Miracle-dz/UniFAR-FTUs huggingface dataset:
Speciﬁcation and Veriﬁcation: The Spec# Experience

Spec# is a programming system that puts speciﬁcations in the hands of programmers and includes tools that use them. The system includes an object-oriented programming language with speciﬁcation constructs, a compiler that emits executable code and run-time checks for speciﬁcations, a programming methodology that gives rules for structuring programs and for using speciﬁcations, and a static program veriﬁer that attempts to mathematically prove the correctness of programs. This paper reﬂects on the six-year experience of building and using Spec#, the scientiﬁc contributions of the project, remaining challenges for tools that seek to establish program correctness, and prospects of incorporating program veriﬁcation into everyday software engineering.
Does intelligence require a body ?

Our brain controls our body—every movement we make is guided by neuronal activity. But how about vice versa? Does the body influence our brain; does it have an impact on how we think? This question might seem odd. We usually stick to a simple ‘sense– think–act’ paradigm: we hear the phone ring (sense), we recognize that we should answer it (think) and grab the receiver (act). This is a one-way process: the brain receives input from the senses and calculates the output. After a long tradition of dualistic thought, influenced most prominently by the French philosopher René Descartes, we tend to regard cognition as virtually disembodied: Descartes argued that the mind and the body are distinct and that thinking is assigned to an immaterial soul that exists independently of a mechanically functioning organism [1].
Predicting the Side Effects of Drugs using Matrix Factorization on Spontaneous Reports Database

Background Drugs with severe side effects can be threatening to patients and compromise pharmaceutical companies financially. Various computational techniques have been proposed to predict the side effects of drugs, including methods that utilize chemical, biological, and phenotypic features. Among them, matrix factorization (MF), which harnesses the known side effects of different drugs, has shown promising results. However, methods encapsulating all characteristics of side-effect prediction have not been investigated thus far. To this effect, we employed the logistic matrix factorization (Logistic MF) algorithm, i.e., MF modified for implicit feedback data, on a spontaneous reports database to improve the accuracy of side-effect prediction.Results A weighting strategy was applied to account for differences in the importance of the drug-side effect pairs. The impact of the cold-start problem and means to tackle it using the attribute-to-feature mapping were also explored. The experimental results demonstrate that the proposed model improved the prediction accuracy by 2.3% and efficiently handled the cold-start problem.Conclusion The proposed methodology is envisaged to benefit applications such as warning systems in clinical settings.
Appearance based Techniques for Face Recognition: A Comparative Study

In these days, appearance based approaches gain popularity in many computer vision problems, more in particular on face recognition techniques. In this context, a study on face recognition techniques based on appearance based paradigm is addressed in our work. More focus is provided to principal component analysis (PCA) based techniques where the principle of PCA is well received by pattern recognition community for most of the dimensionality reduction problems or for feature selection in a large collection of features set. We have seen several variants of PCA in the literature applied to the domain of face recognition considering variety of natural problems that would occur during face recognition. In our work, we have made an attempt to study the problem of face recognition under different situations. The study is conducted with varying dimension of features on a variety of face databases which include pose, illumination and occlusion problems. The effect of varying training samples is also addressed in our study. We have considered the standard PCA, two dimensional PCA (2D PCA) which works in row directions, alternative 2D PCA that works in column directions and bidirectional PCA for comparative analysis on many of the standard face databases such as AT&T, UMIST and IITK datasets. Extensive experimental results on each of these datasets along with computing time and their recognition accuracy under different dimension of feature vectors with varying number of training samples is reported in our work.
Enigmatic plant-working tools and the transition to farming in the Rhine/Meuse Delta

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What Does Financial Volatility Tell Us About Macroeconomic Fluctuations ? * February 6 , 2012

This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.
Preserve the Privacy of Anonymous and Confidential Database using K-anonymity

