Miracle-dz/UniFAR-FTUs huggingface dataset:
Extremal K\"ahler metrics on blowups

Consider a compact Kähler manifold which either admits an extremal Kähler metric, or is a small deformation of such a manifold. We show that the blowup of the manifold at a point admits an extremal Kähler metric in Kähler classes making the exceptional divisor sufficiently small if and only if it is relatively K-stable, thus proving a special case of the Yau-Tian-Donaldson conjecture. We also give a geometric interpretation of what relative K-stability means in this case in terms of finite dimensional geometric invariant theory. This gives a complete solution to a problem addressed in special cases by Arezzo, Pacard, Singer and Székelyhidi. In addition, the case of a deformation of an extremal manifold proves the first non-trivial case of a general conjecture of Donaldson.
Study on precoding optimization algorithms in massive MIMO system with multi-antenna users

The paper studies the multi-user precoding problem as a non-convex optimization problem for wireless multiple input and multiple output (MIMO) systems. In our work, we approximate the target Spectral Eﬃciency function with a novel computationally simpler function. Then, we reduce the precoding problem to an un- constrained optimization task using a special diﬀerential projection method and solve it by the Quasi-Newton L-BFGS iterative procedure to achieve gains in ca- pacity. We are testing the proposed approach in several scenarios generated using Quadriga — open-source software for generating realistic radio channel impulse re- sponse. Our method shows monotonic improvement over heuristic methods with reasonable computation time. The proposed L-BFGS optimization scheme is novel in this area and shows a signiﬁcant advantage over the standard approaches. The proposed method has a simple implementation and can be a good reference for other heuristic algorithms in this ﬁeld.
Gradient representations in ReLU networks as similarity functions

Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap.
Simultaneous Neural Machine Translation with Constituent Label Prediction

Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process. However, deciding whether to read more input words or start to translate is difficult for language pairs with different word orders such as English and Japanese. Motivated by the concept of pre-reordering, we propose a couple of simple decision rules using the label of the next constituent predicted by incremental constituent label prediction. In experiments on English-to-Japanese simultaneous translation, the proposed method outperformed baselines in the quality-latency trade-off.
Positional stability of skyrmions in a racetrack memory with notched geometry

Magnetic skyrmions are chiral spin textures with attractive features, such as ultra-small size, solitonic nature, and easy mobility with small electrical currents that make them promising as information-carrying bits in low-power high-density memory, and logic applications. However, it is essential to guarantee the positional stability of skyrmions for reliable information extraction. Using micromagnetic simulations for the minimum energy path (MEP), we compute the energy barriers associated with stabilizing notches along a racetrack. We vary material parameters, specifically, the strength of the chiral Dzyaloshinskii-Moriya interactions (DMI), the notch geometry, and the thickness of the racetrack to get the optimal barrier height. We find that the reduction of skyrmion size as it squeezes past the notch gives rise to the energy barrier. We find a range of energy barriers up to ~ 45 kBT for a racetrack of 5 nm thickness that can provide years long positional lifetime of skyrmions for long-term memory applications while requiring a moderate amount of current (~ 10^10 A/m2) to move the skyrmions. Furthermore, we derive quasi-analytical equations to estimate the energy barrier. We also explore other pinning mechanisms, such as a local variation of material parameters in a region, and find that notched geometry provides the highest energy barrier. Our results open up possibilities to design practical skyrmion-based racetrack geometries for spintronics applications.
Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO

Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surrogate objective (PPO-clip), which has been popularly used in deep reinforcement learning due to its simplicity and effectiveness. Despite its superior empirical performance, PPO-clip has not been justiﬁed via theoretical proof up to date. This paper proposes to rethink policy optimization and reinterpret the theory of PPO-clip based on hinge policy optimization (HPO), called to improve policy by hinge loss in this paper. Speciﬁcally, we ﬁrst identify sufﬁcient conditions of state-wise policy improvement and then rethink policy update as solving a large-margin classiﬁcation problem with hinge loss. By leveraging various types of classiﬁers, the proposed design opens up a whole new family of policy-based algorithms, including the PPO-clip as a special case. Based on this construct, we prove that these algorithms asymptotically attain a globally optimal policy. To our knowledge, this is the ﬁrst ever that can prove global convergence to an optimal policy for a variant of PPO-clip. We corroborate the performance of a variety of HPO algorithms through experiments and an ablation study.
Min-similarity association rules for identifying past comorbidities of recurrent ED and inpatient patients

