Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ultra-Low Phase Noise Class-F 2 CMOS Oscillator With 191 dBc/Hz FoM and Long-Term Reliability

In this paper, we propose a new class of operation of an RF oscillator that minimizes its phase noise. The main idea is to enforce a clipped voltage waveform around the LC tank by increasing the second-harmonic of fundamental oscillation voltage through an additional impedance peak, thus giving rise to a class-F 2 operation. As a result, the noise contribution of the tail current transistor on the total phase noise can be significantly decreased without sacrificing the oscillator's voltage and current efficiencies. Furthermore, its special impulse sensitivity function (ISF) reduces the phase sensitivity to thermal circuit noise. The prototype of the class-F 2 oscillator is implemented in standard TSMC 65 nm CMOS occupying 0.2 mm 2 . It draws 32-38 mA from 1.3 V supply. Its tuning range is 19% covering 7.2-8.8 GHz. It exhibits phase noise of -139 dBc/Hz at 3 MHz offset from 8.7 GHz carrier, translated to an average figure-of-merit of 191 dBc/Hz with less than 2 dB variation across the tuning range. The long term reliability is also investigated with estimated >10 year lifetime.
Karaoke callout: using social and collaborative cell phone networking for new entertainment modalities and data collection

We have developed a user-trainable query by humming (QBH) system that develops an error probability model of a user's singing. While the training is effective, it is also tedious and time consuming, requiring the user to sing dozens of melodies to the system before the system can be trained. To make training fun, we introduce a new interactive, distributed karaoke game, called Karaoke Callout, played over a cell phone. The user selects a song and sings it into the cell phone. The audio is sent to a server which rates the quality of the singing by measuring how closely it resembles a canonical example of the song stored in the server database, sending a score back to the user. The user may then challenge anyone in the phone's contact list. An SMS text challenge is sent to the challenged person's cell phone. The challenged person sings the song, attempting to better the performance of the challenger. This challenge may then be repeated, with either party selecting a new song with which to "call out" the other party. Over the course of an interaction, numerous examples of each party's singing are created and stored. These may then be used to train a QBH to the idiosyncrasies of each user's singing, as well as providing new query targets for the system.
Amlodipine and valsartan as components of a rational and effective fixed-dose combination

Pharmacological treatment of hypertension is effective in preventing cardiovascular and renal complications. Calcium antagonists and blockers of the renin-angiotensin system are widely used today to initiate antihypertensive therapy but, when given as monotherapy, do not suffice in most patients to normalize blood pressure. Combining the two types of agents considerably increases the antihypertensive efficacy, but not at the expense of a deterioration of tolerability. This is exemplified by the experience accumulated with the recently developed fixed dose combination containing the AT1-receptor blocker valsartan (160 mg) and the dihydropyridine amlodipine (5 or 10 mg). In a randomized trial, an 8-week treatment normalized blood pressure (<140/90 mmHg) within 8 weeks in a large fraction of hypertensive patients (78.4% and 85.2% using the 5/160 [n = 371] and 10/160 mg [n = 377] dosage, respectively). Like all AT1-receptor blockers valsartan has a placebo-like tolerability. Valsartan prevents to a large extent the occurrence amlodipine-induced peripheral edema. Both amlodipine and valsartan have beneficial effects on cardiovascular morbidity and mortality, as well as protective effects on renal function. The co-administration of these two agents is therefore very attractive, as it enables a rapid and sustained blood pressure control in hypertensive patients. The availability of a fixed-dose combination based on amlodipine and valsartan is expected therefore to facilitate the management of hypertension, to improve long-term adherence with antihypertensive therapy and, ultimately, to have a positive impact on cardiovascular and renal outcomes.
On weakly group-theoretical non-degenerate braided fusion categories

Fil: Natale, Sonia Lujan. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Centro de Investigacion y Estudios de Matematica. Universidad Nacional de Cordoba. Centro de Investigacion y Estudios de Matematica; Argentina
Brand recommendation leveraging heterogeneous implicit feedbacks

Collaborative Filtering (CF) is one of the most commonly used and successful recommendation techniques. The performance of traditional collaborative filtering is limited by the sparsity of data, and relies on explicit user feedbacks (ratings). However, the growing number of products and users will leave the user-item matrix sparser, and high-quality explicit feedbacks are not always available. In this paper, we focus on brand recommendation. A brand represents a set of products, which can ease the sparsity problem to some extent. We propose a learning-based recommendation approach, leveraging heterogeneous implicit feedbacks. First, we describe relations between brands by using the sequence of different types of behaviors, and extract path-based features to connect users to their unknown brands. Then we apply machine-learning approaches to predict the purchase probability using these features. Finally we recommend top-n ranked brands to users according to the predicted probability. Extensive experiments on large-scale real-world e-commerce data provided by Tmall.com demonstrate the effectiveness of our approach.
Distributed margin optimization using Spectrum Balancing in multi-user DSL systems