In field of IT sector to maintain privacy and confidentiality of data is very important for decision making. So there is requirement of certain data to be published and exchanging of the information is in demand. The data to be exchanged contains sensitive information which moves around various parties and this may violate individual's privacy. So to preserve information in its accurate form while moving among various parties, my aim is to provide mechanism known as k_anonymous technique that doesn’t allow the unauthenticated user to modify the data. In this application two protocols that will solve this problem based on suppression and generalization k-anonymous and confidential databases are used. The protocols rely on well-known cryptographic assumptions, and it provides theoretical analyses to proof their experimental results to illustrate their efficiency. KeywordsAnonymity, data management, privacy, secure
A Delay-Optimal Group Mutual Exclusion Algorithm for a Tree Network

The group mutual exclusion problem is an extension of the traditional mutual ex- clusion problem in which every critical section is associated with a type or a group. Proc- esses requesting critical sections of the same type can execute their critical sections con- currently. However, processes requesting critical sections of different types must execute their critical sections in a mutually exclusive manner. We present an efficient distributed algorithm for solving the group mutual exclusion problem when processes are arranged in the form of a tree. Our algorithm is derived from Beauquier et al.'s group mutual exclusion algorithm for a tree network. The mes- sage complexity of our algorithm is at most 3hmax, where hmax is the maximum height of the tree when rooted at any process. Its waiting time and synchronization delay, meas- ured in terms of number of message hops, are at most 2hmax and hmax, respectively. Our algorithm has optimal synchronization delay for the class of tree network based algo- rithms for group mutual exclusion in which messages are only exchanged over the edges in the tree. Our simulation experiments indicate that our algorithm outperforms Beauquier et al.'s group mutual exclusion algorithm by as much as 70% in some cases.
Process logic: Expressiveness, decidability, completeness

We define a process logic PL that subsumes Pratt's process logic, Parikh's SOAPL, Nishimura's process logic, and Pnueli's Temporal Logic in expressiveness. The language of PL is an extension of the language of Propositional Dynamic Logic (PDL). We give a deductive system for PL which includes the Segerberg axioms for PDL and prove that it is complete. We also show that PL is decidable.
for the Information

The article describes the algorithm and principles of operation of an automated system designed to study parental satisfaction with the quality of services provided by preschool education organizations. The relevance of the research is determined by the existing contradiction between the high demand for reliable and relevant information about parental satisfaction with the quality of services provided by preschool institutions from regional and municipal education authorities and administration of preschool institutions, and the insufficiently developed scientific and methodological tools for studying this issue. Successful socialization of a child requires close interaction between his parents or legal representatives with teaching staff and the administration. The study of requests for the services of pre-school educational organizations and their subjective evaluation by customers and consumers is one of the measures for coordinating interests and building cooperation between subjects of educational activities. The questionnaire and database developed by the authors lays the basis of the automated system. The attention given to parental opinion allows identifying the state and significant trends in development of preschool institutions, both at the institutional, municipal and regional levels, to make it possible not only to quickly respond to the social demand, but also to build effective interaction between administration, teachers and parents, to share responsibility for the educational process.
Backjump-based techniques versus conflict-directed heuristics

We present a general algorithm which gives a uniform view of several state-of-the-art systematic backtracking search algorithms for solving both binary and nonbinary CSP instances. More precisely, this algorithm integrates the most usual or/and sophisticated look-back and look-ahead schemes. By means of this algorithm, our purpose is then to study the interest of backjump-based techniques with respect to conflict-directed variable ordering heuristics.
metaFlye: scalable long-read metagenome assembly using repeat graphs

Long-read sequencing technologies substantially improved assemblies of many isolate bacterial genomes as compared to fragmented assemblies produced with short-read technologies. However, assembling complex metagenomic datasets remains a challenge even for the state-of-the-art long-read assemblers. To address this gap, we present the metaFlye assembler and demonstrate that it generates highly contiguous and accurate metagenome assemblies. In contrast to short-read metagenomics assemblers that typically fail to reconstruct full-length 16S RNA genes, metaFlye captures many 16S RNA genes within long contigs, thus providing new opportunities for analyzing the microbial “dark matter of life”. We also demonstrate that long-read metagenome assemblers significantly improve full-length plasmid and virus reconstruction as compared to short-read assemblers and reveal many novel plasmids and viruses.
Archaeological Explanation for the Diffusion Theory of the Japonic and Koreanic Language

