Miracle-dz/UniFAR-FTUs huggingface dataset:
On deterministic context-free languages, multihead automata, and the power of an auxiliary pushdown store

A deterministic context-free language L0 is described which is log(n)-complete for the family of languages recognized by deterministic log(n)- tape bounded auxiliary pushdown automata in polynomial time. It follows that L0 is a “hardest” deterministic context-free language (DCFL), since all DCFL's are recognized in polynomial time by deterministic pushdown automata. L0 is, moreover, a simple precedence language and a simple LL(1) language. Thus the tape complexities of these proper subfamilies are essentially the same as the tape complexity of all DCFL's. We show that an auxiliary pushdown store does, in fact, add some power to some restricted families of log(n)-tape bounded Turing machines. The basic result is that every two-way 2k-head nondeterministic finite automation can be replaced by an equivalent two-way k-head nondeterministic pushdown automation. This indicates, also, that every 2k-head nondeterministic finite automation language can be recognized in 0(n3k) steps. Other results relate multihead automata classes with other multihead automata classes, with families recognized by log(n)-tape bounded Turing machines with restricted tape alphabets, and with time-bounded complexity classes.
Stable Isotope Fractionation Caused by Glycyl Radical Enzymes during Bacterial Degradation of Aromatic Compounds

ABSTRACT Stable isotope fractionation was studied during the degradation of m-xylene, o-xylene, m-cresol, and p-cresol with two pure cultures of sulfate-reducing bacteria. Degradation of all four compounds is initiated by a fumarate addition reaction by a glycyl radical enzyme, analogous to the well-studied benzylsuccinate synthase reaction in toluene degradation. The extent of stable carbon isotope fractionation caused by these radical-type reactions was between enrichment factors (ε) of −1.5 and −3.9, which is in the same order of magnitude as data provided before for anaerobic toluene degradation. Based on our results, an analysis of isotope fractionation should be applicable for the evaluation of in situ bioremediation of all contaminants degraded by glycyl radical enzyme mechanisms that are smaller than 14 carbon atoms. In order to compare carbon isotope fractionations upon the degradation of various substrates whose numbers of carbon atoms differ, intrinsic ε (εintrinsic) were calculated. A comparison of εintrinsic at the single carbon atoms of the molecule where the benzylsuccinate synthase reaction took place with compound-specific ε elucidated that both varied on average to the same extent. Despite variations during the degradation of different substrates, the range of ε found for glycyl radical reactions was reasonably narrow to propose that rough estimates of biodegradation in situ might be given by using an average ε if no fractionation factor is available for single compounds.
Fast 3 D Perception for Collision Avoidance and SLAM in Domestic Environments 1 Fast 3 D Perception for Collision Avoidance and SLAM in Domestic Environments

Autonomous service robots that assist in housekeeping, serve as butlers, guide visitors through exhibitions in museums and trade fairs, or provide care to elderly and disabled people could substantially ease everyday life for many people and present an enormous economic potential (Haegele et al., 2001; Pollack et al., 2002; Siegwart et al., 2003). Moreover, regarding the aging society in most industrialized countries the application of service robots in (elderly) health care might not only be helpful but necessary in the future. However, these service robots face the challenging task of operating in real-world indoor and domestic environments. Domestic environments tend to be cluttered, dynamic and populated by humans and domestic animals. In order to adequately react to sudden dynamic changes and avoid collisions, these robots need to be able to constantly acquire and process, in real-time, information about their environment. Furthermore, in order to act in a goal-directed manner, plan actions and navigate effectively, autonomous mobile robots need an internal representation or map of their environment. Nature and complexity of these representations highly depend on the robot’s task and workspace. When operating in preliminary unknown environments, e.g., when it is unfeasible (or simply uncomfortable) to manually model the environment beforehand, the robot needs to construct an internal environment model on its own. Moreover, in dynamic environments the robot further needs to be able to continuously acquire and integrate new sensory information to update the internal environment model in regions where changes have taken place. As integrating new information into the model (mapping) requires knowledge about the robot’s pose (position and orientation in the environment) and determining the robot’s pose requires a map of the environment, these two problems need to be considered jointly and the problem 4
Using Recommendation Systems to Adapt Gameplay

