Miracle-dz/UniFAR-FTUs huggingface dataset:
A modified likelihood function approach to DOA estimation in the presence of unknown spatially correlated Gaussian noise using a uniform linear array

The problem of modified ML estimation of DOAs of multiple source signals incident on a uniform linear array (ULA) in the presence of unknown spatially correlated Gaussian noise is addressed here. Unlike previous work, the proposed method does not impose any structural constraints or parameterization of the signal and noise covariances. It is shown that the characterization suggested here provides a very convenient framework for obtaining an intuitively appealing estimate of the unknown noise covariance matrix via a suitable projection of the observed covariance matrix onto a subspace that is orthogonal complement of the so-called signal subspace. This leads to a formulation of an expression for a so-called modified likelihood function, which can be maximized to obtain the unknown DOAs. For the case of an arbitrary array geometry, this function has explicit dependence on the unknown noise covariance matrix. This explicit dependence can be avoided for the special case of a uniform linear array by using a simple polynomial characterization of the latter. A simple approximate version of this function is then developed that can be maximized via the-well-known IQML algorithm or its variants. An exact estimate based on the maximization of the modified likelihood function is obtained by using nonlinear optimization techniques where the approximate estimates are used for initialization. The proposed estimator is shown to outperform the MAP estimator of Reilly et al. (1992). Extensive simulations have been carried out to show the validity of the proposed algorithm and to compare it with some previous solutions.
Toward a reflective SimStudent: Using experience to avoid generalization errors

Simulated learner systems are used for many purposes ranging from computational models of learning to teachable agents. To support these varying applications, some simulated learner systems have relied heavily on machine learning to achieve the necessary generality. However, these efforts have resulted in simulated learners that sometimes make generalization errors that the humans they model never make. In this paper, we discuss an approach to reducing these kinds of generalization errors by having the simulated learner system reflect before acting. During these reflections, the system uses background knowledge to recognize implausible actions as incorrect without having to receive external feedback. The result of this metacognitive approach is a system that avoids implausible errors and requires less instruction. We discuss this approach in the context of SimStudent, a computational model of human learning that acquires a production rule model from demonstrations.
Detecting Emergence in Social Networks

As social networks expand and interconnect with other social networks, their combined behavior can become more complex than each individual network in isolation and can result in unexpected self-organizing or emergent behaviors. This emergent behavior results from the combined knowledge or skills of the participants, and it enables the group to do things that it could not otherwise accomplish. Examples of such social network emergence can be found in startup companies, charitable organizations, groups of researchers and terrorist cells. This paper discusses a mathematical modeling technique for prediction and detection of emergent behavior in social networks using Semi-Boolean Algebra. This paper discusses emergent behavior, the use of Semi-Boolean Algebra for detecting it, and gives examples of its use on emergence in an example social network.
Remote Controlling of Home Appliances using Mobile Telephony

In modern days, we must use various high-tech machineries and equipments to get our jobs done and make the life easier. These machineries should be controlled by the homeowner from any location as the homeowner might be away from home at workplace or traveling in a different place in the weekend. Thus a system of remote monitoring and controlling are very much necessary. Smart home is one of these types of system equipped with home appliances which we wish to control smartly from anywhere. Some products are commercially available which allow remote home appliance controlling through internet which is undoubtedly emerging. But it lacks the true sense of real mobility and security, making the remote home appliance controlling a limited term than it is supposed to be. In search of a true remote and adequately secure solution to be really effective and practicable, mobile telephony is better than any other solutions. Mobile phones have become almost an inseparable part of civil lives today. In this paper we introduce a new mechanism so that the ordinary services of the mobile phones can be leveraged to communicate with and control the home appliances and make our homes a really smart one.
nfluence of the implanted pulse generator as reference electrode in finite lement model of monopolar deep brain stimulation

