Miracle-dz/UniFAR-FTUs huggingface dataset:
Acromioclavicular Joint Stabilization: A Biomechanical Study of Bidirectional Stability and Strength

Background: It is important to restore horizontal and vertical stability to the acromioclavicular (AC) joint when treating dislocations of this joint. Most surgical stabilization techniques of the AC joint have primarily addressed the coracoclavicular ligament complex; however, these techniques may not satisfactorily restore horizontal stability to the AC joint. Purpose: To evaluate the strength and bidirectional stability of 3 AC joint stabilizing techniques in a cadaveric model. Study Design: Controlled laboratory study. Methods: A total of 24 cadaveric shoulders were randomly allocated to 3 treatment groups. For each group, a standardized AC joint stabilizing procedure was performed, and the specimens were potted for mechanical testing. The following reconstruction techniques were used: a single clavicular tunnel for group A, a double clavicular tunnel for group B, and a double clavicular tunnel plus suture fixation across the AC joint for group C. The specimens underwent cyclic loading in the horizontal and vertical planes and then load to failure. Eight control specimens also underwent cyclic loading in both planes. Construct stiffness during cyclic loading, change in displacement after cyclic loading in both planes, load to failure in the vertical plane, and mode of failure were evaluated, and stiffness was compared among the treatment groups as well as with a control group. Results: There was a decrease in joint stiffness for all groups, including controls, during the cyclic loading. Compared with controls, all 3 treatment groups demonstrated equivalent stiffness and displacement in the vertical plane. In the horizontal plane, all 3 treatment groups demonstrated decreased stiffness, increased displacement, or both when compared with controls. When groups were compared, no treatment arm proved superior regarding stiffness or displacement in either plane. Load-to-failure testing of the 3 treatment groups in the vertical plane demonstrated construct strength and stiffness comparable with reports for the native AC joint. The mode of failure was predominantly fracture at the point of fixation to the testing apparatus. Conclusion: There was no difference in bidirectional strength and stability between the single– and double–clavicular tunnel techniques of coracoclavicular reconstruction. The addition of a stabilizing suture across the AC joint does not improve horizontal stability in the absence of repair of the AC joint capsule and deltotrapezial fascia. Clinical Relevance: This laboratory study provides further evidence of the importance of the AC joint capsule and associated soft tissues in affording horizontal stability to that joint. Information from this and subsequent studies utilizing a bidirectional model can influence the choice of surgical procedure in the clinical treatment of AC joint dislocations.
Least-squares parameter estimation for state-space models with state equality constraints

If a dynamic system has active constraints on the state vector and they are known, then taking them into account during modeling is often advantageous. Unfortunately, in the constrained discrete-time state-space estimation, the state equality constraint is defined for a parameter matrix and not on a parameter vector as commonly found in regression problems. To address this problem, firstly, we show how to rewrite the state equality constraints as equality constraints on the state matrices to be estimated. Then, we vectorise the matricial least squares problem defined for modeling state-space systems such that any method from the equality-constrained least squares framework may be employed. Both time-invariant and time-varying cases are considered as well as the case where the state equality constraint is not exactly known.
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations

This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries. To this end, we propose IRNet, which estimates rough areas of individual instances and detects boundaries between different object classes. It thus enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately. Furthermore, IRNet is trained with inter-pixel relations on the attention maps, thus no extra supervision is required. Our method with IRNet achieves an outstanding performance on the PASCAL VOC 2012 dataset, surpassing not only previous state-of-the-art trained with the same level of supervision, but also some of previous models relying on stronger supervision.
Universal Lipschitz Approximation in Bounded Depth Neural Networks

Adversarial attacks against machine learning models are a rather hefty obstacle to our increasing reliance on these models. Due to this, provably robust (certified) machine learning models are a major topic of interest. Lipschitz continuous models present a promising approach to solving this problem. By leveraging the expressive power of a variant of neural networks which maintain low Lipschitz constants, we prove that three layer neural networks using the FullSort activation function are Universal Lipschitz function Approximators (ULAs). This both explains experimental results and paves the way for the creation of better certified models going forward. We conclude by presenting experimental results that suggest that ULAs are a not just a novelty, but a competitive approach to providing certified classifiers, using these results to motivate several potential topics of further research.
Boto (Inia geoffrensis—Cetacea: Iniidae) aggregations in two provisioning sites in the lower Negro River—Amazonas, Brazil: are they related?

