Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving Security Games on Graphs via Marginal Probabilities

Security games involving the allocation of multiple security resources to defend multiple targets generally have an exponential number of pure strategies for the defender. One method that has been successful in addressing this computational issue is to instead directly compute the marginal probabilities with which the individual resources are assigned (first pursued by Kiekintveld et al. (2009)). However, in sufficiently general settings, there exist games where these marginal solutions are not implementable, that is, they do not correspond to any mixed strategy of the defender. In this paper, we examine security games where the defender tries to monitor the vertices of a graph, and we show how the type of graph, the type of schedules, and the type of defender resources affect the applicability of this approach. In some settings, we show the approach is applicable and give a polynomial-time algorithm for computing an optimal defender strategy; in other settings, we give counterexample games that demonstrate that the approach does not work, and prove NP-hardness results for computing an optimal defender strategy.
Semantic Memory

Semantic memory refers to our general world knowledge that encompasses memory for concepts, facts, and the meanings of words and other symbolic units that constitute formal communication systems such as language or math. In the classic hierarchical view of memory, declarative memory was subdivided into two independent modules: episodic memory, which is our autobiographical store of individual events, and semantic memory, which is our general store of abstracted knowledge. However, more recent theoretical accounts have greatly reduced the independence of these two memory systems, and episodic memory is typically viewed as a gateway to semantic memory accessed through the process of abstraction. Modern accounts view semantic memory as deeply rooted in sensorimotor experience, abstracted across many episodic memories to highlight the stable characteristics and mute the idiosyncratic ones. A great deal of research in neuroscience has focused on both how the brain creates semantic memories and what brain regions share the responsibility for storage and retrieval of semantic knowledge. These include many classic experiments that studied the behavior of individuals with brain damage and various types of semantic disorders but also more modern studies that employ neuroimaging techniques to study how the brain creates and stores semantic memories. Classically, semantic memory had been treated as a miscellaneous area of study for anything in declarative memory that was not clearly within the realm of episodic memory, and formal models of meaning in memory did not advance at the pace of models of episodic memory. However, recent developments in neural networks and corpus-based tools for modeling text have greatly increased the sophistication of models of semantic memory. There now exist several good computational accounts to explain how humans transform first-order experience with the world into deep semantic representations and how these representations are retrieved and used in meaning-based behavioral tasks. The purpose of this article is to provide the reader with the more salient publications, reviews, and themes of major advances in the various subfields of semantic memory over the past forty-five years. For more in-depth coverage, we refer the reader to the manuscripts in the General Overviews section.
GraphLog: a visual formalism for real life recursion

We present a query language called GraphLog, based on a graph representation of both data and queries. Queries are graph patterns. Edges in queries represent edges or paths in the database. Regular expressions are used to qualify these paths. We characterize the expressive power of the language and show that it is equivalent to stratified linear Datalog, first order logic with transitive closure, and non-deterministic logarithmic space (assuming ordering on the domain). The fact that the latter three classes coincide was not previously known. We show how GraphLog can be extended to incorporate aggregates and path summarization, and describe briefly our current prototype implementation.
Extracting Complex Biological Events with Rich Graph-Based Feature Sets

We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.
Improving action recognition by selection of features

In this paper we are interested in knowing, which features provide useful information for recognizing a gesture or an action, and how the set of selected characteristics impact the accuracy of detection. Then we define a large set of possible features, which are angles calculated from the joints of the skeleton provided by the kinect device. Our contribution is to propose an algorithm: Reduction of Feature Dimensions based on Standard Deviation (RFD-SD), to select the relevant set of features. In some cases we are evaluating if using a Genetic Algorithm (GA) contributes to improve the recognition. The experiments are carried out with two public datasets, to analyze the effect of using the complete set of proposed features, and the set of selected features. The obtained results suggest that action recognition improves by using the selected features.
Modeling and Manufacturing of Micromechanical RF Switch with Inductors

This study presents the simulation, fabrication and characterization of micromechanical radio frequency (RF) switch with micro inductors. The inductors are employed to enhance the characteristic of the RF switch. An equivalent circuit model is developed to simulate the performance of the RF switch. The behaviors of the micromechanical RF switch are simulated by the finite element method software, CoventorWare. The micromechanical RF switch is fabricated using the complementary metal oxide semiconductor (CMOS) and a post-process. The post-process employs a wet etching to etch the sacrificial layer, and to release the suspended structures of the RF switch. The structure of the RF switch contains a coplanar waveguide (CPW), a suspended membrane, eight springs and two inductors in series. Experimental results reveal that the insertion loss and isolation of the switch are 1.7 dB at 21 GHz and 19 dB at 21 GHz, respectively. The driving voltage of the switch is about 13 V.
Input selection for fast feature engineering

