Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly Labelled AudioSet Tagging With Attention Neural Networks

Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognizing a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, whereas the onset and offset times are unknown. To address the weakly labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning (MIL) methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths, and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision of 0.369, outperforming the best MIL method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet-embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class.
Spatial-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data

Prognostic tumor growth modeling via medical imaging observations is a challenging yet important problem in precision and predictive medicine. Traditionally, this problem is tackled through mathematical modeling and evaluated using relatively small patient datasets. Recent advances of convolutional networks (ConvNets) have demonstrated their higher accuracy than mathematical models in predicting future tumor volumes. This indicates that deep learning may have great potentials on addressing such problem. The state-of-the-art work models the cell invasion and mass-effect of tumor growth by training separate ConvNets on 2D image patches. Nevertheless such a 2D modeling approach cannot make full use of the spatial-temporal imaging context of the tumor's longitudinal 4D (3D + time) patient data. Moreover, previous methods are incapable to predict clinically-relevant tumor properties, other than the tumor volumes. In this paper, we exploit to formulate the tumor growth process through convolutional LSTMs (ConvLSTM) that extract tumor's static imaging appearances and simultaneously capture its temporal dynamic changes within a single network. We extend ConvLSTM into the spatial-temporal domain (ST-ConvLSTM) by jointly learning the inter-slice 3D contexts and the longitudinal dynamics. Our approach can incorporate other non-imaging patient information in an end-to-end trainable manner. Experiments are conducted on the largest 4D longitudinal tumor dataset of 33 patients to date. Results validate that the proposed ST-ConvLSTM model produces a Dice score of 83.2%+-5.1% and a RVD of 11.2%+-10.8%, both statistically significantly outperforming (p<0.05) other compared methods of traditional linear model, ConvLSTM, and generative adversarial network (GAN) under the metric of predicting future tumor volumes. Last, our new method enables the prediction of both cell density and CT intensity numbers.
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks

Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories: exact verifiers that run in exponential time and relaxed verifiers that are efficient but incomplete. In this paper, we unify all existing LP-relaxed verifiers, to the best of our knowledge, under a general convex relaxation framework. This framework works for neural networks with diverse architectures and nonlinearities and covers both primal and dual views of robustness verification. We further prove strong duality between the primal and dual problems under very mild conditions. Next, we perform large-scale experiments, amounting to more than 22 CPU-years, to obtain exact solution to the convex-relaxed problem that is optimal within our framework for ReLU networks. We find the exact solution does not significantly improve upon the gap between PGD and existing relaxed verifiers for various networks trained normally or robustly on MNIST and CIFAR datasets. Our results suggest there is an inherent barrier to tight verification for the large class of methods captured by our framework. We discuss possible causes of this barrier and potential future directions for bypassing it. Our code and trained models are available at this http URL .
Octree Guided CNN With Spherical Kernels for 3D Point Clouds

We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds. The network architecture capitalizes on the sparse nature of irregular point clouds,and hierarchically coarsens the data representation with space partitioning. At the same time, the proposed spherical kernels systematically quantize point neighborhoods to identify local geometric structures in the data, while maintaining the properties of translation-invariance and asymmetry. We specify spherical kernels with the help of network neurons that in turn are associated with spatial locations.We exploit this association to avert dynamic kernel generation during network training that enables efficient learning with high resolution point clouds. The effectiveness of the proposed technique is established on the benchmark tasks of 3D object classification and segmentation, achieving competitive performance on ShapeNet and RueMonge2014 datasets.
Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online.
Attack-Defense Quantification Based On Game-Theory

With the developing of the attack and defense technology, the cyber environment has been more and more sophisticated. We failed to give an accurate evaluation of network security situation, as we lack a more accurate quantitative evaluation of attack-defense behaviors. In response to this situation, we proposed an attack-defense stochastic game model (ADSGM), analyzed the different security property of distinct defense mechanism, and put forward a corresponding utility calculation coping with the distinct defense mechanism. Through a case study, we showed the impact of active defense and the risk of attack exposure, demonstrated the effectiveness of our methods on attack-defense behavior quantification. This paper filled with the gap in the quantitative assessment of defensive measures, to make the quantitative evaluation of attack-defense more comprehensive and accurate.
Recursive Subtree Composition in LSTM-Based Dependency Parsing

