Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Bias Correction Algorithm for use in Data Assimiliation

This report examines the e ect of model bias within a data assimilation system. For this study we use the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) which employs the Incremental Analysis Updating (IAU) procedure. In such a procedure, the after-analysis minus rst guess increments are used as external forcing within a general circulation model (GCM) to provide corrective adjustments for data assimilation. It is shown that for a GCM which contains a climatological model bias, the climatology of the assimilation will be biased from the climatology of the after-analysis. A modi ed IAU procedure is presented which e ectively removes the bias from the assimilation. The procedure may also be modi ed for incorporation into traditional intermittent data assimilation approaches. iii
A usage-based model for the acquisition of syntactic constructions and its application in spoken language understanding

According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions as pairings of meaning and form at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item-based knowledge. In this thesis, we first explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity and abstraction can be modeled computationally by formalizing ideas proposed within usage-based theories to language acquisition and construction grammar. Similar to a child, the model learns language by observing natural language utterances, represented as sequences of words, in an ambiguous context. Starting from ambiguous contexts, the model establishes form-meaning mappings based on cross-situational statistics, a mechanism also referred to as cross-situational learning. In contrast to previous models investigating cross-situational learning, which typically focused on word-referent mappings, we explore how the same cross-situational learning mechanism can be applied consistently to establish form-meaning pairings beyond such simple mappings. We present empirical results, showing that the model can learn a compact and precise representation of the input data which generalizes well to unseen data. In line with findings from psycholinguistic studies with children, language learning in the model proceeds gradually. The model’s generalization abilities are initially limited, increase over time, and finally converge, suggesting that the employed mechanisms allow accurate learning without (severe) deterioration of knowledge already captured by the network during further processing of examples. In addition, we present empirical results that are in line with recent findings from psycholinguistic studies with children and that show i) how our model is able to perform cross-situational verb learning by storing information about possible referents with verb entries and ii) how it can establish verb entries based on syntactic information alone. The model thus suggests learning mechanisms that may be at play during the emergence of
Error Correcting Coding and Security for Data Networks: Analysis of the Superchannel Concept

Preface.1 Problems Facing Error Control Coding in Data Networks.1. 1 International Recommendations on Using Error Control Coding at Different Network Layers.1. 2 Classification of Problems on Coding in Networks.2 Block Codes.2. 1 Main Definitions.2. 2 Algebraic Structures.2. 3 Linear Block Codes.2. 4 Cyclic Codes.2. 5 Bounds on Minimum Distance.3 General Methods of Decoding of Linear Codes.3. 1 Minimum Distance Decoding.3. 2 Information Set Decoding.3. 3 A Supercode Decoding Algorithm.3. 4 The Complexity of Decoding in the Channel with Independent Errors.4 Codes with Algebraic Decoding.4. 1 Hamming Codes.4. 2 Reed-Solomon Codes.4. 3 BCH Codes.4. 4 Decoding of BCH Codes.4. 5 The Sudan Algorithm and its Extensions.5 Decoding of LDPC Codes.5. 1 Low-Density Parity-Check Codes.5. 2 LDPC Constructions.5. 3 Estimating the Minimum Distance of EG-LDPC Codes.5. 4 Burst-Error-Correcting LDPC Codes.5. 5 Decoding Schemes of LDPC Codes.5. 6 Simulation Results in AWGN.Appendix 5.A Euclidean Geometries.6 Convolutional Codes and Turbo-Codes.6. 1 Convolutional Codes Representation and Encoding.6. 2 Viterbi Decoding Algorithm.6. 3 List Decoding.6. 4 Sequential Decoding.6. 5 Parallel-Concatenated Convolutional Codes and Soft Input Soft Output Decoding.6. 6 SISO Decoding Algorithms.7 Coding of Messages at the Transport Layer of the Data Network.7. 1 Decreasing the Message Delay with the help of Transport Coding.7. 2 Transmission of Message during Limited Time.7. 3 Transmission of Priority Messages without using Priority Packets . 7.4 Estimation of the Effectiveness of Transport Coding for the Nonexponential Model of Packet Delay.8 Providing Security of Data in a Network with the Help of Coding Methods.8. 1 Public-Key Cryptography.8. 2 Codebased Cryptosystems: McEliece and Niederreiter.8. 3 Cryptosystems Based on Full Decoding.8. 4 Further Development of Codebased Cryptosystems.8. 5 Codebased Cryptosystems and RSA: Comparison and Perspectives.8. 6 Codebased Signature.9 Reconciliation of Coding at Different Layers of a Network.9. 1 Transport Coding in a Network with Unreliable Channels.9. 2 Reconciliation of Channel and Transport Coding.9. 3 Use of Tornado Codes for Reconciliation of Channel and Transport Coding.9. 4 Development of Coding Methods at the Presentation Layer.9. 5 Reconciliation of Coding at Neighbour Layers of a Network.Index.
Advanced volume rendering

