Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoothed Analysis of the Expected Number of Maximal Points in Two Dimensions

The {\em Maximal} points in a set S are those that aren't {\em dominated} by any other point in S. Such points arise in multiple application settings in which they are called by a variety of different names, e.g., maxima, Pareto optimums, skylines. Because of their ubiquity, there is a large literature on the {\em expected} number of maxima in a set S of n points chosen IID from some distribution. Most such results assume that the underlying distribution is uniform over some spatial region and strongly use this uniformity in their analysis. This work was initially motivated by the question of how this expected number changes if the input distribution is perturbed by random noise. More specifically, let Ballp denote the uniform distribution from the 2-d unit Lp ball, delta Ballq denote the 2-d Lq-ball, of radius delta and Ballpq be the convolution of the two distributions, i.e., a point v in Ballp is reported with an error chosen from delta Ballq. The question is how the expected number of maxima change as a function of delta. Although the original motivation is for small delta the problem is well defined for any delta and our analysis treats the general case. More specifically, we study, as a function of n,\delta, the expected number of maximal points when the n points in S are chosen IID from distributions of the type Ballpq where p,q in {1,2,infty} for delta > 0 and also of the type Ballp infty-q, where q in [1,infty) for delta > 0.
Fully Distributed Event-Triggered Protocols for Linear Multiagent Networks

This paper considers the distributed event-triggered consensus problem for general linear multiagent networks. Both the leaderless and leader–follower consensus problems are considered. Based on the local sampled state information, distributed adaptive event-triggered protocols are designed, which can ensure that the consensus of the agents is achieved, and the Zeno behavior is excluded by showing that the interval between any two triggering events is lower-bounded by a strictly positive value. Compared with the previous related works, our main contribution is that the proposed adaptive event-based protocols are fully distributed and scalable, which do not rely on any global information of the network graph and are independent of the network's scale. In these event-based protocols, continuous communications are not required for either control laws updation or triggering functions monitoring.
The high-level basis of body adaptation

Prolonged visual exposure, or ‘adaptation’, to thin (wide) bodies causes a perceptual aftereffect such that subsequently seen bodies appear wider (thinner) than they actually are. Here, we conducted two experiments investigating the effect of rotating the orientation of the test stimuli by 90° from that of the adaptor. Aftereffects were maximal when adapting and test bodies had the same orientation. When they differed, the axis of the perceived distortion changed with the orientation of the body. Experiment 1 demonstrated a 58% transfer of the aftereffect across orientations. Experiment 2 demonstrated an even greater degree of aftereffect transfer when the influence of low-level mechanisms was reduced further by using adaptation and test stimuli with different sizes. These results indicate that the body aftereffect is mediated primarily by high-level object-based processes, with low-level retinotopic mechanisms playing only a minor role. The influence of these low-level processes is further reduced when test stimuli differ in size from adaptation stimuli.
A Collective Variational Autoencoder for Top-N Recommendation with Side Information

Recommender systems have been studied extensively due to their practical use in real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information has been widely utilized to address rating sparsity Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional, existing deep models tend to have large input dimensionality, which dominates their overall size. This makes them difficult to train, especially with insufficient inputs. Rather than learning item representations, in this paper, we propose to learn feature representations through deep learning from side information. Learning feature representations ensures a sufficient number of inputs to train a deep network. To achieve this, we propose to simultaneously recover user ratings and side information, by using a Variational Autoencoder (VAE). Specifically, user ratings and side information are encoded and decoded collectively through the same inference network and generation network. This is possible as both user ratings and side information are associated with items. To account for the heterogeneity of user ratings and side information, the final layer of the generation network follows different distributions. The proposed model is easy to implement and efficient to optimize and is shown to outperform state-of-the-art top-N recommendation methods that use side information.
Analysis of the regulation networks in grapevine reveals response to waterlogging stress and candidate gene-marker selection for damage severity