The Negro River currently has seven floating houses where tourists can feed and interact with botos, each with its own history of how these aggregations were formed. Some keepers say these groups are familial, even reporting individuals being born into the group. However, behavioral studies have shown that botos are solitary, only forming groups at feeding areas and during the mating season. In the present study we used 12 microsatellite and molecular sex markers to characterize relationships within and between two boto aggregations (ten and seven botos each) in the lower Negro River. Molecular sexing revealed that all botos sampled from both aggregations were males. This may be explained by habitat preference, as male botos are primarily found in the main channels of large rivers, whereas females prefer more protected areas, such as flooded forests and its channels and lakes. Most of the animals were unrelated within each aggregation, demonstrating that these aggregations are not normally formed due to kinship bonds, but are exclusively for feeding, as botos learn that these places provide easy access to food. This study provides important information that helps us understand how human interaction is affecting the social structure and behavior of these animals.
Back to the Future: Knowledge Distillation for Human Action Anticipation

We consider the task of training a neural network to anticipate human actions in video. This task is challenging given the complexity of video data, the stochastic nature of the future, and the limited amount of annotated training data. In this paper, we propose a novel knowledge distillation framework that uses an action recognition network to supervise the training of an action anticipation network, guiding the latter to attend to the relevant information needed for correctly anticipating the future actions. This framework is possible thanks to a novel loss function to account for positional shifts of semantic concepts in a dynamic video. The knowledge distillation framework is a form of self-supervised learning, and it takes advantage of unlabeled data. Experimental results on JHMDB and EPIC-KITCHENS dataset show the effectiveness of our approach.
Trade induced technical change? The impact of Chinese imports on innovation, IT and productivity

We examine the impact of Chinese import competition on broad measures of technical change patenting, IT, R&D, TFP and management practices – using new panel data across twelve European countries from 1996-2007. In particular, we establish that the absolute volume of innovation increases within the firms most affected by Chinese imports. We correct for endogeneity using the removal of product-specific quotas following China’s entry into the World Trade Organization. Chinese import competition led to increased technical change within firms and reallocated employment between firms towards more technologically advanced firms. These within and between effects were about equal in magnitude, and account for about 15% of European technology upgrading over 2000-2007 (and even more when allowing for offshoring to China). Rising Chinese import competition also led to falls in employment, profits, prices and the share of unskilled workers. By contrast, import competition from developed countries had no effect on innovation. We develop a simple “trapped factor” model that is consistent with these empirical findings. JEL No. O33, F14, L25, L60,
Trade Induced Technical Change ? The Impact of Chinese Imports on Innovation , IT and Productivity

We examine the impact of Chinese import competition on patenting, IT, R&D and TFP using a panel of up to half a million firms over 1996-2007 across twelve European countries. We correct for endogeneity using the removal of product-specific quotas following China’s entry into the World Trade Organization. Chinese import competition had two effects: first, it led to increases in R&D, patenting, IT and TFP within firms; and second it reallocated employment between firms towards more innovative and technologically advanced firms. These within and between effects were about equal in magnitude, and appear to account for around 15% of European technology upgrading between 2000-2007. Rising Chinese import competition also led to falls in employment, profits, prices and the skill share. By contrast, import competition from developed countries had no effect on innovation. We develop a simple “trapped factor” model of innovation that is consistent with these empirical findings. JEL Classifications: O33, F14, L25, L60
Learning and Smoothing in Switching Markov Models with Copulas. (Apprentissage et Lissage dans Les Modèles de Markov à Sauts et à Copules)

Les modeles de Markov a sauts (appeles JMS pour Jump Markov System) sont utilises dans de nombreux domaines tels que la poursuite de cibles, le traitement des signaux sismiques et la finance, etant donne leur bonne capacite a modeliser des systemes non-lineaires et non-gaussiens. De nombreux travaux ont etudie les modeles de Markov lineaires pour lesquels bien souvent la restauration de donnees est realisee grâce a des methodes d’echantillonnage statistique de type Markov Chain Monte-Carlo. Dans cette these, nous avons cherche des solutions alternatives aux methodes MCMC et proposons deux originalites principales. La premiere a consiste a proposer un algorithme de restauration non supervisee d’un JMS particulier appele « modele de Markov couple a sauts conditionnellement gaussiens » (note CGPMSM). Cet algorithme combine une methode d’estimation des parametres basee sur le principe Esperance-Maximisation (EM) et une methode efficace pour lisser les donnees a partir des parametres estimes. La deuxieme originalite a consiste a etendre un CGPMSM specifique appele CGOMSM par l’introduction des copules. Ce modele, appele GCOMSM, permet de considerer des distributions plus generales que les distributions gaussiennes tout en conservant des methodes de restauration optimales et rapides. Nous avons equipe ce modele d’une methode d’estimation des parametres appelee GICE-LS, combinant le principe de la methode d’estimation conditionnelle iterative generalisee et le principe des moindre-carres lineaires. Toutes les methodes sont evaluees sur des donnees simulees. En particulier, les performances de GCOMSM sont discutees au regard de modeles de Markov non-lineaires et non-gaussiens tels que la volatilite stochastique, tres utilisee dans le domaine de la finance.
Swimming training potentiates the recovery of femoral neck strength in young diabetic rats under insulin therapy