Although many advances have been achieved within the visualization community in the last decade, many challenging problems are still open in volume rendering: high-dimensional rendering, time-varying datasets, large datasets, complex flow fields, improvement of rendering accuracy, fidelity and interactivity, interdisciplinary research with other application communities, and so on. In this thesis, we study three challenging topics in advanced volume rendering: volumetric shadow and soft shadow algorithm in order to generate more realistic scenes; interval volumes and time-varying interval volumes for structured and unstructured grids; and implicit flow fields for three-dimensional flow visualization. A shadow is a region of relative darkness within an illuminated region caused by an object totally or partially occluding a light source. Shadows are essential to realistic and informative scenes. In volume rendering, the shadow calculation is difficult because the light intensity is attenuated as the light traverses the volume. We investigate a new shadow algorithm that properly determines the light attenuation and generates shadows for volumetric datasets, by using a 2D shadow buffer to keep track of the light attenuation through the volumetric participating media. Our shadow algorithm can generate accurate shadows with low storage requirements. The generation of soft shadows is a challenging topic in computer graphics which requires integrating the contributions of extended light sources on the illumination of objects. We have extended our shadow algorithm to deal with extended light sources and generate volumetric soft shadows with an analytic method and using a convolution technique. This shadow and soft shadow algorithm also has been applied to mixed scenes of volumetric and polygonal objects. Multiple light scattering is also modeled in our volumetric lighting model. Interval volume algorithm is a region-of-interest extraction algorithm for steady and time-varying three-dimensional structured and unstructured grids. A high-dimensional iso-surface algorithm is used to construct interval volumes. The algorithm is independent of the dimension and topology of the polyhedral cells comprising the grid, and thus offers an excellent enhancement for volume rendering of unstructured grids. Three-dimensional flow visualization is a challenging topic due to clutter, occlusion, and lack of depth perception cues in three dimensions. We propose an implicit flow field method to visualize 3D flow fields. An implicit flow field is first extracted using an advection operator on the flow, with a set of flow-related attributes stored. Two techniques are then employed to render the implicit flow field: a slice-based three-dimensional texture mapping approach and an interval volume approach. (Abstract shortened by UMI.)
Privacy Preserving Clustering In Data Mining

Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Such data include shopping habits, criminal records, medical history, credit records etc . On one hand such data is an important asset to business organization and governments for decision making by analyzing it . On the other hand privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate clustering result. Trying to give solution for this we implemented vector quantization approach piecewise on the datasets which segmentize each row of datasets and quantization approach is performed on each segment using K means which later are again united to form a transformed data set. Some experimental results are presented which tries to finds the optimum value of segment size and quantization parameter which gives optimum in the tradeoff between clustering utility and data privacy in the input dataset.
Network Science: Theory and Applications

The first comprehensive book on the emerging field of network science, Network Science: Theory and Applications is an exhaustive review of terms, ideas, and practices in the various areas of network science. In addition to introducing theory and application in easy-to-understand, topical chapters, this book describes the historical evolution of network science through the use of illustrations, tables, practice problems with solutions, case studies, and applications to related Java software. Researchers, professionals, and technicians in engineering, computing, and biology will benefit from this overview of new concepts in network science.
Connecting Topincs Using transclusion to connect proxy spaces

Topincs is a software system for agile and distributed knowledge management on top of the common web infrastructure and the Topic Maps Data Model. It segments knowledge into stores and offers users a way to establish a temporary connection between stores through transclusion and merging. This allows to easily copy topics and statements. Through this mechanism, later acts of integration are simplified, due to matching item identifiers. Furthermore, transclusion can be used to create permanent connections between stores.
Théories homologiques des algèbres de Hopf

