Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric Decentralized Flocks

This paper analyzes the transient dynamics of one dimensional flocks, platoons, i.e., a finite collection of identical vehicles moving on the line, with a single leader with independent motion. We show for a class of platoon control laws that if the information flow is asymmetric then a motion change of the leader will cause system transients with amplitudes that grow at an exponential rate as the length of the platoon increases. With suitable choice of the control parameters the system is asymptotically stable and in steady state all vehicles move at the same velocity as the leader and at the required separation. When the leader changes velocity, over very long time scales the vehicles in the platoon tend to the steady state dictated by the leader's new velocity. The transient dynamics in the intermediary regime can however appear quite unstable, where the trailing vehicle can undergo oscillatory motion with amplitudes that grow exponentially large with the number of cars N in the platoon, or may be irresponsive over an exponentially long time to the change in the motion of the leader. In this paper we prove that if the control law is asymmetric then such transient errors, measured in terms of displacement between the leader and the trailing car, grow at an exponential rate in N, the length of the platoon. This contrasts sharply with the symmetric (bidirectional) case when such transient errors grow only linearly in the length of the platoon, the theoretical minimum for decentralized linear time-invariant platoon control systems with a constant vehicle spacing policy. These results suggest that symmetry of the information flow is an important design parameter for safe control laws for platoons.
Use of Monoclonal Antibody MIB-1 in Differential Diagnosis of Uterine Cervical Glandular Neoplasm

This study was undertaken to evaluate the significance of MIB-1 immunoreactivity in the discrimination of differential diagnosis of benign cervical lesion, endocervical glandular dysplasia, and endocervical adenocarcinoma on formalin-fixed, paraffinembedded tissue. Fifty-two cases were selected from the pathology files of Korea University Hospital from 1994 to 1998 and were composed of 25 cases of endocervical adenocarcinoma, including three cases in which squamous cell carcinoma coexists; six cases of endocervical glandular dysplasia associated with squamous cell carcinoma or cervical intraepithelial neoplasia (CIN); and 21 cases of chronic endocervicitis. Immunohistochemical staining for MIB-1 was performed after microwave treatment on formalin-fixed, paraffin-embedded tissue sections. MIB-1 expression was evaluated according to the labeling indexes. The labeling indexes were calculated for 52 specimens by means of image analysis (Quantification of Proliferation Index). The results are as follows; There was diffuse strong MIB-1 reactivity in endocervical adenocarcinoma (labeling index 37.6-61.4%, mean 54.9%), moderate spotty reactivity in endocervical glandular dysplasia (labeling index 27.9-42.6%, mean 35.1%), and minimal to focal reactivity in chronic endocervicitis (labeling index 0-24.4%, mean 5.2%). Significant differences in MIB-1 labeling indexes existed among the chronic endocervicitis group, the endocervical glandular dysplasia group, and endocervical adenocarcinoma group. In conclusion, MIB-1 immunostaining is of value in the differential diagnosis among benign cervical lesions, endocervical glandular dysplasia, and endocervical adenocarcinoma.
Sufficient Conditions for Generic Feedback Stabilizability of Switching Systems via Lie-Algebraic Solvability

We address the stabilization of switching linear systems (SLSs) with control inputs under arbitrary switching. A sufficient condition for the stability of autonomous (without control inputs) SLSs is that the individual subsystems are stable and the Lie algebra generated by their evolution matrices is solvable. This sufficient condition for stability is known to be extremely restrictive and therefore of very limited applicability. Our main contribution is to show that, in contrast to the autonomous case, when control inputs are present the existence of feedback matrices for each subsystem so that the corresponding closed-loop matrices satisfy the aforementioned Lie-algebraic stability condition can become a generic property, hence substantially improving the applicability of such Lie-algebraic techniques in some cases. Since the validity of this Lie-algebraic stability condition implies the existence of a common quadratic Lyapunov function (CQLF) for the SLS, our results yield an analytic sufficient condition for the generic existence of a control CQLF for the SLS.
FAUST: Dataset and Evaluation for 3D Mesh Registration

New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it. Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking. The problem is particularly challenging for non-rigid and articulated objects like human bodies. While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult. We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments. We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology. To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth. We find that current shape registration methods have trouble with this real-world data. The dataset and evaluation website are available for research purposes at http://faust.is. tue.mpg. de.
EDUCACIÓN PRENATAL Y SU RELACIÓN CON EL TIPO DE PARTO: UNA VÍA HACIA EL PARTO NATURAL

SUMMARY Objective: To determine that an educative intervention during pregnancy has effects on the pregnancy, delivery and newborn. Method: A semi experimental study design was made in series of normal cases with 940 primiparous, since 20 weeks of gestation in two Municipal Centers of Health, Concepcion, Chile, between 2001 and 2004. It was applied an educative intervention in 8 sessions to 40 primiparous with his respective partner and doula. The results were compared with a reference group of 55 normal primiparous. Results: The comparison between the study and reference groups show significant difference in: pregnancy pathology, 17.5% versus 47.3% (p<0.05); use of oxytocin, 40 % versus 78.2% (p<0.01); need of episiotomy, 64.9% versus 100% (p<0.01); caesarean section rate, 7.5% versus 27.3% (p<0.01). Conclusion: Education during gestation using protocols in the prenatal control reduced the number of unnecessary routine intervention during pregnancy and birth.
Semi-Global Multi-Frequency Estimation in the Presence of Deadzone and Saturation

