the oxidative stress biomarkers, advanced oxidation protein products, ferric reducing ability and thiol groups. In addition a subgroup of 35 ALS patients were also assessed for total SOD and catalase plasmatic activity. Finally in 28 ALS patients we evaluated the plasmatic curve of the oxidative stress biomarkers and lactate during an incremental exercise test. No significant differences were observed in the genotype distribution and allelic frequency in ALS patients compared to the controls. We found significant increased advanced oxidation protein products (p < 0.001) and significant decreased ferric reducing ability (p < 0.001) and thiol groups (p < 0.001) in ALS patients compared to controls. When comparing different genotypes of PGC-1α, no relation between Gly482Ser polymorphism and oxidative stress biomarker levels was detected in resting conditions. On the other hand, when considering exercise performance, lactate levels were significantly higher (between p < 0.01 and p < 0.001) and greater protein oxidative products were found in AA (Ser482Ser) compared to GG (Gly482Gly) and GA (Gly482Ser) ALS patients. Our findings highlight the importance and confirm the involvement of oxidative stress in ALS pathogenesis. Although not associated with 1444 G > A SNP, ALS patients with Gly482Ser allelic variant show increased exercise-related oxidative stress. This thus highlights the possible role of this antioxidant defense transcriptional coactivator in ALS.
A Fixed-Tuned SIS Mixer with Ultra-Wide-Band IF and Quantum-Limited Sensitivity for ALMA Band 3 (84-116 GHz) Receivers

This paper describes an 84-116 GHz Nb/Al-oxide/Nb SIS mixer with multi-octave IF bandwidth and quantum-limited sensitivity. The mixer can be integrated with a three-stage 4-12 GHz HFET preamplifier or fitted with a coaxial connector for operation with an external isolator and IF amplifier. An 84-116 GHz receiver using the mixer with an integrated 4-12 GHz HFET preamplifier has a double sideband noise temperature of 16-20 K (3 to 4 h</kB) over most of the IF band. Analysis of the mixer-preamp using Tucker=s theory shows good agreement with measurements. The mixer is easy to build and is being used as a building block for the Band 3 sideband-separating receivers for the Atacama Large Millimeter Array (ALMA) ** . With minor modifications, this mixer design can be scaled to other ALMA bands. I INTRODUCTION
De-Regulating Markets for Financial Information

In October 2009, the house financial services committee voted to study the effects of removing ratings requirements for credit products. Eliminating such requirements would allow the issuers of credit products to decide whether or not to pay a ratings agency to rate their asset. If such a rating was not provided by the asset issuer, investors themselves might purchase a rating. This paper studies the circumstances under which free markets for information will provide information, in the absence of government mandates and the efficiency properties of each regime. Although government regulation requires too much information for some assets and too little for others, private markets also suffer from inefficiencies stemming from the non-concave nature of information production. The results inform the debate about how and when to require information provision for a wide range of financial and non-financial products.
Wavelet-Based Islanding Detection in Grid-Connected PV Systems

Distributed power generation systems (DPGSs) based on inverters require reliable islanding detection algorithms (passive or active) in order to determine the electrical grid status and operate the grid-connected inverter properly. These methods are based on the analysis of the DPGS voltage, current, and power in time or frequency domain. This paper proposes a time-frequency detection algorithm based on monitoring the DPGS output power considering the influence of the pulsewidth modulation, the output LCL filter, and the employed current controller. Wavelet analysis is applied to obtain time localization of the islanding condition. Simulation and experimental results show the performance of the proposed detection algorithm in comparison with existing methods.
YourSQL: A High-Performance Database System Leveraging In-Storage Computing