Electrical deep brain stimulation (DBS) is an efficient method to treat movement disorders. Many models of DBS, based mostly on finite elements, have recently been proposed to better understand the interaction between the electrical stimulation and the brain tissues. In monopolar DBS, clinically widely used, the implanted pulse generator (IPG) is used as reference electrode (RE). In this paper, the influence of the RE model of monopolar DBS is investigated. For that purpose, a finite element model of the full electric loop including the head, the neck and the superior chest is used. Head, neck and superior chest are made of simple structures such as parallelepipeds and cylinders. The tissues surrounding the electrode are accurately modelled from data provided by the diffusion tensor magnetic resonance imaging (DT-MRI). Three different configurations of RE are compared with a commonly used model of reduced size. The electrical impedance seen by the DBS system and the potential distribution are computed for each model. Moreover, axons are modelled to compute the area of tissue activated by stimulation. Results show that these indicators are influenced by the surface and position of the RE. The use of a RE model corresponding to the implanted device rather than the usually simplified model leads to an peda increase of the system im
Baggy Bounds Checking: An Efficient and Backwards-Compatible Defense against Out-of-Bounds Errors

Attacks that exploit out-of-bounds errors in C and C++ programs are still prevalent despite many years of research on bounds checking. Previous backwards compatible bounds checking techniques, which can be applied to unmodified C and C++ programs, maintain a data structure with the bounds for each allocated object and perform lookups in this data structure to check if pointers remain within bounds. This data structure can grow large and the lookups are expensive. In this paper we present a backwards compatible bounds checking technique that substantially reduces performance overhead. The key insight is to constrain the sizes of allocated memory regions and their alignment to enable efficient bounds lookups and hence efficient bounds checks at runtime. Our technique has low overhead in practice--only 8% throughput decrease for Apache-- and is more than two times faster than the fastest previous technique and about five times faster--using less memory--than recording object bounds using a splay tree.
A HMM-based approach to learning probability models of programming strategies for industrial robots

The integration of industrial robot systems into the manufacturing environments of small and medium sized enterprises is a key requirement to guarantee competitiveness and productivity. Due to the still complex and time-consuming procedure of robot path definition, novel programming strategies are needed, converting the robotic system into a flexible coworker that actively supports its operator. In this paper, a learning-from-demonstration strategy based on Hidden Markov Models is presented, which permits the robot system to adapt to user- as well as process-specific features. To evaluate the suitability of this approach for small-lot production, the learning strategy has been implemented for an arc welding robot and has been evaluated on-site at a medium sized metal-working company.
Features extraction from point clouds for automated detection of deformations on automotive body parts

This paper proposes an innovative solution to the problem of extracting feature nodes from a 3D model and grouping nearby feature nodes according to the likelihood that they belong to the same feature. The technique is designed specifically with the problem of detecting unwanted deformations on automotive body part in mind, where feature line detection will not always give the best results. Using an octree representation, the multiresolution method is able to analyze the model for features of various scales. It also uses the octree data structure for feature grouping, and provides an alternative to feature line extraction for connecting similar feature nodes. An existing technique is compared to the proposed approach for feature extraction, and results are presented for the feature grouping method using a point cloud of a miniature car model.
7,8-Dichloro-1-oxo-β-carbolines as a Versatile Scaffold for the Development of Potent and Selective Kinase Inhibitors with Unusual Binding Modes

Development of both potent and selective kinase inhibitors is a challenging task in modern drug discovery. The innate promiscuity of kinase inhibitors largely results from ATP-mimetic binding to the kinase hinge region. We present a novel class of substituted 7,8-dichloro-1-oxo-β-carbolines based on the distinct structural features of the alkaloid bauerine C whose kinase inhibitory activity does not rely on canonical ATP-mimetic hinge interactions. Intriguingly, cocrystal structures revealed an unexpected inverted binding mode and the presence of halogen bonds with kinase backbone residues. The compounds exhibit excellent selectivity over a comprehensive panel of human protein kinases while inhibiting selected kinases such as the oncogenic PIM1 at low nanomolar concentrations. Together, our biochemical and structural data suggest that this scaffold may serve as a valuable template for the design and development of specific inhibitors of various kinases including the PIM family of kinases, CLKs, DAPK3 (ZIPK), BMP2K (BIKE), and others.
FILTERING AND RESTORATION OF STRUCTURES IN 3D ULTRASOUND IMAGES