OBJECTIVE: To test whether swimming training benefits femoral neck strength in young diabetic rats under insulin therapy. METHODS: A total of 60 male Wistar rats (age: 40 days) were divided equally into the following six groups: control sedentary, control exercise, diabetic sedentary, diabetic exercise, diabetic sedentary plus insulin and diabetic exercise plus insulin. Diabetes was induced with a unique intraperitoneal injection (60 mg/kg body weight) of streptozotocin. Seven days after the injection and after 12 hours of fasting, the animals with blood glucose levels ≥300 mg/dL were considered diabetic. Seven days after the induction of diabetes, the animals in the exercise groups were subjected to progressive swimming training (final week: 90 min/day; 5 days/week; 5% load) for eight weeks. The animals in the insulin groups received a daily dose of insulin (2-4 U/day) for the same period. RESULTS: Severe streptozotocin-induced diabetes reduced the structural properties of the femoral neck (trabecular bone volume, trabecular thickness and collagen fiber content). The femoral neck mechanical properties (maximum load and tenacity) were also impaired in the diabetic rats. Insulin therapy partially reversed the damage induced by diabetes on the structural properties of the bone and mitigated the reductions in the mechanical properties of the bone. The combination of therapies further increased the femoral neck trabecular bone volume (∼30%), trabecular thickness (∼24%), collagen type I (∼19%) and type III (∼13%) fiber contents, maximum load (∼25%) and tenacity (∼14%). CONCLUSIONS: Eight weeks of swimming training potentiates the recovery of femoral neck strength in young rats with severe streptozotocin-induced diabetes under insulin therapy.
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning

Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors. In this work, we explore the potential of \sos in the field of descriptor learning by building upon the intuition that a positive pair of matching points should exhibit similar distances with respect to other points in the embedding space. Thus, we propose a novel regularization term, named Second Order Similarity Regularization (SOSR), that follows this principle. By incorporating SOSR into training, our learned descriptor achieves state-of-the-art performance on several challenging benchmarks containing distinct tasks ranging from local patch retrieval to structure from motion. Furthermore, by designing a von Mises-Fischer distribution based evaluation method, we link the utilization of the descriptor space to the matching performance, thus demonstrating the effectiveness of our proposed SOSR. Extensive experimental results, empirical evidence， and in-depth analysis are provided, indicating that SOSR can significantly boost the matching performance of the learned descriptor.
Recent EFFECTS OF FEAR AND ANGER ON PERCEIVED RISKS OF TERRORISM A National Field Experiment

Copyright © 2002 American Psychological Society VOL. 14, NO. 2, MARCH 2003 1010 Vermont Avenue, NW, Suite 1100 Washington, DC 20005-4907 (202) 783-2077; (202) 783-2083 (fax); aps@aps.washington. dc.us www.psychologicalscience. org Address correspondence to Jennifer Lerner, Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213; e-mail: jlerner@andrew.cmu. edu. Terrorist attacks on the United States intensely affected many individuals and institutions, well beyond those directly harmed. Financial markets dropped, consumer spending declined, air travel plummeted, and public opinion toward government shifted. These responses reflected intense thought– and emotion. The attacks–and prospect of sustained conflict with a diffuse, unfamiliar enemy–created anger, fear, and sadness. A growing literature considers the interplay of emotions and risk perceptions (see Holtgrave & Weber, 1993; Loewenstein & Lerner, in press; Loewenstein, Weber, Hsee, & Welch, 2001; Mellers, Schwartz, & Ritov, 1999; Schwarz & Clore, 1996; Slovic, Finucane, Peters, & MacGregor, in press). Its theories can both illuminate current events and be tested by them. Early research found that positive emotions trigger more optimistic risk assessments and negative emotions trigger more pessimistic ones, even if the source of the emotion has no relation to the target risks (Johnson & Tversky, 1983). Recent EFFECTS OF FEAR AND ANGER ON PERCEIVED RISKS OF TERRORISM A National Field Experiment
Continuing vocational training and innovation in Europe

