Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic models with unknown objects

Humans and other intelligent agents must make inferences about the real-world objects that underlie their observations: for instance, the objects visible in an image, or the people mentioned in a set of text documents. The agent may not know in advance how many objects exist, how they are related to each other, or which observations correspond to which underlying objects. Existing declarative representations for probabilistic models do not capture the structure of such scenarios. This thesis introduces Bayesian logic (BLOG), a first-order probabilistic modeling language that specifies probability distributions over possible worlds with varying sets of objects. A BLOG model contains statements that define conditional probability distributions for a certain set of random variables; the model also specifies certain context-specific independence properties. We provide criteria under which such a model is guaranteed to fully define a probability distribution. These criteria go beyond existing results in that they can be satisfied even when the Bayesian network defined by the model is cyclic, or contains nodes with infinitely many ancestors. We describe several approximate inference algorithms that exploit the context-specific dependence structure revealed by a BLOG model. First, we present rejection sampling and likelihood weighting algorithms that are guaranteed to converge to the correct probability for any query on a structurally well-defined BLOG model. Because these algorithms instantiate only those variables that are context-specifically relevant, they can generate samples in finite time even when the model defines infinitely many variables. We then define a general framework for inference on BLOG models using Markov chain Monte Carlo (MCMC) algorithms. This framework allows a programmer to plug in a domain-specific proposal distribution, which helps the Markov chain move to high-probability worlds. Furthermore, the chain can operate on partial world descriptions that specify values only for context-specifically relevant variables. We give conditions under which MCMC over such partial world descriptions is guaranteed to converge to correct probabilities. We also show that this framework performs efficiently on a real-world task: reconstructing the set of distinct publications referred to by a set of bibliographic citations.
Neutrosophy to Enrich Legal Opinion Mining

The main characteristics of legal research lie in its complex operation and the articulation between science, technology, politics, and law. This is why it is claimed that the technology helps experts in processing information resulting from the sources of a legal investigation. Furthermore, the data mining process offers statistical techniques and pattern recognition and trend identification in the stored information. In this way, it facilitates its study and offers a significant contribution to the speed and efficiency required by today's users. Therefore, the main objective of the research is to develop a simple method for mining legal opinions enriched with Neutrosophy that contributes to legal scientific research. This multidisciplinary project allowed obtaining a model that integrates single value neutrosophic numbers to calculate polarity and statistical processing based on data clustering.
Linking Atmospheric River Hydrological Impacts on the U.S. West Coast to Rossby Wave Breaking

AbstractAtmospheric rivers (ARs) have significant hydrometeorological impacts on the U.S. West Coast. This study presents the connection between the characteristics of large-scale Rossby wave breaking (RWB) over the eastern North Pacific and the regional-scale hydrological impacts associated with landfalling ARs on the U.S. West Coast (36°–49°N). ARs associated with RWB account for two-thirds of the landfalling AR events and >70% of total AR-precipitation in the winter season. The two regimes of RWB—anticyclonic wave breaking (AWB) and cyclonic wave breaking (CWB)—are associated with different directions of the vertically integrated water vapor transport (IVT). AWB-ARs impinge in a more westerly direction on the coast whereas CWB-ARs impinge in a more southwesterly direction.Most of the landfalling ARs along the northwestern coast of the United States (states of Washington and Oregon) are AWB-ARs. Because of their westerly impinging angles when compared to CWB-ARs, AWB-ARs arrive more orthogonally to the .
Hardware Trojans

Given the increasing complexity of modern electronics and the cost of fabrication, entities from around the globe have become more heavily involved in all phases of the electronics supply chain. In this environment, hardware Trojans (i.e., malicious modifications or inclusions made by untrusted third parties) pose major security concerns, especially for those integrated circuits (ICs) and systems used in critical applications and cyber infrastructure. While hardware Trojans have been explored significantly in academia over the last decade, there remains room for improvement. In this article, we examine the research on hardware Trojans from the last decade and attempt to capture the lessons learned. A comprehensive adversarial model taxonomy is introduced and used to examine the current state of the art. Then the past countermeasures and publication trends are categorized based on the adversarial model and topic. Through this analysis, we identify what has been covered and the important problems that are underinvestigated. We also identify the most critical lessons for those new to the field and suggest a roadmap for future hardware Trojan research.
Bioinformatics for glycomics : Status , methods , requirements and perspectives

