Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Overcomplete Dictionaries with Application to Image Denoising

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Image denoising is an important application of this sparse model. However, whether the sparse representation can efficiently separate image and noise depends much on the atoms of dictionary can capture the structure of images. In this paper, we address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations in overcomplete dictionaries that is learned by the K singular value decomposition algorithm. Experiments show that the dictionary can describe the image content effectively and leads to an state-of-the-art denoising performance.
Three Cognitive Markers of Unconscious Semantic Activation

A “response window” technique is described and used to reliably demonstrate unconscious activation of meaning by subliminal (visually masked) words. Visually masked prime words were shown to influence judged meaning of following target words. This priming-effect marker was used to identify two additional markers of unconscious semantic activation: (i)the activation is very short-lived (the target word must occur within about 100 milliseconds of the subliminal prime); and (ii) unlike supraliminal prime-target pairs, a subliminal pair leaves no memory trace that can be observed in response to the next prime-target pair. Thus, unconscious semantic activation is shown to be a readily reproducible phenomenon but also very limited in the duration of its effect.
Highly Highly Detailed 3 D Modelling of Mayan Cultural Heritage Using an UAV

Highly detailed and highly accurate 3D models are indispensable tools for the management of cultural heritage, as well as for archaeological and anthropological research. Moreover, these digital 3D models should be combined with high resolution texture maps to facilitate the understanding of the heritage sites or parts of the heritage. The construction of these models requires the deliberate selection of a data acquisition platform and spatial measurement instruments. Different considerations on these issues are discussed in this paper, based on a case study at the site of Edzná, Mexico.
Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters

Business-critical workloads -- web servers, mail servers, app servers, etc. -- are increasingly hosted in virtualized data enters acting as Infrastructure-as-a-Service clouds (cloud data enters). Understanding how business-critical workloads demand and use resources is key in capacity sizing, in infrastructure operation and testing, and in application performance management. However, relatively little is currently known about these workloads, because the information is complex -- larges-scale, heterogeneous, shared-clusters -- and because datacenter operators remain reluctant to share such information. Moreover, the few operators that have shared data (e.g., Google and several supercomputing centers) have enabled studies in business intelligence (MapReduce), search, and scientific computing (HPC), but not in business-critical workloads. To alleviate this situation, in this work we conduct a comprehensive study of business-critical workloads hosted in cloud data enters. We collect two large-scale and long-term workload traces corresponding to requested and actually used resources in a distributed datacenter servicing business-critical workloads. We perform an in-depth analysis about workload traces. Our study sheds light into the workload of cloud data enters hosting business-critical workloads. The results of this work can be used as a basis to develop efficient resource management mechanisms for data enters. Moreover, the traces we released in this work can be used for workload verification, modelling and for evaluating resource scheduling policies, etc.
Multi-embedding and path approximation of metric spaces

Metric embeddings have become a frequent tool in the design of algorithms. The applicability is often dependent on how high the embedding's distortion is. For example embedding into ultrametrics (or arbitrary trees) requires linear distortion. Using probabilistic metric embeddings, the bound reduces to O(log nlog logn). Yet, the lower bound is still logarithmic.We make a step further in the direction of bypassing this difficulty. We define "multi-embeddings" of metric spaces where a point is mapped onto a set of points, while keeping the target metric being of polynomial size and preserving the distortion of paths. The distortion obtained with such multi-embeddings into ultrametrics is at most O(log Δ log log Δ) where Δ is the (normalized) diameter, and probabilistically O(log n log log log n). In particular, for expander graphs, we are able to obtain constant distortions embeddings into trees vs. the Ω(logn) lower bound for all previous notions of embeddings.We demonstrate the algorithmic application of the new embeddings by obtaining improvements for two well-known problems: group Steiner tree and metrical task systems.
Efficient Regret Bounds for Online Bid Optimisation in Budget-Limited Sponsored Search Auctions

