Miracle-dz/UniFAR-FTUs huggingface dataset:
O(cid:31)ine narrowing-driven partial evaluation

Narrowing-driven partial evaluation (NPE) is a powerful technique for the specialization of rewrite systems. Although it gives good re-sults on small programs, it does not scale up well to realistic problems (e.g., interpreter specialization). In this work, we introduce a faster partial evaluation scheme by ensuring the termination of the process o(cid:31)ine . For this purpose, we (cid:28)rst characterize a class of rewrite systems which are quasi-terminating , i.e., the computations performed with needed narrowing(cid:22)the symbolic computation mechanism of NPE(cid:22)only contain (cid:28)nitely many different terms (and, thus, partial evaluation terminates). Since this class is quite restrictive, we introduce an annotation algorithm for a broader class of systems so that they behave like quasi-terminating rewrite systems w.r. t. a proposed extension of needed narrowing.
Blue Laws, DUIs and Alcohol-Related Accidents: Regression Discontinuity Evidence from Colorado

On July 1st, 2008, Colorado Blue Laws banning Sunday packaged liquor sale were repealed. We estimate the effect of this policy change on alcohol-related accidents and traffic citations in a regression discontinuity design. Across specifications, we find no evidence that the repeal of Colorado Blue Laws increased or decreased alcohol-related accidents and alcohol-related traffic citations. This stands in sharp contrast with previous studies that found a 30% increase or more in alcohol-related accidents on Sundays following the repeal of similar laws in New Mexico.
Extreme Learning ANFIS for classification problems

This paper compares Extreme Learning ANFIS (ELANFIS) with conventional ANFIS for classification problems. ELANFIS is a hybrid Fuzzy System based on Extreme Learning Machines. It combines the linguistic knowledge representation of a Fuzzy System with the fast learning speed of ELMs. This paper also proposes the use of a zero order ELANFIS for classification tasks and compares it with first order Fuzzy Systems. The results show that zero order ELANFIS gives lesser classification error with faster learning speed as compared to the mentioned methods since the number of parameters to tune is lesser.
Beyond Adaptation: Decolonizing Approaches to Coping With Oppression

How should one respond to racial oppression? Conventional prescriptions of mainstream social psychological science emphasize the idea of coping with oppression—whether via emotional management strategies that emphasize denial or disengagement; problem-focused strategies that emphasize compensation, self-efficacy, or skills training; or collective strategies that emphasize emotional support—in ways that promote adaptation to, rather than transformation of, oppressive social structures. Following a brief review of the literature on coping with racism and oppression, we present an alternative model rooted in perspectives of liberation psychology (Martin-Baro, 1994). This decolonial approach emphasizes critical consciousness (rather than cultivated ignorance) of racial oppression, a focus on de-ideologization (rather than legitimation) of status quo realities, and illumination of models of identification conducive to collective action. Whereas the standard approach to coping with oppression may ultimately both reinforce and reproduce systems of domination, we propose a decolonial approach to racism perception as a more effective strategy for enduring prosperity and well-being.
Opportunities and pitfalls of multi-core scaling using hardware transaction memory

Hardware transactional memory, which holds the promise to simplify and scale up multicore synchronization, has recently become available in main stream processors in the form of Intel's restricted transactional memory (RTM). Will RTM be a panacea for multi-core scaling? This paper tries to shed some light on this question by studying the performance scalability of a concurrent skip list using competing synchronization techniques, including fine-grained locking, lock-free and RTM (using both Intel's RTM emulator and a real RTM machine). Our experience suggests that RTM indeed simplifies the implementation, however, a lot of care must be taken to get good performance. Specifically, to avoid excessive aborts due to RTM capacity miss or conflicts, programmers should move memory allocation/deallocation out of RTM region, tuning fallback functions, and use compiler optimization.
Scheduling grid tasks under uncertain demands

The uncertainty of the demands of grid applications can cause unpredicted performance and, consequently, can make ineffective schedules derived for target demand values. To produce effective results, schedulers need to take into account the difficulty in estimating the demands of applications. In this paper, a scheduler based on fuzzy optimization is proposed to deal with such uncertainties. It is shown, via numerical results, that the proposed scheduler presents advantages when compared to classical schedulers.
EE 368 Project : Face Detection in Color Images