Recommendation systems are key components in many Web applications (Amazon, Netflix, eHarmony). Each system gathers user input, such as the products they buy, and searches for patterns in order to determine user preferences and tastes. These preferences are then used to recommend other content that a user may enjoy. Games on the other hand are often designed with a one-size-fits-all approach not taking player preferences into account. However there is a growing interest in both the games industry and game research communities to begin incorporating systems that can adapt, or alter how the game functions, to specific players. This article examines how Web application recommendation systems compare to current games that adapt their gameplay to specific players. The comparison shows that current games do not use recommendation methods that are data intensive or collaborative when adapting to players. Design suggestions are offered within this manuscript for how game developers can benefit from incorporating the lesser used recommendation methods.
Linkage in the chain of care: a grounded theory of professional cooperation between antenatal care, postpartum care and child health care

Purpose The purpose of this article is to present a Swedish study exploring health care professionals’ cooperation in the chain of care for expectant and new parents between antenatal care (AC), postpartum care (PC) and child health care (CHC). Furthermore, the rationale was to conceptualise barriers and facilitators of cooperation in order to generate a comprehensive theoretical model which may explain variations in the care providers’ experiences. Methods Thirty-two midwives and CHC nurses were interviewed in five focus group – and two individual interviews in a suburb of a large Swedish city. Grounded Theory was applied as the research methodology. Results One core category was discerned: linkage in the chain of care, including six categories with subcategories. Despite the fact that midwives as well as CHC nurses have common visions about linkage, cooperation is not achieved because of interacting barriers that have different influences on the three links in the chain. Conclusions Barriers to linkage are lack of professional gain, link perspective and first or middle position in the chain, while facilitators are chain perspective, professional gain and last position in the chain. As the last link, CHC nurses promote a linkage most strongly and have the greatest gain from such linking.
Practical and Efficient Attribute-Based Encryption with Constant-Size Ciphertexts in Outsourced Verifiable Computation

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the $d$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using $\textsf{Python}$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.
OpAmp design for lock-in amplifiers in portable sensing systems

This paper presents a 1.2 V low-power rail-to-rail class AB operational amplifier (OpAmp) suitable for integrated lock-in amplifiers. The proposed OpAmp has been designed in a standard 0.18 μm CMOS technology. For a 1.2 V single supply and 81 μW power consumption, simulations shows a 86 dB open loop gain, 56o phase margin, 17 MHz unity gain frequency with 1 pF load and 97 dB CMRR. Adaptive biasing provides 33 V/μs slew-rate for a 1 pF capacitive load. A compact and reliable lock-in amplifier (LIA) has been designed using the proposed circuit. Post-layout results for noisy signals confirm the capability of the LIA to effectively recover information from signal-to-noise ratios down to -26 dB with errors below 4.2% up to 50 kHz and a power consumption of only 675 μW.
Behavioral targeting with social regularization

Behavioral targeting (BT) is a valuable tool for online advertising. In this paper, we study a new problem of incorporating social information into traditional behavior targeting models. Specifically, we present a social regularization based Poisson regression framework for behavior targeting. Based on the observation that social information can be diverse and competing, we furthermore present two specific social regularization terms: the average-based social regularization term and the individual-based social regularization term. To validate the effectiveness of the proposed models, we use the KDDCUP'12 behavior targeting data, issued by the Tecent company in China, as the test bed. The results demonstrate that the proposed models, by incorporating additional social network information, can achieve at least 5% improvement compared to the traditional Poisson regression based model from the CTR lift viewpoint, especially when the historical behavior data is sparse and insufficient.
The Effect of Relational Mobility and Social Attractiveness on Psychological Well-Being

Previous research has indicated that in cultures and social environments where individuals are presented with many opportunities to form new relationships (i.e., environments high in relational mobility), individuals tend to form relationships with others like themselves. This tendency can be caused by two different mechanisms: First, the tendency of people to form relationships with similar others may be caused by the similarity-attraction effect, whereby individuals prefer to interact with people like themselves. High relational mobility therefore increases opportunities for individuals to maximize their preference for similar others. However, increased similarity between relationship partners can also be caused by assortment effects, whereby people who possess desirable characteristics form relationships with other desirable individuals, leaving those with less desirable characteristics to form relationships with less desirable partners. This thesis examined how relational mobility might influence the wellbeing of individuals who may be considered less desirable to others. Two exploratory studies examined the relation between attributes associated with lower social attractiveness on psychological well being, and how this relation changed as a function of relational mobility. The first study examined the impact of relational mobility on depression among American undergraduates with higher or lower amounts of social skills, and the second study examined the well being of individuals as a function of their social skills and disability statues. Overall, the results of both studies suggest that individuals who possess characteristics associated with lower social attractiveness (having a disability, or having lower social skills) tended to have higher levels of psychological well being when they perceived relational mobility to be high.
Instructional Scaffolding in Online Learning Environment: A Meta-analysis

