Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Spatial Analysis of Rare Crimes Technical Report Submitted to the Mapping and Analysis for Public Safety ( MAPS ) Program , National Institute of Justice ( NIJ )

Research Goals and Objectives: The main goal of this project was to develop an analytical approach that will allow researchers to incorporate spatial error structures in models of rare crimes. In order to examine the causes of violence, researchers are frequently confronted with the need to apply spatial econometric methods to models with discrete outcomes. Appropriate methods for doing so when the outcomes are measured at intra-city areal units are lacking. The aim of this research was to fill that gap. This research effort developed and applied the framework to a real-world empirical problem. It examined the socio-economic and demographic determinants of disaggregate homicide rates at two different intra-city levels of areal aggregation and compared inferences derived from several sets of models. The analysis was conducted on disaggregated homicide counts (1989-91) recorded in Chicago’s census tracts and neighborhood clusters using explanatory factors obtained from census sources. Research Design and Methodology: An extension of the Generalized Cross Entropy (GCE) method was applied to these data in an attempt to utilize their flexibility in allowing error structures across space. In addition, an information-based measure was developed and used in selecting the hypothesized error structure that “best” approximates the true underlying structure. Research Results and Conclusions: Findings from this research confirmed that ignoring spatial structures in the regression residuals often leads to severely biased inferences and, hence, a poor foundation on which to base policy. In addition, evidence was found of homicide type-specific and areal units-specific models, highlighting the need for disaggregating violence into distinct types. However, resource deprivation in a community was found to be a reliable and persistent predictor of all types of violence analyzed and at both levels of areal aggregation. Additionally, there was evidence of a spill-over effect of resource deprivation on the amount of violence expected in neighboring areas. This highlights the need for taking seriously the spatial structure in a sample when planning for and implementing policy measures, especially at the intra-city level, where the observational units are spatially linked in meaningful ways. The GCE approach utilized in this project offers several avenues for future research especially as they relate to the analysis of rare crimes. This includes the possibility of modeling other substantive spatial processes, an improved modeling of underlying populationat-risk instability, modeling mixed processes, and modeling spatio-temporal dynamics.
Multi-Level Kernel Machine for Scene Image Classification

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.
Enhancement of noisy speech by using improved global soft decision

We propose a novel speech enhancement algorithm, termed improved global soft decision (IGSD). IGSD is a unified framework for global soft decision on speech absence/presence, noise spectrum estimation, spectral gain modification based on Ephraim-Malah noise suppression. In IGSD, speech absence probability (SAP) is the most important factor, and we propose an efficient and novel SAP estimation in which the SAP is derived based on the general hypothesis for speech absence/presence. In IGSD, the global SAP based on the global hypothesis for speech absence/presence is used to prevent from the problem caused by insufficient amount of data, but more general hypothesis is utilized in the derivation of global SAP estimation. The performance of IGSD is evaluated both subjectively and objectively, and the quality of speech is improved significantly, compared with conventional GSD speech enhancement algorithm.
Facilitating reuse in model-based development with context-dependent model element recommendations

Reuse recommendation systems suggest code entities useful for the task at hand within the IDE. Current approaches focus on code-based development. However, model-based development poses similar challenges to developers regarding the identification of useful elements in large and complex reusable modeling libraries. This paper proposes an approach for recommending library elements for domain specific languages. We instantiate the approach for Simulink models and evaluate it by recommending library blocks for a body of 165 Simulink files from a public repository. We compare two alternative variants for computing recommendations: association rules and collaborative filtering. Our results indicate that the collaborative filtering approach performs better and produces recommendations for Simulink models with satisfactory precision and recall.
Multi-profile instruction based compression

Code compression has been used to minimize the memory area requirement of embedded systems. Recently, performance improvement and energy consumption reduction are observed as a by-product of compression. In this paper we propose a novel technique for efficiently exploring the trade-offs involved in code compression. Our multiprofile approach to build dictionaries combines the best features of both static and dynamic program behaviors. The experiments with Mediabench and MiBench suites and the Leon (SPARCv8) processor reveal a compression ratio as low as 71% while performance speed-up reaches 1.5.
A 6-GS/s 6-bit time interleaved SAR-ADC