Optimizing the SNR margin in Dynamic Spectrum Management (DSM) systems can significantly improve stability of DSL networks in the presence of slowly varying noise. In this work, we present a novel distributed approach to the margin optimization problem where the network operator specifies the desired ratios of user margins relative to each other and, given this constraint, the Spectrum Balancing algorithm seeks the solution with the largest sum margin. By employing partial Lagrangian relaxation of a rate-adaptive problem each user optimizes its own transmit power spectrum while the Spectrum Management Center (SMC) iteratively updates the target margins of each user so that the system eventually converges to an optimal power allocation with desired margin ratios. Numerical simulations demonstrate fast convergence and show that significantly higher margins can be achieved with our approach compared to purely selfish optimization.
EEG Power-Spectral and Coherence Differences Between Attentional States during a Complex Auditory Task

Abstract This electroencephalographic (EEG) study was designed to explore the differences in power spectra and coherence associated with subjective levels of engrossment, or absorption, in an auditory task. Quantitative referential EEG activity was recorded using 19 electrodes while subjects (n = 17) listened to a story and indicated on a continuous basis, by means of a hand held switch, whether they were engrossed or non-engrossed in the story content. Results from power spectra data indicated significant differences between subjective engrossed and non-engrossed states (particularly in the theta and betal bands) with increased mean power during engrossed states. Differences in coherence were not shown to be significantly different. The results suggest that engrossed and non-engrossed states have different EEG correlates for power measures during specific complex tasks.
Unilateral fatiguing exercise and its effect on ipsilateral and contralateral resting mechanomyographic mean frequency between aerobic populations

The purpose of this investigation was to establish a better understanding of contralateral training and its effects between homologous muscles following unilateral fatiguing aerobic exercise during variable resting postural positions, and to determine if any observable disparities could be attributed to the differences between the training ages of the participants. Furthermore, we hypothesized that we would observe a contralateral cross‐over effect for both groups, with the novice trained group having the higher mechanomyographic mean frequency values in both limbs, across all resting postural positions. Twenty healthy male subjects exercised on an upright cycle ergometer, using only their dominate limb, for 30 min at 60% of their VO2 peak. Resting electromyographic and mechanomyographic signals were measured prior to and following fatiguing aerobic exercise. We found that there were resting mechanomyographic mean frequency differences of approximately 1.9 ± 0.8% and 0.9 ± 0.7%; 9.1 ± 0.3% and 10.2 ± 3.7%; 2 ± 1.8% and 3 ± 1.4%; and 0.9 ± 0.6% and 0.2 ± 1.3% between the novice and advanced trained groups (for the upright sitting position with legs extended 180°; upright sitting position with legs bent 90°; lying supine position with legs extended 180°; and lying supine with legs bent 90°, respectively), from the dominant and nondominant limbs, respectively. We have concluded that despite the relative matching of exercise intensity between groups, acute responses to contralateral training become less accentuated as one progresses in training age. Additionally, our results lend support to the notion that there are multiple, overlapping neural and mechanical mechanisms concurrently contributing to the contralateral cross‐over effects observed across the postexercise resting time course.
Snap & play: auto-generate personalized find-the-difference mobile game

According to the year 2010 report of the Entertainment Software Association [5], 42% of USA heads of households reported playing games on mobile devices, rising quickly from the 20% in 2002 and bringing huge market for mobile games. In this paper, by taking the popular game, Find-the-Difference (FiDi), as a concrete example, we explore new mobile game design principles and techniques for enhancing player's gaming experience in personalized, automatic, and dynamic aspects. Unlike traditional FiDi game, where image pairs (source image vs. target image) with M different patches are manually produced by game developer and players may feel boring or cheat after practicing all image pairs, our proposed Personalized FiDi (P-FiDi) mobile game may be played under a new Snap & Play mode. The player may first take photos with one s mobile device (or select from one's own albums). Then, these photos serve as source images, and the P-FiDi system automatically generates the counterpart target images by sequential operations of aesthetic image quality enhancement, image patch and differentiating style joint selection, music adaptation, dynamic difficulty level determination, and ultimate automatic image editing with a rich set of popular differentiating styles used in traditional FiDi game. Finally, the player enjoys the unique gaming with one's own (instant) photos and music, and the freedom to have new gaming image pairs any time. The user studies show that the P-FiDi mobile game is satisfying in terms of player experience.
Keep it simple: reward and task design in crowdsourcing