This paper presents YourSQL, a database system that accelerates data-intensive queries with the help of additional in-storage computing capabilities. YourSQL realizes very early filtering of data by offloading data scanning of a query to user-programmable solid-state drives. We implement our system on a recent branch of MariaDB (a variant of MySQL). In order to quantify the performance gains of YourSQL, we evaluate SQL queries with varying complexities. Our result shows that YourSQL reduces the execution time of the whole TPC-H queries by 3.6×, compared to a vanilla system. Moreover, the average speed-up of the five TPC-H queries with the largest performance gains reaches over 15×. Thanks to this significant reduction of execution time, we observe sizable energy savings. Our study demonstrates that the YourSQL approach, combining the power of early filtering with end-to-end datapath optimization, can accelerate large-scale analytic queries with lower energy consumption.
Non-simple purely infinite C*-algebras

A C -algebra A is defined to be purely infiniteif there are no characters on A ,a nd if for every pair of positive elements a, b in A, such that b lies in the closed two-sided ideal generated by a, there exists a sequence rn in A such that rn arn b. This definition agrees with the usual definition by J. Cuntz when A is simple. It is shown that the property of being purely infinite is preserved under extensions, Morita equivalence, inductive limits, and it passes to quotients, and to hereditary sub-C -algebras. It is shown that A is purely infinite for every C -algebra A. Purely infinite C -algebras admit no traces, and, conversely, an approximately divisible exact C -algebra is purely infinite if it admits no nonzero trace.
Stokes-Dirac structures through reduction of infinite-dimensional Dirac structures

We consider the concept of Stokes-Dirac structures in boundary control theory proposed by van der Schaft and Maschke. We introduce Poisson reduction in this context and show how Stokes-Dirac structures can be derived through symmetry reduction from a canonical Dirac structure on the unreduced phase space. In this way, we recover not only the standard structure matrix of Stokes-Dirac structures, but also the typical non-canonical advection terms in (for instance) the Euler equation.
An Evolutionary Emergency Model of Home Network Environment

In this paper, we propose an evolutionary emergency model of home network environment (EEMHNE). This model not only can do self-tuning to fit the home network environment but also detect the emergency event automatically. There are three modules in this model, namely, emergency process module (EPM), emergency knowledge base (EKB), and knowledge base evolution module (KBEM). EKB includes three components and can provide rules to EPM for fuzzy inferences. EPM determines the emergency situations by fuzzy inferences and send warning messages to the users. KBEM can adjust all components in EKB by using genetic algorithm. Via this model, we can have the more reliable and safer home network environment
State Monitor for Lithium-ion Power Battery Pack

Management technology for the lithium-ion traction batteries is the key research point for electric vehicles (EV). The states of temperature, current, single voltage, state of charge (SOC) and state of energy (SOE) should be acquired and evaluated timely. This paper has designed a battery state monitor system for the EV batteries based on LabView. Combined with the Extended Kalman Filter (EKF) of the optimal estimation theory and system parameter identify method, evaluate the SOC and SOE for lithium-ion batteries. The experiment results indicate that the EKF method is suitable for estimating the states of batteries, the virtual instrument technology could provide multiple message of the batteries.
Product Trees for Gaussian Process Covariance in Sublinear Time

Gaussian process (GP) regression is a powerful technique for nonparametric regression; unfortunately, calculating the predictive variance in a standard GP model requires time O(n2) in the size of the training set. This is cost prohibitive when GP likelihood calculations must be done in the inner loop of the inference procedure for a larger model (e.g., MCMC). Previous work by Shen et al. (2006) used a k-d tree structure to approximate the predictive mean in certain GP models. We extend this approach to achieve efficient approximation of the predictive covariance using a tree clustering on pairs of training points. We show empirically that this significantly increases performance at minimal cost in accuracy. Additionally, we apply our method to "primal/dual" models having both parametric and nonparametric components and show that this enables efficient computations even while modeling longer-scale variation.
Development of a robot quizmaster with auditory functions for speech-based multiparty interaction