We study the problem of an advertising agent who needs to intelligently distribute her budget across a sequence of online keyword bidding auctions. We assume the closing price of each auction is governed by the same unknown distribution, and study the problem of making provably optimal bidding decisions. Learning the distribution is done under censored observations, i.e. the closing price of an auction is revealed only if the bid we place is above it. We consider three algorithms, namely e—First, Greedy Product-Limit (GPL) and LuekerLearn, respectively, and we show that these algorithms provably achieve Hannan-consistency. In particular, we show that the regret bound of e—First is at most O(T⅔) with high probability. For the other two algorithms, we first prove that, by using a censored data distribution estimator proposed by Zeng [19], the empirical distribution of the closing market price converges in probability to its true distribution with a O(1/√t) rate, where t is the number of updates. Based on this result, we prove that both GPL and LuekerLearn achieve O(√T) regret bound with high probability. This in fact provides an affirmative answer to the research question raised in [1]. We also evaluate the abovementioned algorithms using real bidding data, and show that although GPL achieves the best performance on average (up to 90% of the optimal solution), its long running time may limit its suitability in practice. By contrast, LuekerLearn and e— First proposed in this paper achieve up to 85% of the optimal, but with an exponential reduction in computational complexity (a saving up to 95%, compared to GPL).
Harm reduction and the ethics of drug use: contemporary techniques of self-governance

ABSTRACT Drawing on Foucault’s conceptualisation of power, this paper examines public health as a distinctly modern regime of governance. An account of the historical regulation of drug use is traced in order to examine socio-historical shifts and lines of continuity in contemporary technologies of harm reduction. Using qualitative interview data, we examined practices of power in the context of contemporary drug use, including self-governance using techniques of monitoring, self-management and self-tracking. Participants’ accounts revealed that they were encouraged to self-govern their drug use through a variety of reformist technologies that are embedded in harm-reduction programs. It is argued that participants’ subjectivity is formed at the intersection of authoritative governance and self-governance, through ethical practices of the self which have emerged from disciplinary health practices, and incorporate the body as the site of power. We illustrate this by drawing a distinction between hygienist and sanitationist practices of public health. These governmental practices, which are embedded in public health programs, encourage people who use drugs to transform themselves into moral citizens, aligning their ethical practices with governing interests.
Multi-Channel Estimation of the Power Spectral Density of Noise for Mixtures of Non-Stationary Signals (第6回音声言語シンポジウム)

The proposed paper deals with the estimation of the power spectral density (PSD) of noise for mixtures of non-stationary wideband signals exploiting sparseness in the time-frequency domain. The proposed method is applied to realize a beamformer-plus-postfilter structure for noise-robust speech recognition. Experiments with a small-scale 4-sensor microphones array show that interfering speech and music can be suppressed by 13 dB for an average signal-to-noise ratio of 0 dB without impairing the quality of the desired speech signal and without introducing ’musical noise’. This noise suppression is reflected by the improvement of the word accuracy of a connected-digit speech recognizer.
Stream oriented interactions for highly distributed and disconnected environments

Distributed systems (DS) is concerned with the engineering of software and operating systems that are physically distributed and joined with a communication medium. Hence, coordination and interaction among software components is an inherent problem that faces the distributed systems community. Consequently, major early contributions in coordination and interaction engineering were sketched in this field. Multi-agent systems (MAS) paradigm considers a computational system as a coherent aggregation of autonomous entities, namely agents, that interact together and with the environment in order to achieve their design goals[Fer95, JW95]. In contrast with DS, early MAS systems consider only logical distribution of the control among the software components [EHRLR80, Len75, Hew76]. However, with advances in network technologies, the physical distribution of the entities becomes inescapable. Within MAS framework, interaction is considered as a fundamental concept just as agent, environment and organisation[Dem95]. Several works have contributed in the study of interaction by integrating in a single framework several results coming form different independent fields such as distributed systems and networking [Hol91], philosophical works such as the communicative acts theory developed by Austin [Aus62], cognitive science, and organisation theory [Mal87]. Among the interaction mechanisms that can be found in MAS systems, this paper focuses on explicit message based interactions. Hence, agents exchange explicit messages in order to achieve their design goals and to coordinate their actions. Messages are expressed using an agent communication language that is composed by the following component: a syntax, a
Climatic and density influences on recruitment in an irruptive population of Roosevelt elk

Abstract Current paradigms of ungulate population ecology recognize that density-dependent and independent mechanisms are not always mutually exclusive. Long-term data sets are necessary to assess the relative strength of each mechanism, especially when populations display irruptive dynamics. Using an 18-year time series of population abundances of Roosevelt elk (Cervus elaphus roosevelti) inhabiting Redwood National Park in northwestern California we assessed the influence of population size and climatic variation on elk recruitment and whether irruptive dynamics occurred. An information-theoretic model selection analysis indicated that abundance lagged 2 years and neither climatic factors nor a mix of abundance and climatic factors influenced elk recruitment. However, density-dependent recruitment differed between when the population was declining and when the population increased and then stabilized at an abundance lower than at the start of the decline. The population displayed irruptive dynamics.
Mechanisms by Which 17β-Estradiol (E2) Suppress Neuronal cox-2 Gene Expression