We present in this report an approach to automatic detection of human faces in color images. The proposed approach consists of three parts: a human skin segmentation to identify probable regions corresponding to human faces; adaptive shape analysis to separate isolated human faces from initial segmentation results; and a view-based face detection to further identify the location of each human face. The human skin segmentation employs a model-based approach to represent and differentiate the background colors and skin colors. To further refine the initial segmentation results, the adaptive shape analysis uses a series of morphological operations based on the prior shape knowledge of upright human faces. The view-based face detection is constructed based on principal component analysis and neural network classification. Our face detector has been applied to several test images, and satisfactory results have been obtained. Index Terms – Human skin segmentation, Gaussian mixture model, Adaptive shape analysis, Principal component analysis, and Neural network.
Community Structure and Function of Amphibian Skin Microbes: An Experiment with Bullfrogs Exposed to a Chytrid Fungus

The vertebrate microbiome contributes to disease resistance, but few experiments have examined the link between microbiome community structure and disease resistance functions. Chytridiomycosis, a major cause of amphibian population declines, is a skin disease caused by the fungus, Batrachochytrium dendrobatidis (Bd). In a factorial experiment, bullfrog skin microbiota was reduced with antibiotics, augmented with an anti-Bd bacterial isolate (Janthinobacterium lividum), or unmanipulated, and individuals were then either exposed or not exposed to Bd. We found that the microbial community structure of individual frogs prior to Bd exposure influenced Bd infection intensity one week following exposure, which, in turn, was negatively correlated with proportional growth during the experiment. Microbial community structure and function differed among unmanipulated, antibiotic-treated, and augmented frogs only when frogs were exposed to Bd. Bd is a selective force on microbial community structure and function, and beneficial states of microbial community structure may serve to limit the impacts of infection.
Can threshold networks be trained directly?

Neural networks with threshold activation functions are highly desirable because of the ease of hardware implementation. However, the popular gradient-based learning algorithms cannot be directly used to train these networks as the threshold functions are nondifferentiable. Methods available in the literature mainly focus on approximating the threshold activation functions by using sigmoid functions. In this paper, we show theoretically that the recently developed extreme learning machine (ELM) algorithm can be used to train the neural networks with threshold functions directly instead of approximating them with sigmoid functions. Experimental results based on real-world benchmark regression problems demonstrate that the generalization performance obtained by ELM is better than other algorithms used in threshold networks. Also, the ELM method does not need control variables (manually tuned parameters) and is much faster.
ULTRA: A Multi-lingual Machine Translator

ULTRA (Universal Language TRAnslator) is a multilingual, interlingual machine translation system currently under development at the Computing Research Laboratory at New Mexico State University. It translates between five languages (Chinese, English, German, Japanese, Spanish) with vocabularies in each language based on approximately 10,000 word senses. The major design criteria are that the system be robust and general purpose with simple to use utilities for customization to suit the needs of particular users. This paper describes the central characteristics of the system: the intermediate representation, the language components, semantic and pragmatic processes, and supporting lexical entry tools.
Design And Implementation Of An Island-Based File System

Reliability, availability and scalability are major concerns in the design of distributed file systems. We have built an island-based file system (IFS) called Archipelago to solve these problems by failure isolation and low-cost consistency maintenance. The building blocks of IFS are smaller self-contained file servers called islands. The main idea underlying island-based design is the one-island principle: as many operations as possible should involve exactly one island. The oneisland principle improves partial reliability and availability because each island can function independently of other islands’ failures. It allows IFS to scale efficiently with the system and workload sizes because consistency across islands can be maintained at a low cost. The data distribution strategies in existing file systems cannot satisfy the one-island principle without sacrificing load balance and scalability. We designed a new strategy in which data is distributed to islands at directory granularity by hashing the pathnames of directories. Certain metadata is replicated across islands in such a way that islands are self-contained and the cost for maintaining consistency across replicas is minimized. We evaluated the data distribution strategy in IFS by statistical analysis of the access patterns and contents of existing file systems in use. We studied partial availability, load balance, replication cost and consistency cost in web access logs, UNIX file system call traces, snapshots of file system contents, and Windows NT file access traces. The results confirmed the assumptions we made in the design. In addition, we compared data loss of IFS and typical non-IFS in case of partial failures in analytic models. The IFS model has a significantly lower data loss ratio than non-IFS, at the cost of replicating a small amount of metadata. We designed three protocols in Archipelago, the rebalance, consistency and recovery protocols, to make the island-based design a viable solution. We have implemented Archipelago on a cluster of PCs running Windows NT 4.0 connected by Ethernet. The consistency and recovery protocols are tested with randomized failure injections. The performance measured in micro benchmarks and operation mixes shows little overhead of the consistency protocol on oneisland operations; in one case, the speedup with 16 islands achieves 98.3% efficiency. A trace-driven study of the online reconfiguration of a web server running on Archipelago shows that data migration in the rebalance protocol is made transparent to the web server and imposes a performance penalty of only 4.5%.
Inflation, inflation uncertainty, and Markov regime switching heteroskedasticity: Evidence from European countries