From a linguistic standpoint, it is assumed that the Japonic language family entered into the Korean Peninsula and from there spread to the Japanese archipelago at the beginning of the Yayoi period, around the 8 th century BC, while the arrival of the Koreanic language family is associated with the advent of the Korean-style bronze dagger culture around 5 th century BC (Whitman 2011). Evidence of demic diffusion in archaeological events (Miyamoto 2014) indicates the result of a linguistic founders effect, because both Koreanic and Japonic are relatively shallow language families (Whitman 2011). But Japonic has no vocabulary dedicated to rice (Whitman 2011). There is some contradiction between the absence of rice vocabulary in Japonic and the demic diffusion of rice farmers in the archeological evidence. In this paper, I would like to resolve this contradiction by offering a new explanation for the demic diffusion of Mumun culture, which possessed a Japonic language, on the Korean peninsula.
Normal Uniform mixture Di(cid:11)erential Gene Expression Detection for cDNA Microarrays

One of the primary tasks in analysing gene expression data is (cid:12)nding genes that are differentially expressed in di(cid:11)erent samples. Some of the more popular methods for cDNA microarray data are based on examining the ratio of expression levels in the two channels, or on using t tests on log ratios. The issue of multiple testing becomes problematic with thousands of tests being routine in an experiment. We propose a simple method, Normal Uniform Di(cid:11)erential Gene Expression (NUDGE) detection, that uses a simple univariate normal-uniform mixture model, in combination with new normalization methods for spread as well as mean that extend the lowess normalization of (Dudoit et al. 2002). It takes account of multiple testing, and gives probabilities of di(cid:11)erential expression as part of its output. It can be applied to either single-slide or replicated experiments, and it is very fast. Three datasets are analyzed using NUDGE, and the results are compared to those given by other popular methods: unadjusted and Bonferroni-adjusted t tests, Signi(cid:12)cance Analysis of Microarrays (SAM), and Empirical Bayes for microarrays (EBarrays) with both Gamma-Gamma and Lognormal-Normal models.
Rateless Codes on Noisy Channels

We study the performance of the newly invented rateless codes (LTand Raptor codes) on noisy channels such as the BSC and the AWGN channel. We find that Raptor codes outperform LT codes, and have good performance on a wide variety of noisy channels. To be published in the Proceedings of the International Symposium on Information Theory, 2004. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2004 201 Broadway, Cambridge, Massachusetts 02139 1. To be published in the Proceedings of the International Symposium on Information Theory, 2004. Please see TR2003-124 for a longer version. Rateless codes on noisy channels Ravi Palanki Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125 USA. e-mail: ravi@systems.caltech. edu Jonathan S. Yedidia Mitsubishi Electric Research Laboratories 201 Broadway 8th floor Cambridge, MA 02139 USA. e-mail: yedidia@merl.com Abstract — We study the performance of two classes of rateless codes (LTand Raptor codes) on noisy channels such as the BSC and the AWGNC. We find that Raptor codes outperform LT codes, and have good performance on a wide variety of noisy channels. We study the performance of two classes of rateless codes (LTand Raptor codes) on noisy channels such as the BSC and the AWGNC. We find that Raptor codes outperform LT codes, and have good performance on a wide variety of noisy channels.
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage

Structural health monitoring (SHM) is vital for ensuring the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, operate passively without human intervention, and customized for each mechanical structure without the need for complex baseline models. We present a novel"deploy-and-forget"approach for automated detection and localization of damages in structures. It is based on a synergistic combination of fully passive measurements from inexpensive sensors and a mechanics-informed autoencoder. Once deployed, our solution continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state's response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the variational autoencoder allows for up to 35\% earlier detection and localization of damage over a standard autoencoder. Our approach holds substantial promise for a significant reduction in human intervention and inspection costs and enables proactive and preventive maintenance strategies, thus extending the lifespan, reliability, and sustainability of civil infrastructures.
A Comparison of Geodetic and Geologic Rates Prior to Large Strike‐Slip Earthquakes: A Diversity of Earthquake‐Cycle Behaviors?