The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a sequential model by recursively composing subtree representations (composition) in a transition-based parser that uses features extracted by a BiLSTM. Composition seems superfluous with such a model, suggesting that BiLSTMs capture information about subtrees. We perform model ablations to tease out the conditions under which composition helps. When ablating the backward LSTM, performance drops and composition does not recover much of the gap. When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information. We take the backward LSTM to be related to lookahead features and the forward LSTM to the rich history-based features both crucial for transition-based parsers. To capture history-based information, composition is better than a forward LSTM on its own, but it is even better to have a forward LSTM as part of a BiLSTM. We correlate results with language properties, showing that the improved lookahead of a backward LSTM is especially important for head-final languages.
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge. In this work we explore how techniques from natural language processing and machine learning can be used to automatically extract chemical insights from large collections of documents. We first describe how to download and process documents from a variety of sources - journal articles, conference proceedings (including NTREM), the US Patent & Trademark Office, and the Defense Technical Information Center archive on this http URL. We present a custom NLP pipeline which uses open source NLP tools to identify the names of chemical compounds and relates them to function words ("underwater", "rocket", "pyrotechnic") and property words ("elastomer", "non-toxic"). After explaining how word embeddings work we compare the utility of two popular word embeddings - word2vec and GloVe. Chemical-chemical and chemical-application relationships are obtained by doing computations with word vectors. We show that word embeddings capture latent information about energetic materials, so that related materials appear close together in the word embedding space.
Variational Inference to Measure Model Uncertainty in Deep Neural Networks

We present a novel approach for training deep neural networks in a Bayesian way. Classical, i.e. non-Bayesian, deep learning has two major drawbacks both originating from the fact that network parameters are considered to be deterministic. First, model uncertainty cannot be measured thus limiting the use of deep learning in many fields of application and second, training of deep neural networks is often hampered by overfitting. The proposed approach uses variational inference to approximate the intractable a posteriori distribution on basis of a normal prior. The variational density is designed in such a way that the a posteriori uncertainty of the network parameters is represented per network layer and depending on the estimated parameter expectation values. This way, only a few additional parameters need to be optimized compared to a non-Bayesian network. We apply this Bayesian approach to train and test the LeNet architecture on the MNIST dataset. Compared to classical deep learning, the test error is reduced by 15%. In addition, the trained model contains information about the parameter uncertainty in each layer. We show that this information can be used to calculate credible intervals for the prediction and to optimize the network architecture for a given training data set.
Towards Efficient Data Valuation Based on the Shapley Value

{\em ``How much is my data worth? ''} is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of \emph{data valuation} by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires \emph{exponential} time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets.
Total phenolic content, quercetin, and antioxidant activity of gandaria (Bouea Macrophylla Griff.) leaf extract at two stages of maturity

The aim of this study was to determine the effect of leaf maturity stage and extraction solvents on Total Phenolic Content (TPC), quercetin, and antioxidant activity of gandaria leaves as well as the relationship of TPC and quercetin content to antioxidant activity. In this study, leaves were extracted using maceration method with three different solvents (96% ethanol, ethyl acetate, and hexane) followed by evaporation using rotary evaporator to obtain Crude Extracts (CE). Tender and mature gandaria leaves from Indonesian Institute of Science were examined for TPC and quercetin content. Measurement of antioxidant activity were performed by Ferric Reducing Antioxidant Power (FRAP) method. The antioxidant compound analysis indicated that hexane extract of mature gandaria leaves contained the highest TPC and quercetin content (30.84±0.54 mg GAE/g and 4.36±0.23 mg QE/g, respectively). Meanwhile, the ethanol extract of mature gandaria leaves demonstrated the highest reducing power (5.62±0.38 mg FeSO4 equivalent/g). These findings showed positive correlation between TPC and reducing power as well as quercetin and reducing power from gandaria leaf extracts depending on their maturity stage and extraction solvents. In addition, mature gandaria leaves had better extraction yield, making it potential for development of functional food.
Learning where to look: Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding, while neglecting the effect of learning discriminative visual features. In this paper, we study the significance of the discriminative region localization. We propose a semantic-guided multiattention localization model, which automatically discovers the most discriminative parts of objects for zero-shot learning without any human annotations. Our model jointly learns cooperative global and local features from the whole object as well as the detected parts to categorize objects based on semantic descriptions. Moreover, with the joint supervision of embedding softmax loss and classcenter triplet loss, the model is encouraged to learn features with high inter-class dispersion and intraclass compactness. Through comprehensive experiments on three widely used zero-shot learning benchmarks, we show the efficacy of the multiattention localization and our proposed approach improves the state-of-the-art results by a considerable margin.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network