This paper hypothesises that continuing vocational training (CVT) should have a positive effect on the innovativeness of nations. Using correlation analysis and several variables from the continuing vocational training survey (CVTS), which depicts various aspects of CVT, the hypothesis of a positive connection between CVT and innovation, as measured in patents, is confirmed in the European context: those nations where the commitment to CVT is the highest are also the most innovative countries in Europe. Consequently, investments and commitment to CVT, when properly planned and executed, have a positive effect on the innovativeness of firms and, subsequently, nations.
Infiltrating CD4+ T cells attenuate chemotherapy sensitivity in prostate cancer via CCL5 signaling

Chemotherapy with Docetaxel (Doc) is efficient in a subset of prostate cancer (PCa) cases; however, most patients ultimately develop resistance to Docetaxel. The tumor immune microenvironment and secreted cytokines play a substantial role in development of resistance to chemotherapy. Our previous study has demonstrated that CD4+ T cells in prostate tumor microenvironment contribute to PCa progression; meanwhile, we found increased CD4+ T‐cell infiltration in tumor area after Doc treatment; however, their effects on PCa chemosensitivity remain unclear. Here, we aim to explore the role and mechanisms of CD4+ T cells in PCa chemotherapy sensitivity.
Design and Validation of Transcription-Mediated-Amplification Nucleic Acid Amplification Tests for Mycoplasma genitalium

Mycoplasma genitalium is a sexually transmitted bacterium linked to adverse sexual and reproductive health outcomes in women and men. M. genitalium is difficult to culture, and in the absence of validated amplified molecular methods for diagnosis of infection, there is no reference standard available for use as a comparator for the validation of new M. genitalium diagnostic tests. ABSTRACT Mycoplasma genitalium is a sexually transmitted bacterium linked to adverse sexual and reproductive health outcomes in women and men. M. genitalium is difficult to culture, and in the absence of validated amplified molecular methods for diagnosis of infection, there is no reference standard available for use as a comparator for the validation of new M. genitalium diagnostic tests. We evaluated the analytical and clinical performance of three transcription-mediated amplification (TMA) tests for M. genitalium, each targeting unique rRNA sequences, for use as a composite comparator for clinical validation of the Aptima Mycoplasma genitalium (AMG) assay, an in vitro diagnostic (IVD) TMA test that targets 16 s rRNA of M. genitalium. Analytical sensitivity, specificity, and strain inclusivity of all four TMA tests were determined using nine laboratory strains of M. genitalium and 56 nontarget bacteria, protozoa, and viruses. Analytical sensitivity of the tests for M. genitalium ranged from 0.017 to 0.040 genome equivalents/ml. None of the nontarget organisms evaluated cross-reacted with any test. A composite comparator reference standard consisting of the 3 alternate (Alt) TMA tests was used to evaluate the clinical performance of the AMG assay by testing residual vaginal swab, female urine, and male urine specimens obtained from 1,400 adult subjects from three U.S. clinical sites. Using this reference standard to establish infected specimen status, the sensitivity, specificity, and overall agreement of the AMG IVD assay were 100%, 99.9%, and 99.9%, respectively. These results demonstrate the utility of molecular composite reference standard methodology for the clinical validation of future IVD tests for this organism.
Demonstration and Understanding of Nano-RAM Novel One-Time Programmable Memory Application

In prior researches, carbon nanotube (CNT)-based nano-random access-memory (NRAM) uses reset initialization which obtains >1011 high write endurance for large-time programmable (LTP) application. In this paper, for the first time, NRAM one-time programmable (OTP) application is proposed by using set initialization for data archive. Specifically, virgin NRAM cells are all in high resistance state (HRS) for storing bit “0. ” In contrast, set initialization uses reversed polarity voltage to obtain low resistance state (LRS) for bit “1. ” Furthermore, physical models of set initialization and retention current degradation are proposed for the first time. The current increment during set initialization can be attributed to electron tunneling and CNT deformation. Retention current degradation may be due to variation of paralleled CNT contacts in bottleneck zone. As for OTP performance, median NRAM bit and 1% tail bit demonstrate more than 1 billion years and 15 years data retentions, respectively, on 150 °C. The tail bit activation energy is 2.41 eV. Finally, no LRS read disturb is found after 10 s 0.5-V stress on both polarities. The virgin NRAM cells in HRS should be more stable than set initialized NRAM cells in LRS for both retention time and read disturb.