This paper presents a 6-GS/s 6-bit time-interleaved successive approximation register (SAR) analog to digital converter (ADC) realized in 90-nm CMOS. The ADC consists of 32 single SAR-ADCs. The measured effective-number-of-bits (ENOB) at sampling rate of 6.144 GS/s are 5-bit at DC and 3.6-bit at the Nyquist frequency. The power consumption of the ADC-core without I/O's and 4-to-1 output MUX is 359 mW for an input swing of 1 V peak to peak differential, resulting in a FOM of 4.9 pJ/conv.
Height-field fluids for computer graphics

The author presents a novel method for animating water based on a simple, rapid, and stable solution of a set of partial differential equations resulting from an approximation to the shallow water equations. The approximation gives rise to a version of the wave equation on a height field where the wave velocity is proportional to the square root of the depth of the water. The resulting wave equation is then solved with an alternating-direction implicit method on a uniform finite-difference grid. The computational work required for an iteration consists mainly of solving a simple tridiagonal linear system for each row and column of the height field. A single iteration per frame suffices in most cases for convincing animation. Unlike previous models, the proposed method handles wave reflections, net transport of water, and boundary conditions with changing topology. <<ETX>>
Finite state tokenisation of an orthographical disjunctive agglutinative language: The verbal segment of Northern Sotho

Tokenisation is an important first pre-processing step required to adequately test finite-state morphological analysers. In agglutinative languages each morpheme is concatinatively added on to form a complete morphological structure. Disjunctive agglutinative languages like Northern Sotho write these morphemes, for certain morphological categories only, as separate words separated by spaces or line breaks. These breaks are, by their nature, different from breaks that separate words that are written conjunctively. A tokeniser is required to isolate categories, like a verb, from raw text before they can be correctly morphologically analysed. The authors have successfully produced a finite state tokeniser for Northern Sotho, where verb segments are written disjunctively but nominal segments conjunctively. The authors show that since reduplication in the Northern Sotho language does not affect the pre-processing tokeniser, the disjunctive standard verbal segment as a construct in Northern Sotho is deterministic, finite-state and a regular Type 0 language in the Chomsky hierarchy and that the copulative verbal segment, due to its semi-disjunctivism, is ambiguously non-deterministic.
Fiscal austerity and the trade-off between public investment and social spending

This article makes two claims: first, it argues that the impact of fiscal austerity varies across different types of public spending. In particular, we show that discretionary spending (public investment) is hit harder by fiscal austerity than entitlement spending (public spending on pensions and unemployment) because of different institutional and political constraints. Second, we find that electoral institutions affect how governments solve this trade-off. Discretionary spending is cut back more severely in countries with an electoral system based on proportional representation than in a majoritarian system. Our empirical analysis relies on a methodological approach (composite data analysis) that takes into account interdependencies between budgetary categories. Using data for 21 Organisation for Economic Co-operation and Development countries from 1979 to 2003, we find strong evidence for the varying impact of fiscal stress on the budget shares of discretionary and entitlement spending as well as strong interactive effect between fiscal austerity and electoral institutions.
Does the Time Inconsistency Problem Make Flexible Exchange Rates Look Worse than You Think?

The Barro-Gordon inflation bias has provided the most influential argument for fixed exchange rate regimes. However, with low inflation rates now widespread, credibility concerns seem no longer relevant. Why give up independent monetary policy to contain an inflation bias that is already under control? We argue that credibility problems do not end with the inflation bias and they are a larger drawback for flexible exchange rates than usually thought. Absent commitment, independent monetary policy can induce expectation traps---that is, welfare ranked multiple equilibria---and perverse policy responses to real shocks, i.e., an equilibrium policy response that is welfare inferior to policy inaction. Both possibilities imply that flexible exchange rates feature unnecessary macroeconomic volatility.
Optimizing the Likelihood with Sequential Monte – Carlo methods

In this paper, a Sequential Monte–Carlo (SMC) method is studied to deal with nonlinear multivariate optimization problems arising from Maximum Likelihood (ML) estimation approaches. In this context, gradient–like methods are not efficient being the computational cost burdensome. Moreover, SMC provide an appealing way of introducing prior information in the estimation of parameters in general state–space models. Relying on SMC methods, the optimization algorithm is completely exposed and its pseudocode delivered for completely general ML problems. For the sake of completeness, the algorithm has been adapted and simulated in a GNSS application, where the use of prior information is of great interest.
Competitive perspective identification via topic based refinement for online documents