We present a new method aimed at restoring structures in 3D US images. In our approach, 3D US data acquired with tilt devices is resampled into cylindrical coordinates with the purpose of overcoming the problems of anisotropic sampling space. Then, enhancement and shadowing effects, as well as average attenuation effects, are removed with a ray-based rescaling process. Finally, a novel method for reducing noise and enhancing the structures based on a modified 3D anisotropic diffusion is applied. The diffusion scheme is improved in several steps: the diffusivity is computed locally as part of each iteration based on local statistics; two terms intended to preserve the mean value along homogeneous regions and to enhance the contrast are introduced. Results show an improvement of the contrast when applied on 3D transrectal US (TRUS) images of prostate, which can facilitate further segmentation.
Diffusion Magnetic Resonance Histograms as a Surrogate Marker and Predictor of Disease Progression in CADASIL: A Two-Year Follow-Up Study

Background and Purpose— Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a cerebral small vessel disease caused by mutations in the NOTCH3 gene. MRI is sensitive in detecting preclinical involvement and changes over time. However, little is known about correlations between MRI metrics and clinical measures on a longitudinal scale. In this study, we assessed the role of quantitative MRI (T2-lesion volume and diffusion tensor imaging [DTI]–derived metrics) in monitoring and predicting disease progression. Methods— Sixty-two CADASIL subjects were followed prospectively over a period of 26.3±1.2 months. Dual-echo scans, DTI scans, and clinical scales were obtained at baseline and at follow-up. T2-lesion volumes were determined quantitatively, and histograms of mean diffusivity (MD) were produced. Results— At follow-up, T2-lesion volumes and MD histogram metrics had changed significantly (all P<0.01). Lesion volumes and average MD correlated with clinical scores at baseline. Changes of average MD correlated with changes of the Rankin score, the National Institutes of Health Stroke Scale score, and the structured interview for the diagnosis of Alzheimer dementia and multiinfarct dementia score (all P<0.01). On multivariate analysis, average MD and systolic blood pressure at baseline were predictors of changes of average MD during follow-up. Moreover, average MD was the main predictor of clinical progression. Sample size estimates showed that the number of individuals required to detect a treatment effect in an interventional trial may be reduced when using MD histograms as an end point. Conclusions— This study establishes correlations between changes of DTI histogram metrics and clinical measures over time. DTI histograms may be used as an adjunct outcome measure in future therapeutic trials. Moreover, DTI histogram metrics predict disease progression in CADASIL.
Bounding the Resource Savings of Utility Computing Models

In this paper we characterize resource usage in six data centers with approximately 1,000 servers. The results substantiate common knowledge that computing resources are typically under-utilized. Utility computing has been proposed as a way of increasing utilization and hence efficiency. Using an off-line integer programming model we bound the potential gain in efficiency for several different utility computing models. In a detailed study of a subset of the data center servers, we found that utility computing offered the potential to reduce the peak and mean number of CPUs needed by up to 53% and 79%, respectively. ∗This work was done as Post-Doctoral research at Hewlett-Packard Laboratories.
Comparison of different image subband coding methods at low bit rates

Two image subband coding methods are introduced as combinations of trellis-coded quantization (TCQ) with zerotree and stack-run coding. These TCQ-based image coding algorithms are compared, at low bit rates, with the set partitioning in hierarchical trees and stack-run scalar quantization-based image coding algorithms. Direct use of TCQ with zerotree or stack-run coding methods is found to provide little or no improvement in rate-distortion performance compared to scalar quantization.
Area Law for Localization-Entropy in Local Quantum Physics