The term ‘glycomics’ describes the scientific attempt to identify and study all the glycan molecules – the glycome – synthesised by an organism. The aim is to create a cell-by-cell catalogue of glycosyltransferase expression and detected glycan structures. The current status of databases and bioinformatics tools, which are still in their infancy, is reviewed. The structures of glycans as secondary gene products cannot be easily predicted from the DNA sequence. Glycan sequences cannot be described by a simple linear one-letter code as each pair of monosaccharides can be linked in several ways and branched structures can be formed. Few of the bioinformatics algorithms developed for genomics/proteomics can be directly adapted for glycomics. The development of algorithms, which allow a rapid, automatic interpretation of mass spectra to identify glycan structures is currently the most active field of research. The lack of generally accepted ways to normalise glycan structures and exchange glycan formats hampers an efficient cross-linking and the automatic exchange of distributed data. The upcoming glycomics should accept that unrestricted dissemination of scientific data accelerates scientific findings and initiates a number of new initiatives to explore the data.
ORACLE-GUIDED SEARCH IN SORTED MATRICES IMPROVING BALANCED FLOW COMPUTATION

In a successor search we are given a key x and a set A from a totally ordered universe and search for the smallest element of A that is larger than or equal to x. It is well known that the number of comparisons with x needed for this task changes from Θ(|A|) to Θ(log |A|) if A is stored in sorted order. Here, we consider a related situation where the elements of A are organised as a so called sorted matrix. In such a matrix every column and every row is sorted. Further, x is given implicitly by a “monotone oracle”. Given a test value t, the oracle answers the question whether t ≥ x. We give a search algorithm for a sorted n × nmatrix performing O(log n) calls to the oracle and O(n) comparisons between matrix elements which we prove to be optimal. We extend this result to the case of non-square matrices and the situation where only columns are sorted. Our search techniques can be applied as the key tool to give an improved algorithm for the uniform balanced network flow problem (ubnfp). The ubnfp consists of finding a feasible s-t-flow of given value F in a graph G = (V, A) which minimizes the difference of the maximum and the minimum flow on an arc. We show that our search techniques can be applied to obtain an O(log m ·T 2 MF(n, m)) time algorithm for solving the ubnfp, where TMF(n, m) is the time required for a maximum flow computation in a network with n vertices and m arcs. This improves upon the previous best time bound of O(n ·T 2 MF(n, m)). Email: {altenhoe,krumke}@mathematik.uni-kl.de Dept. of Mathematics, University of Kaiserslautern, Paul-Ehrlich-Str. 14, 67663 Kaiserslautern, Germany. Email: gassner@opt.math.tu-graz.ac. at Institut fuer Mathematik, Technische Universitaet Graz Steyrergasse 30/II, 8010 Graz, Austria. This research is partially supported by the Austrian Science Fund Project P18918-N18 Email: jacob@in.tum. de Department of Computer Science, Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany 1 2 M. W. ALTENHÖFER, E. GASSNER, R. JACOB, AND S. O. KRUMKE
City Research Online

—Learning more about people mobility is an important task for ofﬁcial decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or ﬂows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and ﬂows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional ﬂow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simpliﬁcations. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in ﬂow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting ﬂows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility and utility of our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identiﬁcation of regular daily and weekly movement patterns of resident population.
Explorer Bootstrapping Generators from Noisy Data

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.
Robots at Work

Abstract We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in seventeen countries from 1993 to 2007 and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.36 percentage points to annual labor productivity growth, while at the same time raising total factor productivity and lowering output prices. Our estimates also suggest that robots did not significantly reduce total employment, although they did reduce low-skilled workers’ employment share.
RCU Usage In the Linux Kernel : One Decade Later

Read-copy update (RCU) is a scalable high-performance synchronization mechanism implemented in the Linux kernel. RCU’s novel properties include support for concurrent reading and writing, and highly optimized interCPU synchronization. Since RCU’s introduction into the Linux kernel over a decade ago its usage has continued to expand. Today, most kernel subsystems use RCU. This paper discusses the requirements that drove the development of RCU, the design and API of the Linux RCU implementation, and how kernel developers apply RCU.
RICE UNIVERSITY CAAM TECHNICAL REPORT TR06-16 THE TOTAL VARIATION REGULARIZED L MODEL FOR MULTISCALE DECOMPOSITION