E2 attenuates inflammatory responses by suppressing expression of pro-inflammatory genes. Given that inflammation is increasingly being associated with neurodegenerative and psychiatric processes, we sought to elucidate mechanisms by which E2 down-regulates a component of an inflammatory response, cyclooxygenase– 2 (COX-2) expression. Although inflammatory processes in the brain are usually associated with microglia and astrocytes, we found that the COX-2 gene (cox-2) was expressed in a neuronal context, specifically in an amygdalar cell line (AR-5). Given that COX-2 has been reported to be in neurons in the brain, and that the amygdala is a site involved in neurodegenerative and neuropsychiatric processes, we investigated mechanisms by which E2 could down-regulate cox-2 expression in the AR-5 line. These cells express estrogen receptors alpha (ERα) and beta (ERβ), and as shown here cox-2. At the level of RNA, E2 and the ERβ selective ligand diarylpropionitrile (DPN) both attenuated gene expression, whereas the ERα selective ligand propyl pyrazole triol (PPT) had no effect. Neither ligand increased ERβ at the cox-2 promoter. Rather, DPN decreased promoter occupancy of NF-κB p65 and histone 4 (H4) acetylation. Treatment with the non-specific HDAC inhibitor Trichostatin A (TSA) counteracted DPN’s repressive effects on cox-2 expression. In keeping with the TSA effect, E2 and DPN increased histone deacetylase one (HDAC1) and switch-independent 3A (Sin3A) promoter occupancy. Lastly, even though E2 increased CpG methylation, DPN did not. Taken together, the pharmacological data indicate that ERβ contributes to neuronal cox-2 expression, as measured by RNA levels. Furthermore, ER ligands lead to increased recruitment of HDAC1, Sin3A and a concomitant reduction of p65 occupancy and Ac-H4 levels. None of the events, however, are associated with a significant recruitment of ERβ at the promoter. Thus, ERβ directs recruitment to the cox-2 promoter, but does so in the absence of being recruited itself.
Ultra Low Power Current-mode Algorithmic Analog-to-digital Converter Implemented In 0.18 /spl mu/m CMOS Technology For Wireless Sensor Network

This paper reviews existing analog-to-digital converters (ADC) and compares them based on the power consumption metric. For applications where power consumption is of utmost importance, a novel 8-bit current mode Successive Approximation ADC (SAR) is proposed. Based on initial simulations made for CMOS 0.35 mum technology, it has been observed that the novel SAR architecture is very flexible i.e. it can be easily tuned to work with different frequencies and different power consumption values. In CMOS 0.35 mum technology the optimum frequency range is 25-350 kS/s, and power dissipation of the analog part of ADC ranges from 40 nW to 550 nW for 1 V power supply. The final post layout simulations of the chip designed in CMOS 0.18 mum technology were made for 0.55 V power supply. Entire (analog and digital circuits) SAR ADC working with the frequency of 250 kHz consumes only 580 nW
Discovery methods for information extraction

At the present state of the art, however, performance varies widely depending on the nature of the language being processed and the complexity of the relation being extracted. For restricted sublanguages and simple relations, levels of accuracy comparable to human coders are possible. This has been achieved, for example, for some types of medical records, where both physicians and an extraction system identified diseases with 70-80% accuracy (Friedman et al. 1995). High performance has also been achieved for semi-structured Web documents — documents with some explicit mark-up (Cohen and Jensen 2001). In contrast, for more complex relations and more general texts, accuracies of 50-60% are more typical. Even at these levels IE can be of significant value in situations where the text is too voluminous to be reviewed manually; for example, to provide a document search tool much richer than current keyword systems (Grishman et al 2002). IE is also being used in other applications where perfect recall is not required, such as data mining from text collections and the generation of time lines for texts.
Fractionally Spaced Frequency-Domain MMSE Receiver for OFDM Systems

Based on frequency-domain oversampling and the Bayesian Gauss-Markov theorem, we propose a fractionally spaced frequency-domain minimum mean-square error (FSFDMMSE) receiver for orthogonal frequency-division multiplexing systems. It is shown that frequency diversity inherent in a frequency-selective fading channel can be extracted and exploited by the proposed FSFD-MMSE receiver. This diversity advantage outweighs the effect of intercarrier interference generated by frequency-domain oversampling, due largely to the MMSE receiver's interference suppression capability. Numerical results show that the FSFD-MMSE receiver outperforms the conventional MMSE receiver under both ideal and practical situations (i.e., with frequency offset, channel estimation errors, and even doubly selective channel fading). In addition, the FSFD-MMSE receiver only needs a fast Fourier transform size that is no larger than N + Q -1 (TV = number of data subcarriers, and Q = number of resolvable multipath components).
A context-aware architecture for mental model sharing in intelligent agents