This paper presents a robot quizmaster that has auditory functions (i.e., ears) for moderating a multiplayer quiz game. The most basic form of oral interaction in a quiz game is that a quizmaster reads aloud a question, and each player is allowed to answer it whenever the answer comes to his or her mind. A critical problem in such oral interaction is that if multiple players speak almost simultaneously for answering, it is difficult for a “human” quizmaster to recognize overlapping answers and judge the correctness of each answer. To avoid this problem, players have conventionally been required to push a button, raise a hand, or say “Yes” to just get a right to answer a question before doing it. This requirement, however, inhibits natural oral interaction. In this paper we propose a “robot” quizmaster that can identify a player who correctly answers a question first, even when multiple players utter answers almost at the same time. Since our robot uses its own microphones (ears) embedded in the head, individual players are not required to wear small pin microphones close to their mouths. To localize, separate, and recognize overlapping utterances captured by the ears, we use a robot audition software called HARK and an automatic speech recognizer called Julius. Experimental results showed the effectiveness of our approach.
MPI-PERF-SIM: Towards an Automatic Performance Prediction Tool of MPI Programs on Hierarchical Clusters

We present in this paper a framework for performance prediction of parallel programs on hierarchical clusters. This framework is mainly designed for the use by the switching functions in parallel adaptive applications. Indeed, the principal referred objectives by this framework are the accuracy of the prediction and the rapidity of the prediction process. To achieve these objectives, our framework is based on two principal steps, the first is at the installation moment of the parallel application, and the second is at runtime. In the first step, we profile two components which are sequential kernels of the program and network performances. In order to model accurately these two components we have developed new strategies of regression. In the second step, we use the generated models and the runtime variables to the completion time estimation via our fast simulator MPI-PERF-SIM. Our experimentations on the Grid'5000 platform demonstrate the interest of this approach that can be the basis of adaptivity for parallel numerical libraries on dedicated hierarchical platforms.
Development and Optimization of an Electrospraying Device for the Continuous Collection of Nano- and Microparticles

The development, fabrication and testing of a compact electrospray device are presented with the dual goal of increasing yields and total particle amounts by orders of magnitude and retaining the monodispersity of the generated polymeric particles. A new and optimized electrospraying device producing 285 mg/24h monodisperse micro-sized particles with a yield of 79.2% was developed and tested. The new design allowed continuous particle production and collection. By optimization of the chamber design, high velocities in combination with reduced turbulences led to increasing yields. In comparison to a simple electrospraying configuration, the new device enabled collection 14.25 times more particles in 24h. Besides this significant increase in particle amounts and numbers, collection effectiveness increased as well. While a basic electrospraying device allowed collection of only 34.7% of the generated particles, the yields determined for the new device were 79.2%. We report a 2.28 times increase in effectiveness combined with the potential to generate continuously particles with enhanced productivity while maintaining monodispersity of the particles. This modified electrospraying device may facilitate the production of micro- and nano-sized particles for biomedical and pharmaceutical applications in relevant amounts and reduced losses inside the device.
Optimal Joint Trajectory Planning for Manipulator Robot Performing Constrained Motion Tasks

This paper presents a novel approach to plan the optimal joint trajectory for a manipulator robot performing constrained motion tasks. In general, a two-step scheme will be deployed to find the optimal robot joint curve. Firstly, instead of solving a nonlinear, implicit EulerLagrange equation, we discretize the corresponding cost function and use Newton’s iterations to numerically calculate the joint trajectory’s intermediate discrete points. Secondly, we interpolate these points to get the final joint trajectory in a way such that the motion constraint will always be sustained throughout the movement. An example of motion planning for a 4-degree-of-freedom robot WAM will be given at the end of this paper.
NEW PATTERN MATCHING ALGORITHMS FOR NETWORK SECURITY APPLICATIONS

OF THE DISSERTATION New Pattern Matching Algorithms for Network Security Applications by Liu Yang Dissertation Director: Vinod Ganapathy Modern network security applications, such as network-based intrusion detection systems (NIDS) and firewalls, routinely employ deep packet inspection to identify malicious traffic. In deep packet inspection, the contents of network packets are matched against patterns of malicious traffic to identify attack-carrying packets. The pattern matching algorithms employed for deep packet inspection must satisfy two requirements. First, the algorithms must be fast. Network security applications are often implemented as middleboxes that reside on high-speed Gbps links, and the algorithms are expected to perform at such speeds. Second, the algorithms must be space-efficient. The middleboxes that perform pattern matching are often implemented as hardware components, they employ fast but expensive SRAM technology to ensure good performance. Unfortunately, existing pattern matching algorithms suffer from a fundamental timespace tradeoff. The large majority of patterns are regular expressions, and there are three prior approaches for matching such patterns: deterministic finite automaton (DFAs), non-deterministic finite automaton (NFAs), and recursive backtracking-based approaches. DFAs are fast to operate, but are space-inefficient. NFAs are space efficient, but are slow to operate. Recursive backtracking is fast for benign packets but is vulnerable to attack-carrying packets that can induce algorithmic complexity attacks.
Proceedings of the Second Dynamic Aspects Workshop (DAW05)