—In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource usage. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing reoccurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. To address this, we developed a computationally efﬁcient and interpretable framework that both identiﬁes recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Speciﬁcally, we present a novel algorithm, called the minimum similarity association rules (MSAR), balancing conﬁdence-support trade-off, to determine the conditions most associated with reoccurring Emergency department (ED) and inpatient visits. We validate MSAR on a large Electric Health Record (EHR) dataset. Part of the solution is deployed in Philips product Patient Flow Capacity Suite (PFCS).
Homography-based Visual Servoing with Remote Center of Motion for Semi-autonomous Robotic Endoscope Manipulation

The dominant visual servoing approaches in Minimally Invasive Surgery (MIS) follow single points or adapt the endoscope’s field of view based on the surgical tools’ distance. These methods rely on point positions with respect to the camera frame to infer a control policy. Deviating from the dominant methods, we formulate a robotic controller that allows for image-based visual servoing that requires neither explicit tool and camera positions nor any explicit image depth information. The proposed method relies on homography-based image registration, which changes the automation paradigm from point-centric towards surgical-scene-centric approach. It simultaneously respects a programmable Remote Center of Motion (RCM). Our approach allows a surgeon to build a graph of desired views, from which, once built, views can be manually selected and automatically servoed to irrespective of robot-patient frame transformation changes. We evaluate our method on an abdominal phantom and provide an open source ROS Moveit integration for use with any serial manipulator 3. A video is provided 4.
Graph-Based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using community-detection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computing-based service placement scheme.
Integrative Clustering of Multi-View Data by Nonnegative Matrix Factorization

Learning multi-view data is an emerging problem in machine learning research, and nonnegative matrix factorization (NMF) is a popular dimensionality-reduction method for integrating information from multiple views. These views often provide not only consensus but also diverse information. However, most multi-view NMF algorithms assign equal weight to each view or tune the weight via line search empirically, which can be computationally expensive or infeasible without any prior knowledge of the views. In this paper, we propose a weighted multi-view NMF (WM-NMF) algorithm. In particular,we aim to address the critical technical gap, which is to learn both view-speciﬁc and observation-speciﬁc weights to quantify each view’s information content. The introduced weighting scheme can alleviate unnecessary views’ adverse eﬀects and enlarge the positive eﬀects of the important views by assigning smaller and larger weights, respectively. In addition, we provide theoretical investigations about the convergence, perturbation analysis, and generalization error of the WM-NMF algorithm. Experimental results conﬁrm the eﬀectiveness and advantages of the proposed algorithm in terms of achieving better clustering performance and dealing with the corrupted data compared to the existing algorithms.
Precise URL Phishing Detection Using Neural Networks

With the development of the Internet, ways of obtaining important data such as passwords and logins or sensitive personal data have increased. One of the ways to extract such information is page impersonation, also called phishing. Such websites do not provide service but collect sensitive details from the user. Here, we present you with ways to detect such malicious URLs with state of art accuracy with neural networks. Different from previous works, where web content, URL or trafﬁc statistics are examined, we analyse only the URL text, making it faster and which detects zero-day attacks. The network is optimised and can be used even on small devices such as Raspberry-Pi without a change in performance.
Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising

The purpose of Inventory Pricing is to bid the right prices to online ad opportunities, which is crucial for a Demand-Side Platform (DSP) to win advertising auctions in Real-Time Bidding (RTB). In the planning stage, advertisers need the forecast of probabilistic models to make bidding decisions. However, most of the previous works made strong assumptions on the distribution form of the winning price, which reduced their accuracy and weakened their ability to make generalizations. Though some works recently tried to fit the distribution directly, their complex structure lacked efficiency on online inference, which is critical for advertising systems. In this paper, we devise a novel loss function, Neighborhood Likelihood Loss (NLL), collaborating with a proposed framework, Arbitrary Distribution Modeling (ADM), to predict the winning price distribution under censorship with no pre-assumption required. We conducted experiments on two real-world experimental datasets and one large-scale, non-simulated production dataset in our system. Experiments showed that ADM outperformed the baselines both on algorithm and business metrics. This method has been released for one year and led to good yield in our system. Without any pre-assumed specific distribution form, ADM showed significant advantages in effectiveness and efficiency, demonstrating its great capability in modeling sophisticated price landscapes.
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals

Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computation with complex probability distributions. However the performance of such methods is critically dependent on properly tuned parameters, most of which are difficult if not impossible to know a priori for a given target distribution. Adaptive MCMC methods aim to address this by allowing the parameters to be updated during sampling based on previous samples from the chain at the expense of requiring a new theoretical analysis to ensure convergence. In this work we extend the convergence theory of adaptive MCMC methods to a new class of methods built on a powerful class of parametric density estimators known as normalizing flows. In particular, we consider an independent Metropolis-Hastings sampler where the proposal distribution is represented by a normalizing flow whose parameters are updated using stochastic gradient descent. We explore the practical performance of this procedure on both synthetic settings and in the analysis of a physical field system, and compare it against both adaptive and non-adaptive MCMC methods.
Multi-Task Meta-Learning Modification with Stochastic Approximation

A BSTRACT Meta-learning methods aim to build learning algorithms capable of quickly adapting to new tasks in low-data regime. One of the main benchmarks of such an algorithms is a few-shot learning problem. In this paper we investigate the modiﬁcation of standard meta-learning pipeline that takes a multi-task approach during training. The proposed method simultaneously utilizes information from several meta-training tasks in a common loss function. The impact of each of these tasks in the loss function is controlled by the corresponding weight. Proper optimization of these weights can have a big inﬂuence on training of the entire model and might improve the quality on test time tasks. In this work we propose and investigate the use of methods from the family of simultaneous perturbation stochastic approximation (SPSA) approaches for meta-train tasks weights optimization. We have also compared the proposed algorithms with gradient-based methods and found that stochastic approximation demonstrates the largest quality boost in test time. Proposed multi-task modiﬁcation can be applied to almost all methods that use meta-learning pipeline. In this paper we study applications of this modiﬁcation on Prototypical Networks and Model-Agnostic Meta-Learning algorithms on CIFAR-FS, FC100, tieredImageNet and miniImageNet few-shot learning benchmarks. During these experiments, multi-task modiﬁcation has demonstrated improvement over original methods. The proposed SPSA-Tracking algorithm shows the largest accuracy boost that is competitive against the state-of-the-art meta-learning methods. Our code is available online 1 .
Prediction-focused Mixture Models

In several applications, besides getting a generative model of the data, we also want the model to be useful for speciﬁc downstream tasks. Mixture models are useful for identifying discrete components in the data, but may not identify components useful for downstream tasks if misspeci-ﬁed; further, current inference techniques often fail to overcome misspeciﬁcation even when a supervisory signal is provided. We introduce the prediction-focused mixture model, which selects and models input features relevant to predicting the targets. We demonstrate that our approach identiﬁes relevant signal from inputs even when the model is highly misspeciﬁed.
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition, which is the first work that the RERC model has been proposed and applied in solving the multi-hop QA challenges. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the most advanced performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.
Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets

Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network

Constructing large-scaled medical knowledge graphs (MKGs) can significantly boost healthcare applications for medical surveillance, bring much attention from recent research. An essential step in constructing large-scale MKG is extracting information from medical reports. Recently, information extraction techniques have been proposed and show promising performance in biomedical information extraction. However, these methods only consider limited types of entity and relation due to the noisy biomedical text data with complex entity correlations. Thus, they fail to provide enough information for constructing MKGs and restrict the downstream applications. To address this issue, we propose Biomedical Information Extraction (BioIE), a hybrid neural network to extract relations from biomedical text and unstructured medical reports. Our model utilizes a multi-head attention enhanced graph convolutional network (GCN) to capture the complex relations and context information while resisting the noise from the data. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation (CDR) and chemical-protein interaction (CPI), and a cross-hospital pan-cancer pathology report corpus. The results show that our method achieves superior performance than baselines. Furthermore, we evaluate the applicability of our method under a transfer learning setting and show that BioIE achieves promising performance in processing medical text from different formats and writing styles.
DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.