Nowadays, with the growth of applying multi agent design paradigms in recent applications, the idea of using mental models for enabling autonomous agents to collaborate is getting more attention. In this paper, an architecture is presented for sharing the mental models through a kind of transition from the agents' present mental states to some new states that can become as conflict less as much toward each other. This architecture has been inspired by a variety of mental models in humans and supports agents working in different simultaneous contexts. Our test bed to evaluate this architecture is complex scenarios which are likely impossible to be resolved by individual agents without sharing. We believe the proposed architecture would be able to provide agents with the capability of generating more consistent behaviors between them.
Fast sampling for Bayesian inference in neural circuits

Time is at a premium for recurrent network dynamics, and particularly so when they are stochastic and correlated: the quality of inference from such dynamics fundamentally depends on how fast the neural circuit generates new samples from its stationary distribution. Indeed, behavioral decisions can occur on fast time scales (~100 ms), but it is unclear what neural circuit dynamics afford sampling at such high rates. We analyzed a stochastic form of rate-based linear neuronal network dynamics with synaptic weight matrix $W$, and the dependence on $W$ of the covariance of the stationary distribution of joint firing rates. This covariance $\Sigma$ can be actively used to represent posterior uncertainty via sampling under a linear-Gaussian latent variable model. The key insight is that the mapping between $W$ and $\Sigma$ is degenerate: there are infinitely many $W$'s that lead to sampling from the same $\Sigma$ but differ greatly in the speed at which they sample. We were able to explicitly separate these extra degrees of freedom in a parametric form and thus study their effects on sampling speed. We show that previous proposals for probabilistic sampling in neural circuits correspond to using a symmetric $W$ which violates Dale's law and results in critically slow sampling, even for moderate stationary correlations. In contrast, optimizing network dynamics for speed consistently yielded asymmetric $W$'s and dynamics characterized by fast transients, such that samples of network activity became fully decorrelated over ~10 ms. Importantly, networks with separate excitatory/inhibitory populations proved to be particularly efficient samplers, and were in the balanced regime. Thus, plausible neural circuit dynamics can perform fast sampling for efficient decoding and inference.
Security Model Evolution of PHP Web Applications

Web sites are often a mixture of static sites and programs that integrate relational databases as a back-end. As they evolve to meet ever-changing user needs, new versions of programs, interactions and functionalities may be added and existing ones may be removed or modified. Web sites require configuration and programming attention to assure security, confidentiality, and trust of the published information. During evolution of Web software, from one version to the next one, security properties may change and possible changes may include new flaws or corrections. Changes to security properties, including access control privileges, can be monitored by observing and analyzing changes between security models extracted from different versions of an application. This paper defines Property Satisfaction Profiles (PSP) as the satisfaction values of properties computed on the extracted models. This paper presents also an investigation of the evolution of the changes in the PSP computed on security models of different versions of a Web application. Model extraction and PSP computation can be performed in linear time on one version. Comparison between two versions is also linear and practical performance is fast. This paper reports results about experiments performed on 31 versions of phpBB, that is a publicly available bulletin board written in PHP. Version 1.0. 0 (9547 LOC) to version 2.0. 22 (40663 LOC) have been considered as a case study. Results show that the proposed approach can be used to observe and monitor the evolution of PSP in successive versions of the same software package. Suggestions for further research are also presented.
Single-View RGBD-Based Reconstruction of Dynamic Human Geometry

We present a method for reconstructing the geometry and appearance of indoor scenes containing dynamic human subjects using a single (optionally moving) RGBD sensor. We introduce a framework for building a representation of the articulated scene geometry as a set of piecewise rigid parts which are tracked and accumulated over time using moving voxel grids containing a signed distance representation. Data association of noisy depth measurements with body parts is achieved by online training of a prior shape model for the specific subject. A novel frame-to-frame model registration is introduced which combines iterative closest-point with additional correspondences from optical flow and prior pose constraints from noisy skeletal tracking data. We quantitatively evaluate the reconstruction and tracking performance of the approach using a synthetic animated scene. We demonstrate that the approach is capable of reconstructing mid-resolution surface models of people from low-resolution noisy data acquired from a consumer RGBD camera.