This paper studies the total variation regularization model with an L 1 ﬁdelity term (TV-L 1 ) for decomposing an image into features of diﬀerent scales. We ﬁrst show that the images produced by this model can be formed from the minimizers of a sequence of decoupled geometry subproblems. Using this result we show that the TV-L 1 model is able to separate image features according to their scales, where the scale is analytically deﬁned by the G -value. A number of other properties including the geometric and morphological invariance of the TV-L 1 model are also proved and their applications discussed.
The limits of intelligence in design

A new, comprehensive design theory is presented, applicable to all design domains such as engineering and industrial design, architecture, city and regional planning, and, in general, any goal-oriented activity that involves decision making. The design process is analyzed into fundamental activities that are characterized with respect to the nature of knowledge requirements and the degree to which they can be specified and delegated to others, in general, and to computers in particular. The characterization of design problems as ``wicked,`` or ``ill-defined,`` design has been understood as a rational activity, that is ``thinking before acting. `` The new theory presented in this paper suggests that design is ``thinking and feeling while acting,`` supporting the position that design is only partially rational. Intelligence, ``natural`` or ``artificial,`` is only one of two requirements for design, the other being emotions. Design decisions are only partially inferred, that is, they are not entirely the product of reasoning. Rather, design decisions are based on judgment that requires the notion of ``good`` and ``bad,`` which is attributed to feelings, rather than thoughts. The presentation of the design theory extends to the implications associated with the limits of intelligence in design, which, in turn, become constraints on the potentialmore » role of computers in design. Many of the current development efforts in computer-aided design violate these constraints, especially in the implementation of expert systems and multi-criterion evaluation models. These violations are identified and discussed in detail. Finally, specific areas for further research and development in computer-aided design are presented and discussed. « less
Estimating the permanent loss of groundwater storage in the southern San Joaquin Valley, California

In the San Joaquin Valley, California, recent droughts starting in 2007 have increased the pumping of groundwater, leading to widespread subsidence. In the southern portion of the San Joaquin Valley, vertical subsidence as high as 85 cm has been observed between June 2007 and December 2010 using Interferometric Synthetic Aperture Radar (InSAR). This study seeks to map regions where inelastic (not recoverable) deformation occurred during the study period, resulting in permanent compaction and loss of groundwater storage. We estimated the amount of permanent compaction by incorporating multiple data sets: the total deformation derived from InSAR, estimated skeletal‐specific storage and hydraulic parameters, geologic information, and measured water levels during our study period. We used two approaches, one that we consider to provide an estimate of the lowest possible amount of inelastic deformation, and one that provides a more reasonable estimate. These two approaches resulted in a spatial distribution of values for the percentage of the total deformation that was inelastic, with the former estimating a spatially averaged value of 54%, and the latter a spatially averaged value of 98%. The former corresponds to the permanent loss of 4.14 × 108 m3 of groundwater storage, or roughly 5% of the volume of groundwater used over the study time period; the latter corresponds to the loss of 7.48 × 108 m3 of groundwater storage, or roughly 9% of the volume of groundwater used. This study demonstrates that a data‐driven approach can be used effectively to estimate the permanent loss of groundwater storage.
Instantaneous Feedback-based Opportunistic Symbol Length Adaptation for Reliable Communication

It is well known that although feedback cannot increase the channel capacity of memoryless channels, it can enhance reliability or shorten codeword length. This work is based on an early result by Viterbi in 1965 that utilizes instantaneous feedback for reliable communications. We build on this work by incorporating (tail-biting) convolutional codes and designing a system where the decoder interacts with the transmitter by sending feedback during the decoding process. The proposed system is called Opportunistic Symbol Length Adaptation (OSLA), in which the symbol length opportunistically adapts to noise realization of each symbol to ensure that the target reliability is achieved. It is shown that, combined with tail-biting convolutional codes, the proposed scheme outperforms state-of-the-art non-feedback codes, as well as a recently proposed deep learning-based feedback scheme with up to 1.5 dB gain in noise-less and noisy feedback channels.
Autonomous Segmentation and Extraction of Objects in Aerial Images Acquired through a Low Cost Still Camera onboard UAV