Comparison of preevent geodetic and geologic rates in three large‐magnitude (Mw = 7.6–7.9) strike‐slip earthquakes reveals a wide range of behaviors. Specifically, geodetic rates of 26–28 mm/yr for the North Anatolian fault along the 1999 MW = 7.6 Izmit rupture are ∼40% faster than Holocene geologic rates. In contrast, geodetic rates of ∼6–8 mm/yr along the Denali fault prior to the 2002 MW = 7.9 Denali earthquake are only approximately half as fast as the latest Pleistocene‐Holocene geologic rate of ∼12 mm/yr. In the third example where a sufficiently long pre‐earthquake geodetic time series exists, the geodetic and geologic rates along the 2001 MW = 7.8 Kokoxili rupture on the Kunlun fault are approximately equal at ∼11 mm/yr. These results are not readily explicable with extant earthquake‐cycle modeling, suggesting that they may instead be due to some combination of regional kinematic fault interactions, temporal variations in the strength of lithospheric‐scale shear zones, and/or variations in local relative plate motion rate. Whatever the exact causes of these variable behaviors, these observations indicate that either the ratio of geodetic to geologic rates before an earthquake may not be diagnostic of the time to the next earthquake, as predicted by many rheologically based geodynamic models of earthquake‐cycle behavior, or different behaviors characterize different fault systems in a manner that is not yet understood or predictable.
adjustedCurves: Estimating Confounder-Adjusted Survival Curves in R

Kaplan-Meier curves stratified by treatment allocation are the most popular way to depict causal effects in studies with right-censored time-to-event endpoints. If the treatment is randomly assigned and the sample size of the study is adequate, this method produces unbiased estimates of the population-averaged counterfactual survival curves. However, in the presence of confounding, this is no longer the case. Instead, specific methods that allow adjustment for confounding must be used. We present the adjustedCurves R package, which can be used to estimate and plot these confounder-adjusted survival curves using a variety of methods from the literature. It provides a convenient wrapper around existing R packages on the topic and adds additional methods and functionality on top of it, uniting the sometimes vastly different methods under one consistent framework. Among the additional features are the estimation of confidence intervals, confounder-adjusted restricted mean survival times and confounder-adjusted survival time quantiles. After giving a brief overview of the implemented methods, we illustrate the package using publicly available data from an observational study including 2982 breast cancer.
Grasp, See and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior

We focus on the task of unknown object rearrangement, where a robot is supposed to re-configure the objects into a desired goal configuration specified by an RGB-D image. Recent works explore unknown object rearrangement systems by incorporating learning-based perception modules. However, they are sensitive to perception error, and pay less attention to task-level performance. In this paper, we aim to develop an effective system for unknown object rearrangement amidst perception noise. We theoretically reveal the noisy perception impacts grasp and place in a decoupled way, and show such a decoupled structure is non-trivial to improve task optimality. We propose GSP, a dual-loop system with the decoupled structure as prior. For the inner loop, we learn an active seeing policy for self-confident object matching to improve the perception of place. For the outer loop, we learn a grasp policy aware of object matching and grasp capability guided by task-level rewards. We leverage the foundation model CLIP for object matching, policy learning and self-termination. A series of experiments indicate that GSP can conduct unknown object rearrangement with higher completion rate and less steps.
Classification Under Strategic Self-Selection

When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consider user actions that manifest as feature modifications, we study a novel setting in which users decide -- in response to the learned classifier -- whether to at all participate (or not). For learning approaches of increasing strategic awareness, we study the effects of self-selection on learning, and the implications of learning on the composition of the self-selected population. We then propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that both complement our analysis and demonstrate the utility of our approach.