People write online documents from different personal perspectives. The competitive perspectives they hold reflect the conflicts in their fundamental stances and viewpoints. For many security-related applications, it is both beneficial and critical to identify the competitive perspectives implied in online documents. Previous work on competitive perspective identification is based on word features, which did not consider that the word usage for perspective expression varies with topics in documents. Thus topic information can be incorporated and contribute to a more fine-grained treatment of perspective identification. Motivated by this, this paper proposes an approach for competitive perspective identification in online documents via topic based refinement. Our approach refines the basic word feature-based perspective identification model with latent semantic information. In addition, we develop a self-adaptive process to fit the model parameters automatically. Experimental study shows the effectiveness of our approach compared to the related work and the baseline methods.
BLOCK SPLITTING TYPE MORPHOLOGICAL ASSOCIATIVE MEMORY AND ITS RECALL RATE IMPROVEMENT BY MAJORITY LOGIC APPROACH

A block splitting type MAM (BMAM) is one of the associative memories. The BMAM is a kind of the MAM without kernel images and is superior to other ordinary MAMs without kernel images in terms of the recall rate and the memory size. However, the perfect recall rate of the BMAM is inferior to that of the MAM using the kernel image. In order to improve the perfect recall rate, we proposed a majority logic approach for the BMAM. We paid attention to the BMAM’s features: multiple outputs can be obtained from single image by using different split manners and most units contained in common to the stored pattern appear in these output. In this paper, we confirm the performance of the BMAM employing the majority logic approach by multiple autoassociation experiments and discuss the effective usage considering the memory size.
Alleviating blocking artifacts via curve-fitting

Image enhancement in discrete cosine transform (DCT) domain, which has been widely utilized, is affected by blocking artifacts seriously. Various effective methods for reducing such blocking artifacts are paid much attention. A novel method for reducing the artifacts is proposed in this paper; the novelty of this method is developing a curve-fitting algorithm to alleviate blocking artifacts and a least-square based technique is adopted to estimate the coefficients for curve fitting. As a result, the blocking artifacts can be significantly alleviated. In addition, the color and contrast of resulted image can be kept as that in the adjusted image (with blocking artifacts). Experiments on different images demonstrate that the proposed algorithm can provide better results than that of the popular CES algorithm[1] (Enhancement of color images by scaling the DCT coefficients) and has lower computational complexity.
Tracing Shocks and Photodissociation in the Galactic Center Region

We present a systematic study of the HNCO, C18O,13CS, and C34S emission toward 13 selected molecular clouds in the Galactic center region. The molecular emission in these positions is used as a template of the different physical and chemical processes claimed to be dominant in the circumnuclear molecular gas of galaxies. The relative abundance of HNCO shows a variation of more than a factor of 20 among the observed sources. The HNCO/13CS abundance ratio is highly contrasted (up to a factor of 30) between the shielded molecular clouds mostly affected by shocks, where HNCO is released to gas phase from grain mantles, and those pervaded by an intense UV radiation field, where HNCO is photodissociated and CS production favored via ion reactions. We propose the relative HNCO to CS abundance ratio as a highly contrasted diagnostic tool to distinguish between the influence of shocks and/or the radiation field in the nuclear regions of galaxies and their relation to the evolutionary state of their nuclear star formation bursts.
A Practical Adaptive Transmission Scheme for Spatial Multiplexing MIMO Systems

We propose in this paper a practical adaptive modulation and channel encoding scheme for spatial multiplexing multiple-input multiple-output (MIMO) systems. The proposed scheme aims not only to maximize the average spectral efficiency (ASE) for a given bit error rate (BER) constraint but also to lower the computational and hardware complexity. First, an adaptive transmission principle for spatial multiplexing MIMO systems is presented under the assumption that the channel state information (CSI) is available at the receiver; Furthermore, we propose not only an adaptive modulation scheme for different transmit antennas but also an adaptive scheme which combines channel encoding and modulation. Finally we demonstrate from the simulation that the proposed scheme is of practical significance since it provides a well-behaved tradeoff between the ASE and complexity in comparison with the singular value decomposition (SVD) based adaptive transmission scheme with the optimal ASE performance and high computational complexity.
Particle filtering for 2-D direction of arrival tracking using an acoustic vector sensor