A hybrid observer to estimate the frequencies of a signal composed of a finite number of sinusoids and measured through saturation and deadzone nonlinearities is proposed. The observer design exploits the Immersion and Invariance technique combined with a classic high-gain approach, yielding semi-global exponential convergence to zero of the estimation errors. Simulation results are provided to illustrate the effectiveness of the observer.
Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs

Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially underdetermined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields (CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.
3D ShapeNets: A deep representation for volumetric shapes

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
Brazilian Science: Riding a Gusher

Over the past 8 years, Latin America9s largest nation has begun to boom. Its economy is growing fast, and it has become a player in world affairs, reveling in an unprecedented bout of self-confidence. It will host the World Cup in 2014 and the Olympics 2 years later. The good times are lifting science, too. Between 1997 and 2007 the number of Brazilian papers in indexed, peer-reviewed journals more than doubled to 19,000 a year. Brazil now ranks 13th in publications, according to Thomson Reuters, having surpassed the Netherlands, Israel, and Switzerland. Brazil9s universities awarded twice as many Ph.D.s this year as they did in 2001, and thousands of new academic jobs have opened up on 134 new federal campuses. It9s a reversal of fortune for a nation that during the 1990s was beset by dire economic problems. But Brazil is not formidable yet; its scientific output trails its ambitions. The country produces few high-impact papers and only a trickle of patents. Its primary and secondary public education system is in shambles, leaving the nation of 195 million chronically short of technical workers.
Methadone vs. L-alpha-acetylmethadol (LAAM) in the Treatment of Opiate Addiction: A Meta-Analysis of the Randomized, Controlled TriaIs

The authors conducted a meta-analysis of the reported randomized, controlled trials comparing methadone to I. -alpha-acetylmethadol (LAAM) to assess the efficacy of LAAM relative to methadone in the treatment of opiate addiction. All studies were conducted in standard outpatient opiate addiction treatment clinics. Most patients were men from lower socioeconomic strata. A statistically significant risk difference favoring methadone was detected for retention-in-treatment and discontinuation of treatment because of side effects. The risk difference for illicit drug use favored LAAM, but was not significant. A small treatment difference in favor of methadone was noted. LAAM does appear to be a relatively effective alternative in the treatment of opiate addiction, more so in certain select situations. (Am J Addict 1997; 6:339–349)
Selective l1 minimization for sparse recovery

—Motivated by recent approaches to switched linear system identiﬁcation based on sparse optimization, the paper deals with the recovery of sparse solutions of underdetermined systems of linear equations. More precisely, we focus on the associated convex relaxation where the ℓ 1 -norm of the vector of variables is minimized and propose a new iteratively reweighted scheme in order to improve the conditions under which this relaxation provides the sparsest solution. We prove the convergence of the new scheme and derive sufﬁcient conditions for the convergence towards the sparsest solution. Experiments show that the new scheme signiﬁcantly improves upon the previous approaches for compressive sensing. Then, these results are applied to switched system identiﬁcation.
A Generalized Gossip Algorithm on Convex Metric Spaces

A consensus problem consists of a group of dynamic agents who seek to agree upon certain quantities of interest. This problem can be generalized in the context of convex metric spaces that extend the standard notion of convexity. In this paper we introduce and analyze a randomized gossip algorithm for solving the generalized consensus problem on convex metric spaces, where the communication between agents is controlled by a set of Poisson counters. We study the convergence properties of the algorithm using stochastic calculus. In particular, we show that the distances between the states of the agents converge to zero with probability one and in the rth mean sense. In the special case of complete connectivity and uniform Poisson counters, we give upper bounds on the dynamics of the first and second moments of the distances between the states of the agents. In addition, we introduce instances of the generalized consensus algorithm for several examples of convex metric spaces together with numerical simulations.
The Effectiveness of a DEAL-Based Intervention to Reduce Stress and Depression Symptoms

Introduction: Many researchers have emphasized the importance of teaching stress management and self-care skills to medical students as they are vulnerable to develop psychological health problems. The researchers designed a 4-hour intervention based on the DEAL model to address these problems. This study aimed to determine outcomes of the DEAL-based intervention on medical students’ stress, anxiety and depression symptoms. Methods: A randomized controlled trial study was conducted on 171 medical students. Their stress, anxiety and depression symptoms were measured by the 21-item Depression Anxiety Stress Scale at five intervals; at 2 weeks before the intervention, and at 1 week, 8 weeks, 16 weeks and 32 weeks after the intervention. The mixed model ANCOVA was applied to determine the effect of the intervention on the participants’ psychological health at five intervals. Results: A total of 153 medical students (intervention = 73 and control = 80) completed this study. Results showed that the intervention group experienced significantly lower stress and depression symptoms than the control group. Although anxiety scores are consistently lower in the intervention group, no significant differences between groups were found. Conclusion: These results support the favourable outcomes of the intervention on psychological health of medical students. It is a promising intervention to be considered by medical schools as it consumes minimal amount of time, money, training and man power as well can be implemented easily.
Understanding deep image representations by inverting them

