Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal estimation with low infinity-norm error by minimizing the mean p-norm error

We consider the problem of estimating an input signal from noisy measurements in both parallel scalar Gaussian channels and linear mixing systems. The performance of the estimation process is quantified by the ℓ∞-norm error metric (worst case error). Our previous results have shown for independent and identically distributed (i.i.d.) Gaussian mixture input signals that, when the input signal dimension goes to infinity, the Wiener filter minimizes the ℓ∞-norm error. However, the input signal dimension is finite in practice. In this paper, we estimate the finite dimensional input signal by minimizing the mean ℓp-norm error. Numerical results show that the ℓp-norm minimizer outperforms the Wiener filter, provided that the value of p is properly chosen. Our results further suggest that the optimal value of p increases with the signal dimension, and that for i.i. d. Bernoulli-Gaussian input signals, the optimal p increases with the percentage of nonzeros.
Audio-visual perception of Mandarin lexical tones in AX same-different judgment task

Two same-different discrimination tasks were conducted to test whether Mandarin and English native speakers use visual cues to facilitate Mandarin lexical tone perception. In the experiments, the stimuli were presented in 3 modes: audioonly (AO), audio-video (AV) and video-only (VO) under the clear and two levels of signal-to-noise ratio (SNR) -6dB and - 9dB noise condition. If the speakers’ perception of AV is better than that of AO, the extra visual information of lexical tones contributes tone perception. In Experiment 1 and 2, we found that Mandarin speakers had no visual augmentation under clear and noise conditions. For English speakers, on the other hand, extra visual information hindered their tone perception (visual reduction) under SNR -9dB noise. This suggests that English speakers rely more on auditory information to perceive lexical tones. Tone pairs analysis in both experiments found that visual reduction in tone pair T2- T3 and visual augmentation in tone pair T3-T4. It indicates that acoustic tone features (e.g. duration, contour) can be seen and be involved in the process of audiovisual perception. Visual cues facilitate or inhibit tone perception depends on whether the presented visual features of the tone pairs are distinctively recognised or highly confusing to each other.
Affinity and selectivity of [11C]‐(+)‐PHNO for the D3 and D2 receptors in the rhesus monkey brain in vivo

Although [11C]‐(+)‐PHNO has enabled quantification of the dopamine‐D3 receptor (D3R) in the human brain in vivo, its selectivity for the D3R is not sufficiently high to allow us to disregard its binding to the dopamine‐D2 receptor (D2R). We quantified the affinity of [11C]‐(+)‐PHNO for the D2R and D3R in the living primate brain. Two rhesus monkeys were examined on four occasions each, with [11C]‐(+)‐PHNO administered in a bolus + infusion paradigm. Varying doses of unlabeled (+)‐PHNO were coadministered on each occasion (total doses ranging from 0.09 to 5.61 μg kg−1). The regional binding potential (BPND) and the corresponding doses of injected (+)‐PHNO were used as inputs in a model that quantified the affinity of (+)‐PHNO for the D2R and D3R, as well as the regional fractions of the [11C]‐(+)‐PHNO signal attributable to D3R binding. (+)‐PHNO in vivo affinity for the D3R (Kd/fND ∼ 0.23–0.56 nM) was 25‐ to 48‐fold higher than that for the D2R (Kd/fND ∼ 11–14 nM). The tracer limits for (+)‐PHNO (dose associated with D3R occupancy ∼ 10%) were estimated at ∼0.02–0.04 μg kg−1 injected mass for anesthetized primate and at 0.01–0.02 μg kg−1 for awake human positron emission tomography (PET) studies. Our data enabled a rational design and interpretation of future PET studies with [11C]‐(+)‐PHNO. Synapse, 2012. © 2011 Wiley Periodicals, Inc.
Real time human tracking system for defense application

Identifying human presence in a visual surveillance system is crucial for variety of applications. Various tracking systems are also refined with a vision to detect human beings. However lesser resolution pictures make them a challenging one. So a stereo vision based human detection and tracking system is proposed for recognition of intruders in the human access controlled areas and can help in observing and inspecting illegal activities. In this depth range of the system is used in parameter estimation. The human presence is identified by skin detection where segmentation is done in HSV color space. Human tracking is a separate hardware system coupled through a serial port. For targeting servo motors are used, along a buzzer and a laser light for pointing the target. Here the presence of animals is also eliminated by a change in algorithm. The proposed implementation need only less protection and climate conditions are least influenced.
Event-Related Functional Network Identification: Application to EEG Classification