This paper proposes a reliable and easy to implement solution for autonomous segmentation and extraction of objects from aerial images captured by a low cost digital camera on-board Unmanned Aerial Vehicles in real time. In recent times, vision has become an ideal sensor modality for autonomous and intelligent unmanned aerial systems for reconnaissance and surveillance operations both in civil and military applications. Real time autonomous extraction of visually significant targets in the image thus becomes imperative and an important problem whose solution has been provided in this paper. The proposed approach utilizes colour and luminance feature of the image to generate a contrast map. This contrast map is further enhanced using multi-channel edge information to locate possible visually salient objects. A graph-cut based approach is then used to extract the object from the image.
AN EFFICIENT PERFORMANCE EXTRAPOLATION FOR QUEUING MODELS IN TRANSIENT ANALYSIS

In designing, analyzing and operating real-life complex systems, we are interested, however, not only in performance evaluation but in sensitivity analysis and optimization as well. Since most systems of practical interest are too complex to allow the analytical solution of totally realistic models, these systems must be studied by means of Monte-Carlo simulation. One problem with Monte Carlo analysis is its expensive use of computer time. To address this problem, we propose an efficient technique for estimating the expected performance of a stochastic system for various values of the parameters from a single simulation of the nominal system. This technique is based on the likelihood ratio performance extrapolation (LRPE). We provide numerical experiments that demonstrate how the proposed technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis.
Optimization by Genetic Algorithm

The genetic algorithm is Search and optimization techniques that generate solutions to optimization problems using techniques inspired by natural evolution. Optimization is the central to any problem involving whether in engineering or economics. This paper presents experimental results of most important benchmark function i.e dejong function by genetic algorithm. This result shows that genetic algorithm provide more optimal solution.
Integrating the Troublemakers: A Taxonomy for the Cooperation between Banks and Fintechs

The banking sector has been subject to fundamental changes as the digitalization enables novel technology-driven banking services and creates new customer demands. Whereas banks face sluggish innovation processes, fintechs take advantage of the digital era, and deliver customer-centric solutions. Although banks have realized that cooperation with fintechs represents a vital approach to foster innovation, they struggle to address the associated challenges. However, research analyzing this phenomenon to establish best practices is scarce, because neither the cooperation between banks and fintechs, nor associated and relevant characteristics have been evaluated. Therefore, we propose a taxonomy which is theoretically funded and empirically proven. Based on literature, 136 real-world cases, and 12 expert interviews, our results suggest to structure and describe cooperations between banks and fintechs through 13 dimensions. Moreover, the empirical examination allows the identification of prevailing cooperation patterns. Our findings contribute to theory development in the field of fintechs, their integration into the banking sector, and the research area of cross-organizational cooperation. Furthermore, this paper reveals practical implications for both banks and fintechs and opens promising avenues for future research.
An Analysis of the Predictivity of ADME QSAR Models Over Time

Three different global QSAR models, human plasma protein binding, aqueous solubility and log D7.4, were updated on a monthly basis over a period of three years. The effect of updating the models on their predictivity was studied using a series of monthly temporal test sets in addition to a final terminal temporal test set. Partial least squares (PLS), Random Forests (RF) and Bayesian Neural Networks (BNN) models are examined, covering three distinctly different approaches to QSAR modelling. It is demonstrated that the models are able to predict forward in time, but that updating models on a regular basis increases their ability to make predictions for current compounds. The degree of the improvement depends on the property studied and the model building technique used. These results demonstrate the importance of updating models on a regular basis. For both static models predicting forward in time, and regularly updating models it is shown that RF models are the most predictive.
AIS Electronic Library (AISeL)

Decision tree pruning is critical for the construction of good decision trees. The most popular and widely used method among various pruning methods is cost-complexity pruning, whose implementation requires a training dataset to develop a full tree and a validation dataset to prune the tree. However, different pruned trees are found to be produced when the original dataset are randomly partitioned into different training and validation datasets. Which pruned tree is the best? This paper presents an approach derived from Bayes’ theorem to select the best pruned tree from a group of pruned trees produced by cost-complexity pruning method. The results of an experimental study indicate that the proposed approach works satisfactorily to find the best pruned tree in terms of classification accuracy and performance stability