Instructional scaffolding or simply known as scaffolding in education is defined as a guidance or support from teachers, instructors or other knowledgeable persons that facilitate students to achieve their goals in learning. Conceptually, scaffolding means providing students with instructions during the early stage of learning before slowly shifting the responsibility to them as they develop their own understanding and skills. As technology extends learning from classroom to learning communities, same goes to the concept of scaffolding. The scaffolding is no longer implemented via face to-face instruction that literally exists between a teacher and students in a classroom. Currently, the form of instructions that emerges between teachers and students is mediated through technology and the learning communities exist in the online settings. Thus, it is important to acknowledge the suitable form of support required for the students, especially in an online learning environment. The aim of this meta-analysis is to investigate the types of scaffolding that could be implemented in an online learning environment together with its potential in validating students' success in an online learning setting.
The Radio Properties of Composite LINER/H II Galaxies

Arcsecond-resolution VLA observations—newly obtained as well as published—of 40 nearby galaxies are discussed, completing a study of the radio properties of a magnitude-limited sample of nearby galaxies of the composite LINER/H II type. Our results reveal an overall detection rate of at least 25% active galactic nucleus (AGN) candidates among these composite sources. The general properties of these AGN candidates, as compared to non-AGN composite sources and H II galaxies, are discussed.
Searching for Arbitrary Information in the WWW : the Fish − Search for Mosaic

While the World−Wide Web (WWW, or Web for short) offers an incredibly rich base of information, organized as a hypertext, it does not provide a uniform and efficient way to retrieve specific information, based on user−defined search−criteria. Two types of search tools have been developed for the WWW: Gateways, offering (limited) search operations on small or large parts of the WWW, using a pre−compiled database. These databases are often built by an automated Web scanner (a "robot"). A client−based search tool that does automated navigation, thereby working more or less like a browsing user, but much faster and following an optimized strategy. This paper highlights the properties, implementation and possible future developments of a client−based search tool called the fish−search, and compares it to other approaches. The fish−search, implemented on top of Mosaic for X, offers an open−ended selection of search criteria. It allows the search to start from the "current" document or from the documents in the user’s hotlist. The future of client−based searching will depend heavily on assistance from WWW servers. The fish−search algorithm is currently being used to search by navigating through individual WWW documents. Substantial speedup and reduction of network resource consumption will become possible when the same search algorithm is converted to navigate through a Web of servers instead of a Web of documents. In order to implement such a search algorithm, WWW servers need to offer a service similar to that provided by GlimpseHTTP.
Neural Activation during Anticipation of Near Pain-Threshold Stimulation among the Pain-Fearful

Fear of pain (FOP) can increase risk for chronic pain and disability but little is known about corresponding neural responses in anticipation of potential pain. In this study, more (10 women, 6 men) and less (7 women, 6 men) pain-fearful groups underwent whole-brain functional magnetic resonance imaging (fMRI) during anticipation of near pain-threshold stimulation. Groups did not differ in the proportion of stimuli judged to be painful but pain-fearful participants reported significantly more state fear prior to stimulus exposure. Within the entire sample, stronger activation was found in several pain perception regions (e.g., bilateral insula, midcingulate cortex (MCC), thalamus, superior frontal gyrus) and visual areas linked to decoding stimulus valences (inferior orbital cortex) during anticipation of “painful” stimuli. Between groups and correlation analyses indicated pain-fearful participants experienced comparatively more activity in regions implicated in evaluating potential threats and processing negative emotions during anticipation (i.e., MCC, mid occipital cortex, superior temporal pole), though group differences were not apparent in most so-called “pain matrix” regions. In sum, trait- and task-based FOP is associated with enhanced responsiveness in regions involved in threat processing and negative affect during anticipation of potentially painful stimulation.
Pings and Pongs : Basics and Applications of the BEAMER Acoustic Sensor System