We use a Markov regime-switching heteroskedasticity model in order to examine the association between inflation and inflation uncertainty in four European countries over the last forty years. This approach allows for regime shifts in both the mean and variance of inflation in order to assess the association between inflation and its uncertainty in short and long horizons. We find that this association differs (i) between transitory and permanent shocks to inflation and (ii) across countries. In particular, the association is positive or zero for transitory shocks and negative or zero for permanent shocks. Hence, Friedman's belief that inflation is positively associated with inflation uncertainty is only partially supported in this study, i.e., by short-run inflation uncertainty
Vinculin modulation of paxillin–FAK interactions regulates ERK to control survival and motility

Cells lacking vinculin are highly metastatic and motile. The reasons for this finding have remained unclear. Both enhanced survival and motility are critical to metastasis. Here, we show that vinculin null (vin−/−) cells and cells expressing a vinculin Y822F mutant have increased survival due to up-regulated activity of extracellular signal–regulated kinase (ERK). This increase is shown to result from vinculin's modulation of paxillin–FAK interactions. A vinculin fragment (amino acids 811–1066) containing the paxillin binding site restored apoptosis and suppressed ERK activity in vin−/− cells. Both vinY822F and vin−/− cells exhibit increased interaction between paxillin and focal adhesion kinase (FAK) and increased paxillin and FAK phosphorylation. Transfection with paxillin Y31FY118F dominant-negative mutant in these cells inhibits ERK activation and restores apoptosis. The enhanced motility of vin−/− and vinY822F cells is also shown to be due to a similar mechanism. Thus, vinculin regulates survival and motility via ERK by controlling the accessibility of paxillin for FAK interaction.
Crowdsourcing Processes: A Survey of Approaches and Opportunities

This article makes a case for crowdsourcing approaches that are able to manage crowdsourcing processes -- that is, crowdsourcing scenarios that go beyond the mere outsourcing of multiple instances of a micro-task and instead require the coordination of multiple different crowd and machine tasks. It introduces the necessary background and terminology, identifies a set of analysis dimensions, and surveys state-of-the-art tools, highlighting strong and weak aspects and promising future research and development directions.
Combinatorial reverse auction algorithm for group buying

Group buying is an important business model. From the perspective of buyers, quantity based discounts provide a huge incentive to form coalitions and take advantage of lower prices without ordering more than their actual demand. By forming a coalition, buyers can also improve their bargaining power and negotiate more advantageously with sellers to purchase at a lower price. In this paper, we study coalition formation problem in group buying. We formulate this problem as a combinatorial reverse auction with multiple buyers and multiple sellers. We propose the concept of proxy buyer to deal with this problem. The proxy buyer consolidates the demands from the buyers and then holds a reverse auction to try to obtain the goods from a set of sellers who can provide the goods. The main results include: (1) a problem formulation for the combinatorial reverse auction problem; (2) a solution methodology based on Lagrangian relaxation and (3) analysis of numerical results based on our solution algorithms.
Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).
Necessary and Suucient Conditions for Robust Gain Scheduling

Recent results in the design of controllers for parameter dependent systems are extended to systems with plant uncertainty. The solution takes the form of an aane matrix inequality (AMI), which is both a necessary and suucient condition for the posed problem to have a solution. The results in this paper may be used for the design of gain scheduled controllers for a class of uncertain systems.
Sparse Space Replica Based Image Reconstruction via Cartesian and Spiral Sampling Strategies