The previously developed algebraic lightfront holography is used in conjunction with the tensor splitting of the chiral theory on the causal horizon. In this way a universal area law for the entanglement entropy of the vacuum relative to the split (tensor factorized) vacuum is obtained. The universality of the area law is a result of the kinematical structure of the properly defined lightfront degrees of freedom. We consider this entropy associated with causal horizon of the wedge algebra in Minkowski spacetime as an analog of the quantum Bekenstein black hole entropy similar to the way in which the Unruh temperature for the wedge algebra may be viewed as an analog in Minkowski spacetime of the Hawking thermal behavior. My more recent preprint hep-th/0202085 presents other aspects of the same problem.
Engagement of CD99 Reduces AP-1 Activity by Inducing BATF in the Human Multiple Myeloma Cell Line RPMI8226

CD99 signaling is crucial to a diverse range of biological functions including survival and proliferation. CD99 engagement is reported to augment activator protein-1 (AP-1) activity through mitogen-activated protein (MAP) kinase pathways in a T-lymphoblastic lymphoma cell line Jurkat and in breast cancer cell lines. In this study, we report that CD99 differentially regulated AP-1 activity in the human myeloma cell line RPMI8226. CD99 was highly expressed and the CD99 engagement led to activation of the MAP kinases, but suppressed AP-1 activity by inducing the expression of basic leucine zipper transcription factor, ATF-like (BATF), a negative regulator of AP-1 in RPMI8226 cells. By contrast, engagement of CD99 enhanced AP-1 activity and did not change the BATF expression in Jurkat cells. CD99 engagement reduced the proliferation of RPMI8226 cells and expression of cyclin 1 and 3. Overall, these results suggest novel CD99 functions in RPMI8226 cells.
The Effect of Warm-up on Tethered Front Crawl Swimming Forces

The Effect 0f Warm-up on Tethered Front Crawl Swimming Forces This study was conducted to determine the effect of warm-up on high-intensity front crawl tethered swimming and thus to better understand possible variations in the force exerted by the swimmers. Ten male national level swimmers (mean ± SD; age 15.3 ± 0.95 years old, height: 1.73 ± 5.2 m, body mass: 64.3 ± 7.8 kg, Fat mass 8.31 ± 3.1 kg) participated in this study. After a typical competition warm-up, the subjects performed a 30 s tethered swimming all-out effort in front crawl swimming technique. The same test was repeated in the day after but performed without warming up. Capillary blood lactate concentration was assessed before and after the swimming test and the Borg ratings of perceived exertion scale was used. Without a previous warm-up, the mean ± SD values of maximum and mean forces were 299.62 ± 77.56 N and 91.65 ± 14.70 N, respectively. These values were different (p<0.05) from the values obtained with warm-up (351.33 ± 81.85 N and 103.97 ± 19.11 N). Differences were also observed when regarding to the forces relative to body mass. However, the values of lactate net concentrations after the test performed with and without warm-up were not different (6.27 ± 2.36 mmol·l-1 and 6.18 ± 2.353 mmol·l-1) and the same occurs with the values of ratings of perceived exertion (15.90 ± 2.42 and 15.60 ± 2.27). These results suggest an improvement of the maximum and mean force of the swimmer on the tethered swimming due to previous warm-up.
An estimation of the protein requirements of Iberian x Duroc 50:50 crossbred growing pigs.