Recent works on brain functional analysis have accentuated the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is important to relate mechanisms of brain functions and the basis for classifying brain states. In this work, the network patterns related to neural tasks based on synchronization measure phase-locking value (PLV) in an electroencephalogram (EEG) are analyzed. Based on network dissimilarities between the rest and motor imagery tasks, important nodes and channel pairs corresponding to motor tasks are identified. A framework is developed to identify these most reactive channel pairs that form the subject-specific functional network. The identified functional network corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even higher variation corresponding to tasks. To demonstrate the potential of the developed framework to brain-computer interface, identified network patterns are employed as features for classification of tasks. Analysis performed with the two classes (left-hand and right-hand motor imagery EEG data) showed that the proposed approach yielded better classification results compared to earlier band-power based approaches for single trial analysis.
Estimation Using Contingent Valuation Data from a Dichotomous Choice with Follow-Up Questionnaire

Abstract Our original article proposed that the two yes–no willingness-to-pay (WTP) responses produced by this survey format should be viewed as a pair of jointly distributed (correlated) discrete random variables. The comparison model—the “conventional” approach to estimation using these types of data—contained a programming error. Correction alters the results from the comparison model, but our original claim—that estimation by conventional methods can produce misleading inferences if ours is the appropriate specification—still stands. This is especially true if negative WTP is precluded when E[WTP] is calculated.
Parallel implementation of real-time semi-global matching on embedded multi-core architectures

Embedded real-time algorithms are often realized with dedicated hardware, exhibiting high production costs and low programming flexibility thereafter. For instance, semi-global matching for stereo image processing, including complex data flows, traditionally runs on customized hardware modules. Combining the processing and memory capabilities of multiple individual cores, emerging embedded multi-core technologies address these problems. However, considering concurrency issues (e.g., data races and lock contentions), parallel programming requires experienced programmers and technology-specific techniques (e.g., synchronization libraries) and tools (e.g., parallel profilers), which are often not available on embedded platforms. In this work, we introduce a parallel version of a semi-global matching algorithm and demonstrate within this case study runtime optimizations necessary to meet real-time requirements. We also show structured steps of the applied parallelization workflow, illustrating an efficient migration strategy to multi-core platforms using runtime information (e.g., profiles and hardware counters). Finally, to evaluate the resulting performance characteristics, we compare the runtime behavior of the parallel version running on a Freescale P4080 processor with reference values taken on an Intel i7, a field-programmable logic device, an extended general purpose processor and a GPU.
Classification of high-energized Gabor responses using Bayesian PCA for Human Face Recognition

Feature extraction methods are based on finding fiducial points (or local small areas) on a face and representing corresponding information in an efficient way. In this paper a novel method is proposed, based on selecting peaks (high-energized points) of the Gabor wavelet responses as feature points. Feature vectors are constructed from these feature points to represent the facial topography. These extracted feature vectors are used to obtain similarity scores of different features, using Bayesian Principal Component Analysis (PCA). Use of these peaks forms the basis of the work. These similarity scores are used for class assignment. Our approach not only reduces computational complexity, but also improves the performance in the presence of occlusions. As both Gabor filter responses and Bayesian Principal Component Analysis reduces intrapersonal variations, both the methods are integrated for better accuracy of face recognition. The efficiency of our method is demonstrated by the experiment on 1000 images from the FRAV2D face database where the images vary in pose, expression, illumination and scale, and 400 images from the ORL face database, where the images slightly vary in pose. Our method has shown 99% recognition accuracy for the FRAV2D database and 100% for the ORL database.
Fusing On-Body Sensing with Local and Temporal Cues for Daily Activity Recognition

Automatically recognizing people's daily activities is essential for a variety of applications, such as just-in-time content delivery or quantified self-tracking. Towards this, researchers often use customized wearable motion sensors tailored to recognize a small set of handpicked activities in controlled environments. In this paper, we design and engineer a scalable, daily activity recognition framework, by leveraging two widely adopted commercial devices: Android smartphone and Pebble smartwatch. Deploying our system outside the laboratory, we collected a total of more than 72 days of data from 12 user study participants. We systematically show the usefulness of time, location, and wrist-based motion for automatically recognizing 10 standardized activities, as specified by the American Time Use Survey taxonomy. Overall, we achieve a recognition accuracy of 76.28% for personalized models and 69.80% for generic, interpersonal models.
Interactions between scatter factors and their receptors: hints for therapeutic applications