Studies have shown that the deep marine biosphere harbors a significant amount of biomass and plays an important role in global biogeochemical cycles. To facilitate studies of microbial communities residing in marine sediments, a number of protocols have been designed to separate the cells from the sediment matrix. A separation protocol currently being developed has been shown to successfully remove up to 70% of the cells from the sediment. In order for a cell separation protocol to provide an accurate representation of microbial communities, it is necessary that the technique remove all phylotypes without bias. Our study uses molecular techniques to provide a qualitative comparison of genetic diversity in cells that have been separated from a sediment sample and cells that are left in sediments after separation. 16S rRNA genes in DNA extracted from these samples are amplified through polymerase chain reaction and then compared through denaturing gradient gel electrophoresis. Resulting band patterns are compared between samples to determine if the genetic diversity of the separated cells has been altered by the cell separation protocol. This will allow us to determine whether or not the protocol provides a true random sample of the microbial community in sediment samples.
Aberrant Neuregulin 1 Signaling in Amyotrophic Lateral Sclerosis

Abstract Neuregulin 1 (NRG1) is a neuron-derived trophic molecule that supports axoglial and neuromuscular development through several alternatively spliced isoforms; its possible role in the pathogenesis and progression of amyotrophic lateral sclerosis (ALS) is not known. We analyzed the relationship of NRG1 isoform expression to glial cell activation and motor neuron loss in spinal cords of ALS patients and during disease progression in the superoxide dismutase 1 (SOD1) ALS mouse model. Microgliosis, astrocytosis, and motor neuron loss were observed in the ventral horns in ALS patients and were increased in SOD1 mice along with disease progression. Type III (membrane-bound) NRG1 expression was reduced in parallel with motor neuron loss, but Type I (secreted) NRG1 expression was increased and was associated with glial activation. Increased NRG1 receptor activation was observed on activated microglia in both ALS patients and in SOD1 mice. This activation was observed at the time of disease onset and before upregulation of NRG1 gene expression in the mice. The downregulation of membrane-bound Type III NRG1 forms may reflect motor neuron loss, but increased signaling by secreted-type NRG1 isoforms could contribute to disease pathogenesis through glial cell activation. NRG1 might, therefore, represent a novel therapeutic target against disease progression in ALS.
Group Adaptive Software Return Value Prediction Sable Technical Report No . 2010-3

Return value prediction (RVP) is a technique for guessing the return value from a function before it actually completes, enabling a number of program optimizations and analyses. However, despite the apparent usefulness, RVP and value prediction in general have seen limited uptake in practice. Hardware proposals have been successful in terms of speed and prediction accuracy, but the cost of dedicated circuitry is high, the available memory for prediction is low, and the flexibility is negligible. Software solutions are inherently much more flexible, but can only achieve high accuracies in exchange for reduced speed and increased memory consumption. In this work we first express many different existing prediction strategies in a unification framework, using it as the basis for a software implementation. We then explore an adaptive software RVP design that relies on simple object-orientation in a hybrid predictor. It allocates predictors on a per-callsite basis instead of globally, and frees the resources associated with unused hybrid sub-predictors after an initial warmup period. We find that these techniques dramatically improve speed and reduce memory consumption while maintaining high prediction accuracy.
Tensor machines for learning target-specific polynomial features

Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy. Regrettably, because random features are target-agnostic, typically thousands of such features are necessary to achieve acceptable accuracies. In this work, we consider the problem of learning a small number of explicit polynomial features. Our approach, named Tensor Machines, finds a parsimonious set of features by optimizing over the hypothesis class introduced by Kar and Karnick for random feature maps in a target-specific manner. Exploiting a natural connection between polynomials and tensors, we provide bounds on the generalization error of Tensor Machines. Empirically, Tensor Machines behave favorably on several real-world datasets compared to other state-of-the-art techniques for learning polynomial features, and deliver significantly more parsimonious models.
Bi-criteria Velocity Minimization of Robot Manipulators Using a Linear Variational Inequalities-Based Primal-Dual Neural Network and PUMA560 Example

In this paper, to diminish discontinuity points arising in the infinity-norm velocity minimization scheme, a bi-criteria velocity minimization scheme is presented based on a new neural network solver (i.e., a primal-dual neural network based on linear variational inequalities (LVI)). Such a kinematic control scheme of redundant manipulators can incorporate joint physical limits such as joint limits and joint velocity limits simultaneously. Moreover, the presented kinematic control scheme can be formulated as a quadratic programming (QP) problem. As a real-time QP solver, the LVI-based primal-dual neural network is established with a simple piecewise linear structure and higher computational efficiency. Computer simulations performed based on a PUMA560 manipulator are presented to illustrate the validity and advantages of such a bi-criteria neural control scheme for redundant robots.