The effects of dietary protein content on the rates of gain and protein deposition were studied in Iberian (IB) × Duroc (DU) 50:50 barrows at 2 stages of growth [10.6 ± 0.2 (n = 28) and 60.0 ± 0.4 (n = 24) kg initial BW]. Two feeding, digestibility, and N-balance trials were performed. At each stage of growth, they were allocated in individual pens and given restrictedly (at 0.9 × ad libitum intake) one of 4 pelleted diets of similar energy concentration (13.8 to 14.5 MJ ME/kg DM), formulated to provide 4 different (ideal) CP contents (236, 223, 208, and 184 g CP/kg DM in the first trial, and 204, 180, 143, and 114 g CP/kg DM in the second trial). Feed allowance was offered in 2 daily equal meals. The average concentration of Lys was 6.59 ± 0.13 g /100 g CP for all diets. Whatever the stage of growth, average daily BW gain and gain to feed ratio were unchanged by increases in dietary CP content (477 ± 7 and 1,088 ± 20 g, and 0.475 ± 0.027 and 0.340 ± 0.113, respectively, in the first and second trial). In pigs growing from 10 to 27 kg BW, the average rate of N retention increased linearly (P < 0.01) on increasing the protein content in the diet up to a break point, so a linear-plateau dose response was observed. Pigs fed diets providing 208 to 236 g/kg DM did not differ in rate of protein deposition (PD). A maximum value of 87 (13.93 g N retained × 6.25) g PD/d was obtained when the diet supplied at least 208 g CP/kg DM. The broken-line regression analysis estimated dietary CP requirements at 211 g ideal CP (15.2 g total Lys)/kg DM. In the fattening pigs, there was a quadratic response (P < 0.01) in the rate of N retention as dietary CP content increased. Maximum N retention (18.7 g/d) was estimated from the first derivative of the function that relates the observed N retained (g/d) and dietary CP content (g/kg DM). This maximum value would be obtained by feeding a diet containing 185 g ideal CP (13.3 g total Lys)/kg DM and represents the maximum capacity of the IB × DU 50:50 pigs for protein accretion (i.e., 117 g PD/d). Consequently, cross breeding of IB dams with Duroc sires alters the metabolic response to dietary CP supply by increasing the potential of the crossbred to accrete protein and by modifying the pattern of PD response with BW.
Summer movement and growth of juvenile anadromous salmonids in small western Washington streams

Movements of juvenile coho salmon (Oncorhynchus kisutch), cutthroat trout (Oncorhynchus clarki clarki), and steelhead trout (Oncorhynchus mykiss) were studied by observations and recapture of marked individuals in three western Washington streams to test the hypotheses that few fish would move, downstream movement would predominate, movers would be initially smaller and grow slower after movement than residents, and habitat quality would influence movement. Contrary to predictions, from 28 to 60% of marked fish moved at least one habitat unit, and immigration of unmarked fish also indicated considerable movement. Upstream movement predominated but the stream with the step-pool/cascade channel type had fewer upstream movers and greater distances moved downstream. Coho movers were not smaller than nonmovers, as predicted based on assumptions that movement results from competitive exclusion. Habitat units that coho left were smaller and shallower but lower in density than units where coho remained. Thus, mov.
Designing and Implementing a Tool for Distributed Collaborative Traceability and Rationale Management

Traceability and rationale management are highly important—especially in distributed collaborative software development projects due to a lack of mutual awareness and informal coordination among the participating stakeholders. Therefore this papers presents a tool for extracting, visualizing, and analyzing the relationships between requirements and other artifacts, activities as well as users within a distributed software project using a collaborative development environment. Underlying requirements and the conceptual design of this tool are based on several real-world use cases demonstrating the respective value contribution of the tool’s functionality.
Assessing the quality of business process modelling techniques

The conceptual modelling of business processes is becoming popular. The number of techniques and tools is growing fast. At the same time, an appropriate framework for understanding the quality of these modelling techniques is lacking. We report upon the development of a framework for understanding the quality of business process modelling techniques, called the QME framework. The framework defines the elements that constitute a modelling technique and presents a number of quality properties as well as ways to operationalise them. The framework is applied to illustrate the quality of the Dynamic Essential Modelling of Organisations (DEMO) business modelling technique. Conclusions are drawn both on the quality of DEMO and on the application of the framework to study DEMO.