Acoustic vector sensor (AVS) signal measures acoustic pressure as well as particle velocity, and therefore contains both the azimuth and the elevation information of the source. Existing 2-D DOA estimation methods for AVS assume that the source is static and extensively rely on the localization techniques. In this paper, a particle filtering (PF) approach is developed to track the 2-D DOA by using a single AVS. A constant velocity model is employed to model the source dynamics and the likelihood function is derived based on maximum likelihood estimation of the source amplitude and the noise variance. Since the likelihood function is usually spread and distorted in the heavy noisy environment, it is further exponentially weighted to enhance the weight of particles at high likelihood area. Simulations show that the proposed algorithm significantly outperforms the traditional Capon beamforming method and is able to lock on the DOA of the source in challenging environments.
Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation

Many current methods for multiple sclerosis (MS) lesion segmentation require radiologist seed points as input, but do not necessarily allow the expert to work in an intuitive or efficient way. Ironically, most methods also assume that the points are placed optimally. This paper examines how seed points can be processed with intuitive heuristics, which provide improved segmentation accuracy while facilitating quick and natural point placement. Using a large set of MRIs from an MS clinical trial, two radiologists are asked to seed the lesions while unaware that the points would be fed into a classifier, based on Parzen windows, that automatically delineates each marked lesion. To evaluate the impact of the new heuristics, an interactive region-growing method is used to provide ground truth and the Dice coefficient (DC) and Spearman's rank correlation are used as the primary measures of agreement. A stratified analysis is performed to determine the effect on scans with low-, medium-, and high lesion loads. Compared to the unenhanced classifier, the heuristics dramatically improve the DC (+32.91 pt.) and correlation (+0.50) for the scans with low lesion loads, and also improve the DC (+14.55 pt.) and correlation (+0.15) for the scans with medium lesion loads, while having a minimal effect for the scans with high lesion loads, which are already segmented accurately by Parzen windows. With the heuristics, the DC is close to 80% and the correlation is above 0.9 for all three load categories.
Developing Industrial Multi-Agent Systems

The development and deployment of multi-agent systems in real world settings raises a number of important research issues and problems which must be overcome if Distributed AI (DAD is to become a widespread solution technology. Work undertaken in the context of the ARCHON project has provided a number of important insights into these issues. By providing an in depth analysis of ARCHON’s electricity transportation management application, this paper draws together many of the experiences obtained when building one of the world’s first operational DA! systems.
Analysis of Asymmetric Measures for Performance Estimation of a Sentiment Classifier

The development of a sentiment classier experiences two problems to cope with: the demand of large amounts of labelled training data and a decrease in performance when the classier is applied to a dierent domain. In this paper, we attempt to address this problem by exploring a number of metrics that try to predict the cross-domain per- formance of a sentiment classier through the analysis of divergence be- tween several probability distributions. In particular, we apply similarity measures to compare dierent domains and investigate the implications of using non-symmetric measures for contrasting feature distributions. We nd that quantifying the dierence between domains is useful to predict which domain has a feature distribution most similar to the tar- get domain.
(MC2)2: A Generic Decision-Making Framework and its Application to Cloud Computing

Cloud computing is a disruptive technology, representing a new model for information technology (IT) solution engineering and management that promises to introduce significant cost savings and other benefits. The adoption of Cloud computing requires a detailed comparison of infrastructure alternatives, taking a number of aspects into careful consideration. Existing methods of evaluation, however, limit decision making to the relative costs of cloud computing, but do not take a broader range of criteria into account. In this paper, we introduce a generic, multi-criteria-based decision framework and an application for Cloud Computing, the Multi-Criteria Comparison Method for Cloud Computing ((MC2)2). The framework and method allow organizations to determine what infrastructure best suits their needs by evaluating and ranking infrastructure alternatives using multiple criteria. Therefore, (MC2)2 offers a way to differentiate infrastructures not only by costs, but also in terms of benefits, opportunities and risks. (MC2)2 can be adapted to facilitate a wide array of decision-making scenarios within the domain of information technology infrastructures, depending on the criteria selected to support the framework.