The scatter factors, which include he patocyte growth factor and macrophage stimulating protein, stand out from other cytokines because of their uncommon biological properties. In additionto promoting cell growth and protection from apoptosis, they are involved in the control of cell dissociation, migration into extracellular matrices, and a unique process of differentiation called ‘branching morphogenesis’. Through the concerted regulation of these complex phenomena, scatter factors promote development, regeneration, and reconstruction of normal organ architecture. In transformed epithelia, scatter factors can mediate tumor invasive growth, a harmful feature of neoplastic progression in which cancer cells invade surrounding tissues, penetrate across the vascular walls, and eventually disseminate throughout the body, giving rise to systemic metastases. A much‐debated issue in basic biology, which has strong implications for experimental medicine, is how to dissociate the favorable effects of growth factors from their adverse ones. Accordingly, to find agonists or antagonists with potential therapeutic applications is a crucial undertaking for current research. Domain‐mapping analyses of growth factor molecules can help to isolate specific structural requirements for the induction of selective biological effects. Based on the observation that certain growth factors must undergo posttranslational modifications to exert a full response, it is possible to interfere with their activation mechanisms to modulate their functions. Finally, the identification of cell type‐specific coreceptors able to potentiate their activity allows drawing of a functional body map, where some organs or tissues may be more responsive than others to growth factors. This review is focused on how, and to what extent, scatter factors can behave ‘well’ or ‘badly’ according to their molecular structure, the way they are activated, and the way they interact with cell surface receptors and coreceptors. — Trusolino, L. , Pugliese, L. , Comoglio, P. M. Interactions between scatter factors and their receptors: hints for therapeutic applications. FASEB J. 12, 1267– 1280 (1998)
Optimizing Yield in Global Routing

We present the first efficient approach to global routing that takes spacing-dependent costs into account and provably finds a near-optimum solution including these costs. We show that this algorithm can be used to optimize manufacturing yield. The core routine is a parallelized fully polynomial approximation scheme, scaling very well with the number of processors. We present results showing that our algorithm reduces the expected number of defects in wiring by more than 10 percent on state-of-the-art industrial chips
The Role of Similarities based on Gender and Work-family Conflict in Mentoring Relationships - The Case of IT Careers

Mentoring has long been considered important in career advancement in many fields. It is important to pay attention to and counter these trends because mentoring appears to be related to a number of organizational and individual-level outcomes. In this research in progress, we examine certain factors that may lead to a more effective mentoring relationship, thereby increasing the likelihood that the relationship will be lasting and beneficial for the individual and organization. Specifically, we suggest similarities based on gender and workfamily conflict will be related to an effective mentoring relationships. Propositions are presented with respect to these relationships. Although we do not directly link mentoring with retention, the outcomes discussed above will likely result in reduced turnover in IT-related jobs. These outcomes are likely to be negatively associated with women's likelihood of remaining in IT fields and their performance and advancement in the IT field.
CLAIRVOYANCE: A Fast And Robust Precision Mosaicing System for Gigapixel Images

In this paper, we present CLAIRVOYANCE: a fully automatic photomosaicing system for building ultra high-resolution composited images from image sequences captured by tailored in-house motorized active pan-tilt digital camera units. Our stand-alone mobile systems built over the past five years are computationally fast, robust to various datasets and deliver unprecedented consumer-level image quality. We describe our simple yet novel lens calibration and radiometric correction procedures based on a fast block matching algorithm. All of our core image stitching components are based on the 2D Fourier phase correlation principle, and are thus easily amenable to hardware LSI implementation. We validate our approach by presenting sharp photomosaics obtained from a few hundreds up to a few thousands data sets of images
1 Dimensionality Reduction and Microarray data

Microarrays are being currently used for the expression lev els of thousands of genes simultaneously. They present new analytical challen ges because they have a very high input dimension and a very low sample size. It is highly compl ex to analyse multi-dimensional data with complex geometry and to identify low-dimensional “principal objects” that relate to the optimal projection while losing the least amount of info rmation. Several methods have been proposed for dimensionality reduction of microarray d ta. Some of these methods include principal component analysis and principal manifold s. This article presents a comparison study of the performance of the linear principal compon ent analysis and the non linear local tangent space alignment principal manifold methods o n uch a problem. Two microarray data sets will be used in this study. A classification model wi ll be created using fully dimensional and dimensionality reduced data sets. To measure the amount of information lost with the two dimensionality reduction methods, the level of perf ormance of each of the methods will be measured in terms of level of generalisation obtaine d by the classification models on previously unseen data sets. These results will be compared with the ones obtained using the fully dimensional data sets.
Analysis and characterization of PLL-based retrodirective array