In this study, a replica based image reconstruction is designed to provide high-quality reconstructions from very sparse space data. The problem of reconstructing an image from its unequal frequency samples take place in many applications. Images are observed on a spherical manifold, where one seeks to get an improved unidentified image from linear capacity, which is noisy, imperfect through a convolution procedure. Existing framework for Total Variation (TV) inpainting on the sphere includes fast methods to render the inpainting problem computationally practicable at high-resolution. In recent times, a new sampling theorem on the sphere developed, reduces the necessary number of samples by a feature of two for equiangular sampling schemes but the image that is not extremely sparse in its gradient. Total Variation (TV) inpainting fails to recover signals in the spatial domain directly with improved dimensionality signal. The regularization behavior is explained by using the theory of Lagrangian multiplier but space limitation fails to discover effective connection. To overcome these issues, Replica based Image Reconstruction (RIR) is developed in this study. RIR presents reconstruction results using both Cartesian and spiral sampling strategies using data simulated from a real acquisition to improved dimensionality signal in the spatial domain directly. RIR combined with the Global Reconstruction Constraint to remove the noisy imperfect area and highly sparse in its gradient. The proposed RIR method leads to significant improvements in SNR with very sparse space for effective analytical connection result. Moreover, the gain in SNR is traded for fewer space samples. Experimental evaluation is performed on the fMRI Data Set for Visual Image Reconstruction. RIR method performance is compared against the exiting TV framework in terms of execution time, Noisy area in SNR, accuracy rate, computational complexity, mean relative error and image dimensionality enhancement.
Excision of bacteriophage lambda from a site in the arabinose B gene

A lambda lysogen with the prophage inserted into the arabinose B gene of Escherichia coli strain K-12 has been prepared. Induction of the phage from this lysogen yields viable phage at a frequency 4 X 10(-6) that found for induction of lysogens with phage inserted at the normal attachment site. Over 30% of the phage particles induced from the insertion in ara are arabinose-transducing phage. The excision end points of 62 independently isolated, nondefective araC-transducing phage containing less than the entire araC gene were genetically determined and were found to be randomly distributed through the araC gene. The amount of arabinose deoxyribonucleic acid contained on four selected transducing phage was determined by electron microscopy of deoxyribonucleic acid heteroduplexes, providing a physical map of the araC gene. The efficiency with which these phage transduce araC and araB point mutations was found to be approximately proportional to the homology length available for recombination.
A high speed low power modulo 2n+1 multiplier design using carbon-nanotube technology

Modulo 2n+1 multiplier is one of the critical components in the area of digital signal processing, residue arithmetic, and data encryption that demand high-speed and low-power operation. In this paper, a new circuit implementation of a high-speed low-power modulo 2n+1 multiplier is proposed. It has three major stages: partial product generation stage, partial product reduction stage, and the final adder stage. The proposed structure introduces a new MUX-based compressor in the partial product reduction stage to reduce power and increase speed, and in the final adder stage, the Sparse-tree-based inverted end-around-carry adder reduces the number of critical path circuit blocks, also avoids wire interconnection problem. The proposed multiplier is implemented using both 32nm CNTFET (Carbon-Nanotube FET) and bulk CMOS technology for performance comparison. The CNTFET-based design dramatically decreases the PDP (Power Delay Product) of the circuit. The simulation results demonstrate that the power consumption of CNTFET-based multiplier is at average 5.72 times less than its CMOS counterpart, while the PDP of CNTFET is 94 times less than the CMOS one.
Automatic translation of support verb constructions

M. Gross (1981) calls such verbs 'support verbs', and I shall adopt his terminologLv. These verbs exhibit many interesting properties which have been studied systematically for several French support verbs: faire (make), avoir (have), prendre (take), etre (be), etc. An examination of the results indicates that support verb,; must be taken into account in the parser and in the lexicon of a program of automatic translation. I shall discuss the problem in the context of a possible program for FrenchEnglish translation. In what follows, Vsup designates the support verb, and Nsup stands for the 'supported' noun associated with Vsup. NO and N2 designate the subject noun and the indirect object noun, respectively. The symbol '+ ' is used to indicate a choice.