Dans cette these, nous etudions des theories homologiques et cohomologiques adaptees aux algebres de Hopf. Dans un premier temps, nous unifions diverses theories cohomologiques pour les algebres de Hopf. Deux d'entre elles ont ete introduites par M. Gerstenhaber et S.D. Schack; l'une est sans coefficients et elle est liee a la cohomologie qui permet d'etudier les deformations d'une algebre de Hopf, l'autre est une theorie a coefficients (qui sont des bimodules de Hopf). La troisieme est une generalisation de la cohomologie qui a ete definie par C. Ospel, il s'agit aussi d'une theorie a coefficients. Pour unifier ces theories, nous les identifions au foncteur Ext sur une algebre associative definie par C. Cibils et M. Rosso qui est une ``algebre enveloppante'' associee a l'algebre de Hopf. Nous etablissons ensuite des formules explicites pour un cup-produit sur deux de ces cohomologies, et montrons que ce produit correspond au produit de Yoneda des extensions. Nous montrons aussi la Morita invariance de ces cohomologies. La deuxieme partie de la these est consacree a l'etude d'une homologie cyclique pour les algebres de Hopf. Il s'agit d'une version duale de la cohomologie qu'ont introduite A. Connes et H. Moscovici. Nous en etudions des proprietes, puis considerons le cas des algebres de groupe. Nous interpretons certaines decompositions (de Burghelea et de Karoubi-Villamayor) de l'homologie cyclique classique d'une algebre de groupe en termes d'homologie cyclique de Connes et Moscovici. Nous etablissons ensuite une formule de decomposition (semblable a celle de Karoubi-Villamayor) de l'homologie cyclique d'une algebre de Hopf cocommutative (qui generalise un resultat de Khalkhali et Rangipour). Enfin, nous calculons quelques exemples d'homologies: l'homologie cyclique classique des algebres de carquois tronquees, ainsi que l'homologie cyclique de Connes et Moscovici dans le cas particulier des algebres de Taft. Nous calculons aussi l'homologie de Hochschild et l'homologie cyclique classique des algebres d'Auslander des algebres de Taft.
"THE POTENTIAL LONG-TERM EFFECTS OF GAMMA IRRADIATION ON PAPER

Abstract The long-term effect of gamma irradiation on paper was studied. Three types of paper (pure rag, mixed rag and mechanically prepared woodpulp) were subjected to three treatments: gamma irradiation, accelerated dry aging, and radiation followed by aging. The effects of these treatments on the physical properties of the paper samples were assessed by measuring alterations in mechanical strength as determined by changes in folding endurance and tear resistance when compared to the untreated control samples. It was found that gamma irradiation caused a decrease in the mechanical strength of the papers similar to that resulting from accelerated dry aging. A synergistic effect was found when both irradiation and accelerated aging were performed on the paper samples, demonstrated by an even greater decrease in both fold endurance and tear resistance than that resulting from either gamma irradiation or accelerated dry aging alone.
A NEW MODEL FOR 'IMPULSIVE' PHENOMENA: APPLICATION TO ATMOSPHERIC-NOISE COMMUNICATION CHANNELS

Abstract : The work is concerned with the development and application of an analytical model for atmospheric noise, which is radio noise originating in lightning discharges. The generalized 't' model resulting from our approach is in good agreement with measured data and describes the received atmospheric noise y(t) as y(t) = a(t) n(t), where n(t) is a narrowband gaussian process and a(t) is a slowly varying random process, independent of n(t), that 'modulates' n(t). The detection of known signals in additive generalized 't' noise is considered, and the receiver that minimizes the probability of error found to be a nonlinear 'logarithmic-correlator' receiver. Analytical and experimental results indicate that the probability of error achieved by this receiver in the presence of additive atmospheric noise.
A unified data-centric approach towards an extensible internet architecture

The Internet's role is changing dramatically, from a means of connecting together PCs and servers to a ubiquitous communications medium interconnecting mobile personal devices, environmental sensors, and Web services. As new services (voice, video, emergency response, etc.) are being deployed on the infrastructure, there have been increased demands on extending the existing Internet architecture for new capabilities, such as efficient and resilient routing among mobile and wired nodes, location of proximity-based services, and wide-area service discovery and composition. This dissertation presents an extensible Internet architecture M OSAIC—based on declarative languages and composable views over router, network and host state at different layers—to meet the demand of the emerging network services. The proposed architecture explicitly separates logical state representation and acquisition from physical implementation, to enable more extensible and adaptive protocols and distributed systems, because programmers can focus on high-level logical operations whose implementations may be separately and transparently optimized. The main contributions made by this dissertation are: (1) a unified network architecture (MOSAIC) under which new networks can be developed, deployed, selected, and dynamically composed according application and administrator needs; (2) a declarative programming language (Mozlog) to concisely specify high-level network protocol specifications; (3) a runtime system prototype that can translate Mozlog specifications into efficient implementations. To validate the approach, we evaluate the work in the context of a heterogeneous, dynamic Internet environment where end hosts are connected via both wired and wireless media and have diverse application requirements. We demonstrate that in MOSAIC, new network services can be readily introduced by either concisely specifying protocols in Mozlog or dynamically selecting and composing existing network services at low overheads.
Sobre a construção de um léxico da afetividade para o processamento computacional do português

The main goal of this paper is to describe the creation of a sentiment lexicon designed to collaborate in sentiment analysis tasks. We also show how the analysis of the affective language finds room in language description and that it can contribute to foreign language teaching as well. Thus, the lexicon was created with a dual purpose: provide a resource for natural language processing tasks and collaborate to the characterization of Portuguese language affective vocabulary, especially in informal contexts.
Privacy Leakage in Smart Homes and Its Mitigation: IFTTT as a Case Study