Crowdsourcing is emerging as an effective method for performing tasks that require human abilities, such as tagging photos, transcribing handwriting and categorising data. Crowd workers perform small chunks of larger tasks in return for a reward, which is generally monetary. Reward can be one factor for motivating workers to produce higher quality results. Yet, as highlighted by previous research, the task design, in terms of its instructions and user interface, can also affect the workers' perception of the task, thus affecting the quality of results. In this study we investigate both factors, reward and task design, to better understand their role in relation to the quality of work in crowdsourcing. In Experiment 1 we test a variety of reward schemas while in Experiment 2 we measure the effects of the complexity of tasks and interface on attention. The long-term goal is to establish guidelines for designing tasks with the aim to maximize workers' performance.
A control theory approach for analyzing the effects of data errors in safety-critical control systems

Computers are increasingly used for implementing control algorithms in safety-critical embedded applications, such as engine control, braking control and flight surface control. Addressing the consequent coupling of control performance with computer related errors, this paper develops a composite computer dependability/control theory methodology for analyzing the effects data errors have on control system dependability. The effect is measured as the resulting control error (defined as the difference between the desired value of a physical properly and its actual value). We use maximum bounds on this measure as the criterion for control system failure (i.e., if the control error exceeds a certain threshold, the system has failed). In this paper we a) present suitable models of computer faults for analysis of control level effects and related analysis methods, and b) apply traditional control theory analysis methods for understanding the effects of data errors on system dependability An automobile slip-control brake-system is used as an example showing the viability of our approach.
Testing Reversible Circuits

This paper is a review of the existing testing algorithms for reversible circuits. Although a great amount of effort has been made on finding means to build reversible gates and circuits, little research has been done on the modeling the faults of reversible circuits according to their process technology and consequently on the development of testing algorithms for reversible circuits. The existing techniques model the faulty wires in the circuit by stuck-at zero/one model. Here we review some interesting properties of reversible circuits with respect to stuck-at fault modeling and argue how they may ease the problem of testing compared to complexity of testing a classical irreversible circuit. For example it can be shown that any complete test set to detect single stuck-at faults is also a complete test for multiple stuck-at faults. Considering the high complexity of multiple faults testing, reversible circuits will benefit a great amount of cost reduction by testing tools only focusing on the single faults, knowing that multiple ones are also covered automatically.
Testing the potential paradoxes in “retrocausal” phenomena

Discussions with regard to potential paradoxes arising from “retrocausal” phenomena have been purely theoretical because so far no empirical effects had been established that allowed for empirical exploration of these potential paradoxes.In this article we describe three human experiments that showed clear “retrocausal” effects. In these neuropsychological, so-called, face-detection experiments, consisting of hundreds of trials per participant, we use brain signals to predict an upcoming random stimulus. The binary random decision, corresponding to showing a noisy cartoon face or showing only noise on a display with equal probability is taken after the brain signals have been measured. The prediction accuracy ranges from 50.5-56.5% for the 3 experiments where chance performance would be 50%. The prediction algorithm is based on a template constructed out of all the pre-stimulus brain signals obtained in other trials of that particular participant. This approach thus controls for individual difference in brain functioning. Subsequently we describe an experiment based upon these findings where the predictive information is used in part of the trials to determine the stimulus rather than randomly select that stimulus. In those trials we analyze what the brain signals tell us what the future stimulus would be and then we reverse the actual future that is presented on the display. This is a ‘bilking’ condition. We analyze what the consequence of the introduction of this bilking condition is on the accuracy of the remaining (normal) trials and, following a suggestion inferred from Thorne et al, we also check what the effect is on the random decision to either bilk or not bilk the specific trial. The bilking experiment is in progress and the results so far do not allow for conclusions and are presented only as an illustration.Discussions with regard to potential paradoxes arising from “retrocausal” phenomena have been purely theoretical because so far no empirical effects had been established that allowed for empirical exploration of these potential paradoxes.In this article we describe three human experiments that showed clear “retrocausal” effects. In these neuropsychological, so-called, face-detection experiments, consisting of hundreds of trials per participant, we use brain signals to predict an upcoming random stimulus. The binary random decision, corresponding to showing a noisy cartoon face or showing only noise on a display with equal probability is taken after the brain signals have been measured. The prediction accuracy ranges from 50.5-56.5% for the 3 experiments where chance performance would be 50%. The prediction algorithm is based on a template constructed out of all the pre-stimulus brain signals obtained in other trials of that particular participant. This approach thus controls for individual difference in br.
Telomere Length Abnormalities Occur Early in the Initiation of Epithelial Carcinogenesis