The application of machine learning to large datasets has become a vital component of many important and sophisticated software systems built today. Such trained systems are often based on supervised learning tasks that require features, signals extracted from the data that distill complicated raw data objects into a small number of salient values. A trained system's success depends substantially on the quality of its features. Unfortunately, feature engineering-the process of writing code that takes raw data objects as input and outputs feature vectors suitable for a machine learning algorithm-is a tedious, time-consuming experience. Because “big data” inputs are so diverse, feature engineering is often a trial-and-error process requiring many small, iterative code changes. Because the inputs are so large, each code change can involve a time-consuming data processing task (over each page in a Web crawl, for example). We introduce Zombie, a data-centric system that accelerates feature engineering through intelligent input selection, optimizing the “inner loop” of the feature engineering process. Our system yields feature evaluation speedups of up to 8× in some cases and reduces engineer wait times from 8 to 5 hours in others.
Exercising Your Brain: a Review of Human Brain Plasticity and Training-induced Learning the Problem of Skill Learning Training Regimens and General Learning Mechanisms of Learning Determinants of Learning and Learning Transfer Task Difficulty Motivation and Arousal

Human beings have an amazing capacity to learn new skills and adapt to new environments. However, several obstacles remain to be overcome in designing paradigms to broadly improve quality of life. Arguably, the most notable impediment to this goal is that learning tends to be quite specific to the trained regimen and does not transfer to even qualitatively similar tasks. This severely limits the potential benefits of learning to daily life. This review discusses training regimens that lead to the acquisition of new knowledge and strategies that can be used flexibly across a range of tasks and contexts. Possible characteristics of training regimens are proposed that may be responsible for augmented learning, including the manner in which task difficulty is progressed, the motivational state of the learner, and the type of feedback the training provides. When maximally implemented in rehabilitative paradigms, these characteristics may greatly increase the efficacy of training. The ability to learn, or, in other words, to acquire skills and alter behavior as a result of experience, is fundamentally important to the survival of all animals. Humans are certainly no exception; our incredible capacity to learn is certainly one of the principle variables explaining the success of our species. Whereas the term learning is extremely broad, and interesting work exists on its every aspect from the relatively simple (e.g. nonassociative learning) to the much more complex (e.g. social learning), this review focuses mainly on skill learning. This research, which has been predominantly carried out with young adults, provides compelling evidence for common principles of learning and learning transfer. We review this work and its implications for the design of training regimens in older adults. Skill learning is defined here as a change, typically an improvement , in perceptual, cognitive, or motor performance that comes about as a result of training and that persists for several weeks or months, thus distinguishing it from effects related to adaptation or other short-lived effects. Perhaps the most notable finding from the past century or more of research in the field is that humans have demonstrated some amount of learning in virtually every paradigm tested. Evidence for the principle that, given appropriate practice, humans improve on essentially every task, is prevalent throughout the psychology literature, ranging from the domain of perceptual learning (Fahle & Poggio, 2002) to that of motor learning (Karni et al., 1998) and cognitive training (Willis et al., 2006). An …
The Business Cycle Implications of Banks' Maturity Transformation

This paper develops a DSGE model in which banks use short-term deposits to provide firms with long-term credit. The demand for long-term credit arises because firms borrow in order to finance their capital stock which they only adjust at infrequent intervals. We show within a real business cycle framework that maturity transformation in the banking sector in general attenuates the output response to a technological shock. Implications of long-term nominal contracts are also examined in a New Keynesian version of the model, where we find that maturity transformation reduces the real effects of a monetary policy shock.
An Optical Source Catalog of the North Ecliptic Pole Region