Owing to the climate change impacts, waterlogging is one of the most hazardous abiotic stresses to crops, which also can result in a serious reduction in the quantity and quality of grape berry and wine production during the rainy season. Therefore, the exploration of the response mechanism of grape to waterlogging is necessary, for which the analysis of the transcriptomic regulation networks of grapevine leaves in response to waterlogging stress was carried out. In this study, 12 634 genes were detected in both waterlogging stress and control grapevine plants, out of which 6837 genes were differentially expressed. A comparative analysis revealed that genes functioning in the antioxidant system, glycolysis and fermentation pathway, chlorophyll metabolism, amino acid metabolism and hormones were activated to reduce injury to grapes under the waterlogging stress. Meanwhile, genes encoding class-2 non-symbiotic haemoglobin were determined as important in waterlogging acclimation. Additionally, the expression variations of three marker genes were found to be informative and can be used to predict the viability of the grapevines subjected to waterlogging. This research not only probes the molecular mechanism underlying grapevine waterlogging tolerance but also puts forward an idea about the application of gene expression information to practical management.
Fiber Supplements Derived From Sugarcane Stem, Wheat Dextrin and Psyllium Husk Have Different In Vitro Effects on the Human Gut Microbiota

There is growing public interest in the use of fiber supplements as a way of increasing dietary fiber intake and potentially improving the gut microbiota composition and digestive health. However, currently there is limited research into the effects of commercially available fiber supplements on the gut microbiota. Here we used an in vitro human digestive and gut microbiota model system to investigate the effect of three commercial fiber products; NutriKane™, Benefiber® and Psyllium husk (Macro) on the adult gut microbiota. The 16S rRNA gene amplicon sequencing results showed dramatic fiber-dependent changes in the gut microbiota structure and composition. Specific bacterial OTUs within the families Bacteroidaceae, Porphyromonadaceae, Ruminococcaceae, Lachnospiraceae, and Bifidobacteriaceae showed an increase in the relative abundances in the presence of one or more fiber product(s), while Enterobacteriaceae and Pseudomonadaceae showed a reduction in the relative abundances upon addition of all fiber treatments compared to the no added fiber control. Fiber-specific increases in SCFA concentrations showed correlation with the relative abundance of potential SCFA-producing gut bacteria. The chemical composition, antioxidant potential and polyphenolic content profiles of each fiber product were determined and found to be highly variable. Observed product-specific variations could be linked to differences in the chemical composition of the fiber products. The general nature of the fiber-dependent impact was relatively consistent across the individuals, which may demonstrate the potential of the products to alter the gut microbiota in a similar, and predictable direction, despite variability in the starting composition of the individual gut microbiota.
A meta-analysis and systematic review evaluating the use of erythropoietin in total hip and knee arthroplasty

Purpose The debate is still ongoing on the effectiveness and safety of erythropoietin (EPO) treatment in orthopedic surgeries. Specifically, previous studies have not compared the dynamic change of hemoglobin (Hb) levels between different transfusion methods. Besides, complications or side effects of this alternative have not been quantitatively analyzed. We conducted a meta-analysis and systemic review to evaluate the efficacy of EPO on Hb levels observed during the whole perioperative period as well as the volume of allogeneic blood transfusion (ABT), the risk of venous thromboembolism, and application frequency of ABT in hip and knee surgery. Materials and methods PubMed, Embase, Web of Science, and the Cochrane library were systematically searched from inception to November 2017. The data from randomized controlled trials were extracted and the risk of bias assessed using Cochrane’s Collaboration’s tool. Results Twenty-five randomized controlled trials involving 4,159 patients were included in this meta-analysis. EPO could reduce exposure to allogeneic blood transfused (odds ratio [OR] =0.42, P=0.001) and reduce the average volume of allogeneic blood transfused (OR = −0.28, P=0.002). When EPO and preoperative autologous blood donation (PABD) were compared, the use of EPO was associated with lower exposure to ABT (OR =0.48, P=0.03), but no significant decrease in the average volume of allogeneic blood transfused (OR = −0.23, P=0.32). The use of EPO was associated with a higher level of Hb with or without use of PABD at all the 4 time points (preoperation, 24–48 hours postoperation, 3–5 days postoperation, discharge of last observation) (P<0.0001), which means EPO could increase the level of Hb significantly during the perioperative period. The results also indicated EPO does not increase the risk of a venous thromboembolism event. Conclusion Preoperative administration of EPO was shown to generally increase Hb levels during the whole perioperative period; however, the extent of the positive effects varies with time points. Additionally, EPO minimizes the need for transfusion significantly in patients undergoing hip or knee surgery without increasing the chance of developing thrombotic complications. Therefore, EPO offers an alternative blood management strategy in total hip arthroplasty and total knee arthroplasty.
The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects

Aim: To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA). Methods: 50 healthy subjects (mean age 45.8 ± 18.5 years, range 21–85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample t-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA. Results: Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (R2 = 0.588; P < 0.001) and discriminated between the older and younger subgroups (P < 0.001). Conclusion: A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI.
Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs

This paper addresses the semantics of weighted argumentation graphs that are bipolar, i.e. contain both attacks and supports for arguments. It builds on previous work by Amgoud, Ben-Naim et. al. We study the various characteristics of acceptability semantics that have been introduced in these works, and introduce the notion of a modular acceptability semantics. A semantics is modular if it cleanly separates aggregation of attacking and supporting arguments (for a given argument $a$) from the computation of their influence on $a$'s initial weight. We show that the various semantics for bipolar argumentation graphs from the literature may be analysed as a composition of an aggregation function with an influence function. Based on this modular framework, we prove general convergence and divergence theorems. We demonstrate that all well-behaved modular acceptability semantics converge for all acyclic graphs and that no sum-based semantics can converge for all graphs. In particular, we show divergence of Euler-based semantics (Amgoud et al.) for certain cyclic graphs. Further, we provide the first semantics for bipolar weighted graphs that converges for all graphs.
Prevalence of celiac disease serological markers in a cohort of Italian rheumatological patients

Aim: To assess the prevalence of celiac disease (CD) serological markers in a cohort of patients referred to an Italian rheumatological outpatient clinic. Background: Current guidelines do not suggest CD screening in patients with rheumatological diseases and these subjects are not considered to be at high risk for CD. Methods: A total of 230 sera of rheumatological patients referred to the Division of Internal Medicine at the Department of Medical and Surgical Sciences between January 2005 and December 2013 were screened for CD by testing IgA antitransglutaminase (TTG IgA), IgG deamidated gliadin peptides (DGP IgG) and IgA antiendomysium (EMA) antibodies. Of the 230 patients tested, 67 had a diagnosis of rheumatoid arthritis (RA), 52 Sjögren’s syndrome (SjS), 42 systemic sclerosis (SCL), 35 systemic lupus erythematosus (SLE), 15 mixed connective tissue disease, 11 polymyositis and 10 dermatomyositis. Results: TTG IgA antibodies were identified in 7/230 cases (3%), 3 in SjS (3/42 – 5.8%), 2 in SCL (2/42 – 4.8%), 1 in RA (1/67 – 1.5%) and 1 in SLE sera (1/35 – 2.8%). All the seven sera were also positive for DGP IgG and EMA IgA. DGP IgG were the most frequent antibody detected, being found in 16 (7%) sera. Conclusion: This study identified a high prevalence of CD antibodies in adult patients referred to a rheumatology outpatient clinic. These results highlight the importance of CD screening in subjects presenting with rheumatological features.
Waptia fieldensis Walcott, a mandibulate arthropod from the middle Cambrian Burgess Shale

Waptia fieldensis Walcott, 1912 is one of the iconic animals from the middle Cambrian Burgess Shale biota that had lacked a formal description since its discovery at the beginning of the twentieth century. This study, based on over 1800 specimens, finds that W. fieldensis shares general characteristics with pancrustaceans, as previous authors had suggested based mostly on its overall aspect. The cephalothorax is covered by a flexible, bivalved carapace and houses a pair of long multisegmented antennules, palp-bearing mandibles, maxillules, and four pairs of appendages with five-segmented endopods—the anterior three pairs with long and robust enditic basipods, the fourth pair with proximal annulations and lamellae. The post-cephalothorax has six pairs of lamellate and fully annulated appendages which appear to be extensively modified basipods rather than exopods. The front part of the body bears a pair of stalked eyes with the first ommatidia preserved in a Burgess Shale arthropod, and a median ‘labral’ complex flanked by lobate projections with possible affinities to hemi-ellipsoid bodies. Waptia confirms the mandibulate affinity of hymenocarines, retrieved here as part of an expanded Pancrustacea, thereby providing a novel perspective on the evolutionary history of this hyperdiverse group. We construe that Waptia was an active swimming predator of soft prey items, using its anterior appendages for food capture and manipulation, and also potentially for clinging to epibenthic substrates.
Machine Learning with Membership Privacy using Adversarial Regularization