In this paper, we present a new phase-locked loop (PLL) architecture that is capable of providing phase conjugation as required for retrodirective array action. An analysis is performed on the architecture, which shows that the presence of nonlinear mixing products gives rise to an additional and substantially larger RMS noise-induced phase-error component than is suggested by analysis based on ideal mixer properties. In addition, the analysis allows us to quantify the effect that a Doppler shifted signal has on phase-conjugation error. For the purpose of demonstration, we have constructed and characterized a six-element array operating using commercially available components at 1 GHz. Near-perfect retrodirective action is presented over a 360deg azimuth sweep, with 16-dB array gain. Due to the inherent isolation between input and output signals, and programmable retransmit frequency, the PLL approach introduced in this paper offers considerable practical advantage when used within retrodirective antenna arrays, as compared to the classical mixer-based approach where mixer losses and RF-to-IF frequency isolation are major issues
Global solar irradiation prediction using a multi-gene genetic programming approach

In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach.
Profound Vitamin D Deficiency in a Diverse Group of Women during Pregnancy Living in a Sun-Rich Environment at Latitude 32°N

Objective: Determine prevalence of vitamin D deficiency (VDD) in a diverse group of women presenting for obstetrical care at two community health centers in South Carolina at latitude 32°N. Methods and Design: Any pregnant woman presenting for care at 2 community health centers was eligible to participate. Sociodemographic and clinical history were recorded. A single blood sample was taken to measure circulating 25(OH)D as indicator of vitamin D status [25(OH)D < 20 ng/mL (50 nmol/L deficiency; <32 ng/mL (80 nmol/L) insufficiency]. Total serum calcium, phosphorus, creatinine, and intact parathyroid hormone also were measured. Results: 559 women, [mean age 25.0 ± 5.4 (range 14–43) years] participated: African American (48%), Hispanic (38%), Caucasian/Other (14%). Mean gestational age was 18.5 ± 8.4 (median 14.6, range 6.4–39.6) weeks' gestation. 48% were VDD; an additional 37% insufficient. Greatest degree was in the African American women (68% deficient; 94% insufficient). In multivariable regression, 25(OH)D retained a significant negative association with PTH (P < .001). Conclusions: VDD was high in a diverse group of women, greatest in those of darker pigmentation. The negative correlation between 25(OH)D and PTH confirms their corroborative use as biomarkers of VDD. These findings raise the issue of adequacy of current vitamin D recommendations for pregnant women.
A novel tag access scheme for low power L2 cache

Tag comparisons occupy a significant portion of cache power consumption in the highly associative cache such as L2 cache. In our work, we propose a novel tag access scheme which applies a partial tag-enhanced Bloom filter to reduce tag comparisons by detecting per-way cache misses. The proposed scheme also classifies cache data into hot and cold data and the tags of hot data are compared earlier than those of cold data exploiting the fact that most of cache hits go to hot data. In addition, the power consumption of each tag comparison can be further reduced by dividing the tag comparison into two micro-steps where a partial tag comparison is performed first and, only if the partial tag comparison gives a partial hit, then the remaining tag bits are compared. We applied the proposed scheme to an L2 cache with 10 programs from SPEC2000 and SPEC2006. Experimental results show average 23.69% and 8.58% reduction in cache energy consumption compared with the conventional serial tag-data access and the other existing methods, respectively.
A computer network is a social network

have investigated its effects on ad-hoc groups. Yet for groups who have long-term agendas, building trust is an important component of the team-building process. In general, trust and relationship building was very crucial to all of the teams observed. For this reason, members cited the importance of meeting face-to-face to build trust, in fact at considerable expense to the company. In a study targeting trust in virtual teams, Jarvenpaa and Leidner [1 ] found that trust in virtual teams may form fast, but may not be substantial. What warrants further study is understanding how face-to-face meeting can work in combination with virtual meetings so that the members can establish and maintain trust. This is especially true in a large organization where the virtual technologies are crossing over organizational barriers.