We present a five-band (u*, g′, r′, i′, z′) optical photometry catalog of the sources in the north ecliptic pole (NEP) region based on deep observations made with MegaCam at CFHT. The source catalog covers about 2 square degree area centered at the NEP and reaches depths of about 26 mag for u*, g′, r′ bands, about 25 mag for i′ band, and about 24 mag for z′ band (4 σ detection over a 1'' aperture). The total number of cataloged sources brighter than r′ = 23 mag is about 56,000 including both point sources and extended sources. From the investigation of photometric properties using the color-magnitude diagrams and color-color diagrams, we have found that the colors of extended sources are mostly (u* - r′) < 3.0 and (g′ - z′) > 0.5. This can be used to separate the extended sources from the point sources reliably, even for the faint source domain where typical morphological classification schemes hardly work efficiently. We have derived an empirical color-redshift relation of the red sequence galaxies using the Sloan Digital Sky Survey data. By applying this relation to our photometry catalog and searching for any spatial overdensities, we have found two galaxy clusters and one nearby galaxy group.
The Foundations of Cryptography - Volume 2: Basic Applications

An electric heating device is provided for internally cooking and/or heating a meat product and an associated bakery product. The device is insertable into the center of the meat product so that the cooking thereof ensues from centrally of the product radially outwardly toward the outer surfaces thereof. The device comprises an elongated electric heating element encapsulated in a sheathing of electrical insulating material. A tubular outer metallic heat conductive shell surrounds the heating element and sheathing and a terminal extends out of each end of the shell. One end of the heating element is connected to one terminal and the other end is connected to the other terminal through a thermal switch in the outer shell and encapsulated within the electrical insulating material. One end of the outer shell is crimped to form a pair of diametrically opposed wing-like appendages to facilitate manipulation of the device during use. A combination manipulating handle and reaming tool is provided for handling the heating device when hot and for preparing the hole in the product for receiving the heating device.
Optimal cross-layer design for throughput maximisation of multi-class VSG CDMA in rayleigh fading channel

In this paper, we propose a joint throughput maximisation across the physical and call admission control (CAC) layer for multi-class VSG CDMA systems in a Rayleigh fading environment. The system model is firstly analysed and a new exact closed-form of the physical layer constraint is derived. We show that it is possible to transform the set of the exact physical layer constraints into the expression of the admission region. This facilitates formulating a linear cost function, which is modeled by semi-Markov decision processes (SMDPs), and results in an accurate cross-layer optimisation design. With the proposed technique, the maximum throughput is achieved by linear-programming approach as well as the QoS requirements of both layers are guaranteed. Based on the numerical results, the system performance is improved up to 50% in terms of throughput and blocking probability
Effects of alcohol, expectancies, and partner type on condom use in college males: Event‐level analyses

Sexually active heterosexual college males (N = 93) provided data on over 1,500 sexual encounters. Alcohol consumption, expectancies about alcohol's impact on condom use, and partner type each contributed to use of a condom. Partner type covaried with alcohol consumption and condom use. The men consumed significantly more alcohol with new partners, followed by casual partners, and then by regular partners. In contrast, they were more likely to use condoms with new partners than with casual or regular partners. Drinking alcohol decreased condom use, but only with casual partners. Expectancies about alcohol's disinhibiting sexual effects decreased condom use as well. These data suggest that alcohol consumption does decrease condom use, particularly with casual partners and when drinkers believe alcohol alters sexual disinhibition. Improving knowledge about HIV and other STD transmission in casual partners and challenging expectancies about alcohol as a sexual disinhibitor could help decrease the spread of HIV and other STDs.
The role of change- and stability-oriented place attachment in rural community resilience: a case study in south-west Scotland

Although recent research into resilience acknowledges the importance of attachment to place and claims that place is often the basis for community development, this relationship has not yet been explored in great detail. We research the link between people, place, and community resilience by examining and unravelling the role of place attachment in rural communities. We address the gap in current research by conducting interviews and eliciting mental maps from residents in two remote rural villages in Scotland. The relationship between people and place can be explained by heterogeneity in a community, and this is influenced by the different aspects of place attachment: social, personal, and environmental. We therefore introduce two types of place attachment: changeoriented and stability-oriented. Both types of place attachment influence individual and community resilience. Resilience and change-oriented place attachment can be restored after a disturbance and both are able to adapt to change. Stability-oriented place attachment, in contrast, can result in nostalgia and fear of loss or change of existing place aspects. However, this inclination towards protective behaviour can also enhance community resilience. Long-term residents and in-migrants each have different types of place attachment, and each can strengthen or weaken community resilience. Long-term residents are more likely to have nostalgic feelings which result in stability-oriented attachment, making them less able to adapt to change. We argue that length of residence is not a factor in resilience building as such, and therefore this paper makes an original contribution to the debate on resilience.
Are rotating strange quark stars good sources of gravitational waves