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.
Cutaneous Melanoma: A Test Field for Immunotherapy and a Medical Challenge

: Cutaneous melanoma (CM) is the fourth tumor in frequency, and that whose incidence increases faster. In half of the cases, CM arises from pre-existing nevi. CM is a highly heterogeneous tumor, whose degree of differentiation varies among different hosts, and such heterogeneity is probably based on the accumulation of mutations that determine transitions from normal melanocytic stem cells (cid:1) mutated benign melanocytic stem cells (cid:1) malignant melanocytic stem cells (cid:1) clonogenic melanocytic cells. These populations may express different antigens and would therefore trigger the proliferation of different T and B cell clones. Early diagnosis is the clue for the cure of CM; when visceral metastatic disease is established, the prognosis is somber. This is especially so since CM is quite resistant to chemotherapy, and some of the reasons for such resistance will be discussed here. However, CM has proved to be sensitive to immunological effectors, although the mechanism of such sensitivity is still being investigated. These effectors range from therapeutic vaccines, in vitro expanded cytotoxic lymphocytes, cytokines, and monoclonal antibodies. Finally, we will discuss new therapeutic approaches that include the combination of immune modulators and vaccines which are being assayed in light of recent tumor immunology research.
Effects of fenofibrate, a PPAR-α ligand, on the haemodynamics of glycerol-induced renal failure in rats

The modulating effect of peroxisome proliferator-activated receptor α ligand on haemodynamic effects of phenylepherine (PE), angiotensin II (AII), endothelin 1 (ET1), acetylcholine (Ach), sodium nitroprusside (SNP) and isoproterenol (ISO) were evaluated in glycerol-induced acute kidney injury in rats. The effect of PE on fenofibrate-treated animals was a dose-dependent increase in mean arterial blood pressure (MAP). For AII and ET1, MAP was also increased for the fenofibrate group but not in a dose-dependent fashion. On the medullary blood flow (MBF), while the lower doses of PE and AII increased the perfusion unit on the fenofibrate-treated group, the higher doses decreased the perfusion unit. The ET1 increased the perfusion unit on this group but not in dose-dependent fashion. The effects of PE and AII on the cortical blood flow (CBF) of fenofibrate-treated group is similar to that of MBF for the same group but not for ET1. The effect of Ach, SNP and ISO in all the groups was the decrease in MAP. ISO caused dose-dependent increase in MBF of fenofibrate-treated group. The effect of Ach, SNP and ISO on the CBF perfusion unit was that of the increase for the fenofibrate-treated group. The study showed that fenofibrate did not attenuate increased blood pressure induced by PE, AII and ET1 but caused enhanced vasodilation by Ach, SNP and ISO.
Convergence of an Upwind Finite-Difference Scheme for Hamilton–Jacobi–Bellman Equation in Optimal Control

This technical note considers convergence of an upwind finite-difference numerical scheme for the Hamilton-Jacobi-Bellman equation arising in optimal control. This effective scheme has been well-adapted and successfully applied to many examples. Nevertheless, its convergence has remained open until now. In this note, we show that the solution from this finite-difference scheme converges to the value function of the associated optimal control problem.
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.
3D Action Recognition from Novel Viewpoints

We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space. The model is a deep convolutional neural network and requires a large corpus of multiview training data which is very expensive to acquire. Therefore, we propose a method to generate this data by fitting synthetic 3D human models to real motion capture data and rendering the human poses from numerous viewpoints. While learning the CNN model, we do not use action labels but only the pose labels after clustering all training poses into k clusters. The proposed model is able to generalize to real depth images of unseen poses without the need for re-training or fine-tuning. Real depth videos are passed through the model frame-wise to extract view-invariant features. For spatio-temporal representation, we propose group sparse Fourier Temporal Pyramid which robustly encodes the action specific most discriminative output features of the proposed human pose model. Experiments on two multiview and three single-view benchmark datasets show that the proposed method dramatically outperforms existing state-of-the-art in action recognition.
Distributed Source Localization of Multi-Agent Systems With Bearing Angle Measurements

This note deals with the distributed source localization problem by considering a group of unicycle-type agents. Without the need of GPS and compass, we develop a distributed source localization scheme based on bearing angle measurements about neighbors. It is shown that if the sensing and communication graph is connected and the relative motion of every pair of neighboring agents satisfies a persistent excitation condition, then every agent is able to estimate the relative coordinate of the source asymptotically.