Purpose: Telomeres help maintain chromosomal integrity. Dysfunctional telomeres can cause genetic instability in vitro and an increased cancer incidence in telomerase knock out mouse models. We recently reported that telomere shortening was a prevalent alteration in human prostate, pancreas, and breast cancer precursor lesions. In the present study, we address whether the previous findings are broadly applicable to human epithelial cancer precursors in general. Experimental Design: Surgical specimens of epithelial cancer precursor lesions from the urinary bladder, esophagus, large intestine, oral cavity, and uterine cervix were examined using a recently developed technique for direct in situ telomere length assessment in formalin-fixed human tissue specimens. Results: Widespread telomere length abnormalities were nearly universal (97.1% of cases) in the preinvasive stages of human epithelial carcinogenesis in all sites examined in this series, with telomere shortening the predominant abnormality (88.6% of cases). Conclusions: Telomere length abnormalities appear to be one of the earliest and most prevalent genetic alterations acquired in the multistep process of malignant transformation. These findings support a model whereby telomere dysfunction induces chromosomal instability as an initiating event in many, perhaps most, human epithelial cancers. Together with previous findings from the prostate and pancreas, the percentage of intraepithelial neoplasia lesions showing telomere length abnormalities is 95.6%. The implications of these findings include the potential that telomere length assessment in situ may be a widely useful biomarker for monitoring disease prevention strategies and for improved early diagnosis.
MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms

We present a novel algorithm for blind denoising of images corrupted by mixed impulse, Poisson, and Gaussian noises. The algorithm starts by applying the Anscombe variance-stabilizing transformation to convert the Poisson into white Gaussian noise. Then it applies a combinatorial optimization technique to denoise the mixed impulse Gaussian noise using proximal algorithms. The result is then processed by the inverse Anscombe transform. We compare our algorithm to state of the art methods on standard images, and show its superior performance in various noise conditions.
Recognition of Face Using Neural Network

— Advancement in Artificial Intelligence has lead to the developments of various “smart” devices. The task of face Recognition has been actively researched in recent years. Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authentication process simplification in computer systems has raised significant attention to reliability and efficiency of biometric systems. Modern biometric systems still face much reliability and efficiency related issues such as database search speed, errors while recognizing of biometric information or automating biometric feature extraction. In face recognition, many methods are used but due to advancement there are some new methods and algorithm used for recognition of face i.e. line edges map, support vector machine etc. for face recognition. A number of current face recognition algorithms use face representations found by supervised and unsupervised statistical methods. In this paper we use a neural network approach for face recognition system. We are creating a supervised multilayer feed forward network model. We will design a neural network for face recognition system which first detect the face and then recognize the face. Specifically some network models use a set of desired outputs to compare with the output and compute an error to make use of in adjusting their weights. Such learning rules are termed as Supervised Learning One such network with supervised learning rule is the Multi-Layer Perceptron (MLP) model. Back propagation algorithm is used to train the network, calculate error and modify weights.
Common inhibitory mechanism in human inferior prefrontal cortex revealed by event-related functional MRI.

Inhibition of an ongoing reaction tendency for adaptation to changing environments is a major function of the human prefrontal cortex. This function has been investigated frequently using the go/no-go task and set-shifting tasks such as the Wisconsin Card Sorting Test (WCST). Studies in humans and monkeys suggest the involvement of the dorsolateral prefrontal cortex in the two task paradigms. However, it remains unknown where in the dorsolateral prefrontal cortex this function is localized, whether a common inhibitory mechanism is used in these task paradigms and how this inhibitory function acts on two different targets, i.e. the go response in the go/no-go task and the cognitive set in the WCST. In the go/no-go task of this study, subjects were instructed to either respond (go trial) or not respond (no-go trial), depending on the cue stimulus presented. The signals of functional MRI (fMRI) related to the inhibitory function should be transient by nature. Thus, we used the temporal resolution of fMRI (event-related fMRI) by which transient signals in go and no-go trials can be analysed separately and compared with each other. We found a focus that showed transient no-go dominant activity in the posterior part of the inferior frontal sulcus in the right hemisphere. This was true irrespective of whether the subjects used their right or left hands. These results suggest that the transient activation in the right inferior prefrontal area is related to the neural mechanism underlying the response inhibition function. Furthermore, this area was found to be overlapped spatially with the area that was activated transiently during cognitive set shifting in the WCST. The transient signals in the go/no-go task peaked 5 s after the transient expression of the inhibitory function, and the transient signals in the WCST peaked 7s after the transient expression, reflecting different durations of neuronal activity in the two inhibitory task paradigms. These results imply that the right inferior prefrontal area is commonly involved in the inhibition of different targets, i.e. the go response during performance of the go/no-go task and the cognitive set during performance of the WCST.