We study the viscosity driven (Jacobi-like) bar mode instability of rapidly rotating strange stars in general relativity. A triaxial, "bar shaped" compact star could be an efficient source of continuous wave gravitational radiation in the frequency range of the forthcoming interferometric detectors. We locate the secular instability point along several constant baryon mass sequences of uniformly rotating strange stars described by the MIT bag model. Contrary to neutron stars, strange stars with T/|W| (the ratio of the rotational kinetic energy to the absolute value of the gravitational potential energy) much lower than the corresponding value for the mass-shed limit can be secularly unstable to bar mode formation if shear viscosity is high enough to damp out any deviation from uniform rotation. The instability develops for a broad range of gravitational masses and rotational frequencies of strange quark stars. It imposes strong constraints on the lower limit of the frequency at the innermost stable circular orbit around rapidly rotating strange stars. The above results are robust for all linear self-bound equations of state assuming the growth time of the instability is faster than the damping timescale. We discuss astrophysical scenarios where triaxial instabilities (r-mode and viscosity driven instability) could be relevant in strange stars described by the standard MIT bag model of normal quark matter. Taking into account actual values of viscosities in strange quark matter and neglecting the magnetic field we show that Jacobi-like instability cannot develop in any astrophysicaly interesting temperature windows. The main result is that strange quark stars described by the MIT bag model can be accelerated to very high frequency in Low Mass X-ray binaries if the strange quark mass is ~ 200 MeV or higher.
ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks

This paper aims to classify and locate objects accurately and efficiently, without using bounding box annotations. It is challenging as objects in the wild could appear at arbitrary locations and in different scales. In this paper, we propose a novel classification architecture ProNet based on convolutional neural networks. It uses computationally efficient neural networks to propose image regions that are likely to contain objects, and applies more powerful but slower networks on the proposed regions. The basic building block is a multi-scale fully-convolutional network which assigns object confidence scores to boxes at different locations and scales. We show that such networks can be trained effectively using image-level annotations, and can be connected into cascades or trees for efficient object classification. ProNet outperforms previous state-of-the-art significantly on PASCAL VOC 2012 and MS COCO datasets for object classification and point-based localization.
Abundances and Kinematics of Field Halo and Disk Stars. I. Observational Data and Abundance Analysis

We describe observations and abundance analysis of a high-resolution, high signal-to-noise ratio survey of 168 stars, most of which are metal-poor dwarfs. We follow a self-consistent LTE analysis technique to determine the stellar parameters and abundances, and we estimate the effects of random and systematic uncertainties on the resulting abundances. Element-to-iron ratios are derived for key α-, odd-Z, Fe-peak, and r- and s-process elements. Effects of non-LTE on the analysis of Fe I lines are shown to be very small on average. Spectroscopically determined surface gravities are derived that are quite close to those obtained from Hipparcos parallaxes.
NICMOS measurements of the near-infrared background

We present preliminary results of an ESO-VLT large programme (AMAZE) aimed at determining the evolution of the mass-metallicity relation at z~3 by means of deep near-IR spectroscopy. Gas metallicities and stellar masses are measured for an initial sample of nine star forming galaxies at z~3.3. When compared with previous surveys, the mass-metallicity relation inferred at z~3.3 shows an evolution significantly stronger than observed at lower redshifts. There are also some indications that the metallicity evolution of low mass galaxies is stronger relative to high mass systems, an effect which can be considered as the chemical version of the galaxy downsizing. The mass-metallicity relation observed at z~3.3 is difficult to reconcile with the predictions of some hierarchical evolutionary models. We shortly discuss the possible implications of such discrepancies.
The trade-off between regularity and stability in Tikhonov regularization

When deriving rates of convergence for the approximations generated by the application of Tikhonov regularization to ill-posed operator equations, assumptions must be made about the nature of the stabilization (i.e., the choice of the seminorm in the Tikhonov regularization) and the regularity of the least squares solutions which one looks for. In fact, it is clear from works of Hegland, Engl and Neubauer and Natterer that, in terms of the rate of convergence, there is a trade-off between stabilization and regularity. It is this matter which is examined in this paper by means of the best-possible worst-error estimates. The results of this paper provide better estimates than those of Engl and Neubauer, and also include and extend the best possible rate derived by Natterer. The paper concludes with an application of these results to first-kind integral equations with smooth kernels.