Machine learning models leak significant amount of information about their training sets, through their predictions. This is a serious privacy concern for the users of machine learning as a service. To address this concern, in this paper, we focus on mitigating the risks of black-box inference attacks against machine learning models. We introduce a mechanism to train models with membership privacy, which ensures indistinguishability between the predictions of a model on its training data and other data points (from the same distribution). This requires minimizing the accuracy of the best black-box membership inference attack against the model. We formalize this as a min-max game, and design an adversarial training algorithm that minimizes the prediction loss of the model as well as the maximum gain of the inference attacks. This strategy, which can guarantee membership privacy (as prediction indistinguishability), acts also as a strong regularizer and helps generalizing the model. We evaluate the practical feasibility of our privacy mechanism on training deep neural networks using benchmark datasets. We show that the min-max strategy can mitigate the risks of membership inference attacks (near random guess), and can achieve this with a negligible drop in the model's prediction accuracy (less than 4%).
Computationally Efficient Approaches for Image Style Transfer

In this work, our focus is on developing fast image style transfer architectures for practical applications. We have proposed three modifications to the architecture of a recent, real-time, artistic style transfer technique to make it computationally more efficient. We have proposed the use of depth-wise separable convolution (DepSep) in place of convolution and nearest neighbor (NN) interpolation in place of transposed convolution. We have also explored the concatenation of nearest neighbour and bilinear (Bil) interpolations in place of transposed convolution. The stylized images from the modified architectures are perceptually similar in quality to those from the original architecture. The decrease in the computational complexity of our architectures is validated by the decrease in the testing time by 26.1%, 39.1%, and 57.1%, respectively, for DepSep, DepSep-NN-Bil and DepSep-NN modifications. Working with another architecture, we have examined how the quality of the stylized reconstruction changes with the change of the loss function to be minimized.
A Low-Cost and High-Gain 60-GHz Differential Phased Array Antenna in PCB Process

A broadband, low-cost, high-gain, multilayer differential antenna without air cavity is proposed for the 60-GHz phased array system. By using the standard low-cost printed circuit board process, a 60-GHz phased array antenna element and a 16-element phased array antenna prototype are implemented. To improve the bandwidth and antenna gain, a superstrate and a float rectangular ring are designed. The float ring can enhance the antenna vertical radiation, suppressing the antenna horizontal radiation and reducing the mutual coupling. Without any embedded air cavity, the proposed antenna is robust against the process variation, which is verified in the measurement. For the antenna element, the measured results demonstrate a peak gain of 8.9 dBi and a 10-dB return-loss bandwidth of more than 12 GHz, and the simulated radiation efficiency is above 90% at 60-GHz band. For the phased array antenna, two beam-fixed (0° and 30°) array antennas are tested to verify the beam scanning performance, which achieve gains of 16.3 and 13.6 dBi, respectively.
Exploring Efficient NFA Data Structures to Accelerate DFA Generation

Deterministic finite automata (DFA) is widely employed in regular expression matching for content-aware applications, such as protocol identification, NIDS, load balancing, traffic billing, etc. Subset construction is the most time-consuming process when converting regular expressions to the corresponding DFA, which brings a great challenge for fast update application scenarios. Subset construction mainly consists of subset computation and subset comparison. The subset comparison is efficiently processed with the trie tree structure, but the subset computation still remains inefficient for the massive and repetitive memory accesses of NFA state transitions. Through the theoretical analysis and the example demonstration, we prove that the inefficiency mainly originates from the traditional list structures for the NFA state transitions. Then, we proposed two improved data structures for the NFA state transitions, namely the ordered list structure and the ordered array structure. These structures leverage the alphabetical order of the transitions to avoid the huge irrelevant memory accesses during subset computation. Experiments on practical rule sets demonstrate that the subset computation procedure achieves a speedup of 6x to 8x and the total DFA generation achieves a speedup of 2x to 4x.
Leveraging Pre-Trained 3D Object Detection Models for Fast Ground Truth Generation

Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. This paper introduces a novel ground truth generation method that combines human supervision with pre-trained neural networks to generate per-instance 3D point cloud segmentation, 3D bounding boxes, and class annotations. The annotators provide object anchor clicks which behave as a seed to generate instance segmentation results in 3D. The points belonging to each instance are then used to regress object centroids, bounding box dimensions, and object orientation. Our proposed annotation scheme requires 30x lower human annotation time. We use the KITTI 3D object detection dataset [1] to evaluate the efficiency and the quality of our annotation scheme. We also test the the proposed scheme on previously unseen data from the Autonomoose self-driving vehicle to demonstrate generalization capabilities of the network.