The combination of smart home platforms and automation apps introduce many conveniences to smart home users. However, this also brings the potential of privacy leakage. If a smart home platform is permitted to collect all the events of a user day and night, then the platform will learn the behavior patterns of this user before long. In this paper, we investigate how IFTTT, one of the most popular smart home platforms, has the capability of monitoring the daily life of a user in a variety of ways that are hardly noticeable. Moreover, we propose multiple ideas for mitigating privacy leakages, which all together form a “Filter-and-Fuzz” (F&F) process: first, it filters out events unneeded by the IFTTT platform. Then, it fuzzifies the values and frequencies of the remaining events. We evaluate the F&F process and the results show that the proposed solution makes the IFTTT unable to recognize any of the user’s behavior patterns.
A Multi-Kingdom Study Reveals the Plasticity of the Rumen Microbiota in Response to a Shift From Non-grazing to Grazing Diets in Sheep

Increasing feed efficiency is a key target in ruminant science which requires a better understanding of rumen microbiota. This study investigated the effect of a shift from a non-grazing to a grazing diet on the rumen bacterial, methanogenic archaea, fungal, and protozoal communities. A systems biology approach based on a description of the community structure, core microbiota, network analysis, and taxon abundance linked to the rumen fermentation was used to explore the benefits of increasing depth of the community analysis. A total of 24 sheep were fed ryegrass hay supplemented with concentrate (CON) and subsequently ryegrass pasture (PAS) following a straight through experimental design. Results showed that concentrate supplementation in CON-fed animals (mainly starch) promoted a simplified rumen microbiota in terms of network density and bacterial, methanogen and fungal species richness which favored the proliferation of amylolytic microbes and VFA production (+48%), but led to a lower (ca. 4-fold) ammonia concentration making the N availability a limiting factor certain microbes. The adaptation process from the CON to the PAS diet consisted on an increase in the microbial concentration (biomass of bacteria, methanogens, and protozoa), diversity (+221, +3, and +21 OTUs for bacteria, methanogens, and fungi, respectively), microbial network complexity (+18 nodes and +86 edges) and in the abundance of key microbes involved in cellulolysis (Ruminococcus, Butyrivibrio, and Orpinomyces), proteolysis (Prevotella and Entodiniinae), lactate production (Streptococcus and Selenomonas), as well as methylotrophic archaea (Methanomassiliicoccaceae). This microbial adaptation indicated that pasture degradation is a complex process which requires a diverse consortium of microbes working together. The correlations between the abundance of microbial taxa and rumen fermentation parameters were not consistent across diets suggesting a metabolic plasticity which allowed microbes to adapt to different substrates and to shift their fermentation products. The core microbiota was composed of 34, 9, and 13 genera for bacteria, methanogens, and fungi, respectively, which were shared by all sheep, independent of diet. This systems biology approach adds a new dimension to our understanding of the rumen microbial interactions and may provide new clues to describe the mode of action of future nutritional interventions.
Measuring Long-term Impact of Ads on LinkedIn Feed

Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, attracts them to engage, brings them value and helps LinkedIn grow. Engagement and Revenue on feed are two critical, yet often conflicting objectives. Hence, it is important to design a good Revenue-Engagement Tradeoff (RENT) mechanism to blend ads in the feed. In this paper, we design experiments to understand how members' behavior evolve over time given different ads experiences. These experiences vary on ads density, while the quality of ads (ensured by relevance models) is held constant. Our experiments have been conducted on randomized member buckets and we use two experimental designs to measure the short term and long term effects of the various treatments. Based on the first three months' data, we observe that the long term impact is at a much smaller scale than the short term impact in our application. Furthermore, we observe different member cohorts (based on user activity level) adapt and react differently over time.
Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness

We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We evaluate our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components. We show that models learnt with our approach perform remarkably well against a wide-range of attacks. Furthermore, combining NoL with state-of-the-art adversarial training extends the robustness of a model, even beyond what it is adversarially trained for, in both white-box and black-box attack scenarios.
Heterogeneous Edge Embeddings for Friend Recommendation

We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks. Most real-world social networks are multi-graphs, where different kinds of relationships (e.g. chat, friendship) are possible between a pair of users. Existing network embedding techniques do not leverage signals from different edge types and thus perform inadequately on link prediction in such networks. We propose a method to mine network representation that effectively exploits heterogeneity in multi-graphs. We evaluate our model on a real-world, active social network where this system is deployed for friend recommendation for millions of users. Our method outperforms various state-of-the-art baselines on Hike's social network in terms of accuracy as well as user satisfaction.