In developing a real-world complex application, we experience the major problem that complex concerns do not easily map onto low-level aspects with join-points based on fixed points in the program code. It is our observation that modularizing concerns and quantification are to be tackled at design-time, using suitable abstractions, with a translation to dynamic weaving at run-time. In particular, we argue that ‘abstract states’ of software entities (a concern consists of software entities) are a promising instrument for defining higher-level join-points for concerns. The specification and quantification of concerns in terms of abstract states typically result in dynamic weaving, i.e. depending on run-time states of the software entities. Based on experience, we provide requirements for supporting concern modelling and quantification at design-time, as well as an initial sketch of an approach that we investigate in this perspective. The approach is based on a new type of higher-level join-points, called state-based join-points and serves as an example of the necessity for advanced dynamic (state-based) aspect weaving. The approach is motivated and illustrated through a scenario in a real-world application, namely decentralized control software for several automatic guided vehicles in an industrial transportation system.
Multilingual Information Extraction with PolyglotIE

We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data. Our tool has two core features: First, it allows users to develop extractors against a unified abstraction that is shared across a large set of natural languages. This means that an extractor needs only be created once for one language, but will then run on multilingual data without any additional effort or language-specific knowledge on part of the user. Second, it embeds this abstraction as a set of views within a declarative IE system, allowing users to quickly create extractors using a mature IE query language. We present PolyglotIE as a hands-on demo in which users can experiment with creating extractors, execute them on multilingual text and inspect extraction results. Using the UI, we discuss the challenges and potential of using unified, crosslingual semantic abstractions as basis for downstream applications. We demonstrate multilingual IE for 9 languages from 4 different language groups: English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi.
Bagging in computer vision

Previous research has shown that aggregated predictors improve the performance of non-parametric function approximation techniques. This paper presents the results of applying aggregated predictors to a computer vision problem, and shows that the method of bagging significantly improves performance. In fact, the results are better than those previously reported on other domains. This paper explains this performance in terms of the variance and bias.
On the optimization of content publishing and search in peer-to-peer network - a location aware approach

With network pipes growing fatter, peer to peer systems are fast emerging as a viable alternative to the client-server model. One of the major problems in P2P networks is the efficient and fast content distribution and search. In this paper we investigate the problem of replicating content information among the peer nodes in a scalable manner using coordinate based placement such that an automatic identification of near by replica locator nodes is possible to improve the search latency when finding content sources. We also investigate the use of social groups as content information aggregators to facilitate faster and more accurate search. We try to improve the overall accessibility of content in the network by hierarchically distributing the content information. We show by way of simulation the improvement in performance of content location using the above methods.
User tests for the convergence of a car silhouettes design model using an Interactive Genetic Algorithm

We propose in this paper a car silhouettes design model based on an Interactive Genetic Algorithm. The design solutions are indirectly encoded by a Fourier analysis approach. Thus after creating an initial population, the designer is able to evaluate himself generations of car profiles, in order to converge towards an individual which meets its requirements. An automatic test has already been conducted, but it is not sufficient to validate the model; we propose here 6 new tests. The two first ones are based on the user satisfaction towards the created design solution. The data were collected during a short subjective evaluation workshop. The 4 other tests use a similarity index (that quantify the perceived distance between 2 individuals) to process the same data and validate the model. These results reveal a really satisfactory behaviour and open perspectives on the understanding of the user perception.