Resource balancing within complex transportation networks is one of the most important problems in real logistics domain. Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting. However, the high complexity of transportation routes, severe uncertainty of future demand and supply, together with non-convex business constraints make it extremely challenging in the traditional resource management field. In this paper, we propose a novel sophisticated multi-agent reinforcement learning approach to address these challenges. In particular, inspired by the externalities especially the interactions among resource agents, we introduce an innovative cooperative mechanism for state and reward design resulting in more effective and efficient transportation. Extensive experiments on a simulated ocean transportation service demonstrate that our new approach can stimulate cooperation among agents and lead to much better performance. Compared with traditional solutions based on combinatorial optimization, our approach can give rise to a significant improvement in terms of both performance and stability.
The State of Sparsity in Deep Neural Networks

We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Additionally, we replicate the experiments performed by (Frankle & Carbin, 2018) and (Liu et al., 2018) at scale and show that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization. Together, these results highlight the need for large-scale benchmarks in the field of model compression. We open-source our code, top performing model checkpoints, and results of all hyperparameter configurations to establish rigorous baselines for future work on compression and sparsification.
Mapping solar array location, size, and capacity using deep learning and overhead imagery

The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Learning continuous representations of nodes is attracting growing interest in both academia and industry recently, due to their simplicity and effectiveness in a variety of applications. Most of existing node embedding algorithms and systems are capable of processing networks with hundreds of thousands or a few millions of nodes. However, how to scale them to networks that have tens of millions or even hundreds of millions of nodes remains a challenging problem. In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system. On the CPU end, augmented edge samples are parallelly generated by random walks in an online fashion on the network, and serve as the training data. On the GPU end, a novel parallel negative sampling is proposed to leverage multiple GPUs to train node embeddings simultaneously, without much data transfer and synchronization. Moreover, an efficient collaboration strategy is proposed to further reduce the synchronization cost between CPUs and GPUs. Experiments on multiple real-world networks show that GraphVite is super efficient. It takes only about one minute for a network with 1 million nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20 hours for a network with 66 million nodes and 1.8 billion edges. Compared to the current fastest system, GraphVite is about 50 times faster without any sacrifice on performance.
Counting subgraphs via DAG tree decompositions

We study the problem of counting subgraphs in a graph $G$ of degeneracy $d$, a good sparsity measure for many real-world graphs. Our main tool is a decomposition for directed acyclic graphs, which we call DAG tree decomposition. This decomposition induces a width measure $s(H)$ on every undirected pattern $H$, and leads to a dynamic programming to count the copies of $H$ in $G$ in time $f(d,k) \cdot \tilde{O}(n^{s(H)})$. This is tight in the following sense: if any algorithm solves the problem in time $f(d,k) \cdot n^{o(s(H)/\ln s(H))}$ for all patterns $H$, then ETH (the Exponential Time Hypothesis) fails. This result has multiple consequences. First, the induced or non-induced copies of any pattern $H$ on $k$ nodes (even disconnected ones) can be counted in time $f(d,k) \cdot n^{O(\alpha(H))}$ where $\alpha(H)$ is the independence number of $H$, and no algorithm can do so in $f(d,k) \cdot n^{o(\alpha(H)/\ln \alpha(H))}$ unless ETH fails. Second, by bounding $s(H)$ we rediscover and enrich classic results. For cliques minus $\epsilon$ edges we give a time bound of $f(d,k) \cdot \tilde{O}(n^{\lceil \frac{1}{2} + \sqrt{\frac{\epsilon}{2}} \, \rceil})$, which extends the classic $f(d,k) \cdot O(n)$ bound for cliques by Chiba and Nishizeki [9]. For complete multipartite graphs, we can count non-induced copies in $f(d,k) \cdot \tilde{O}(n)$ (essentially matching an $f(d,k) \cdot O(n)$ bound by Eppstein [13]), which becomes $f(d,k) \cdot \tilde{O}(n^{\lfloor\frac{\epsilon}{4}\rfloor+2})$ for complete multipartite graphs plus $\epsilon$ edges. Third, we can count the induced or non-induced copies of any $H$ in $f(d,k) \cdot\tilde{O}(n^{\frac{k}{4} + 2})$, beating the decades-old state-of-the-art $O(n^{\frac{\omega k}{3}+2})$ algorithm of Ne\v{s}et\v{r}il and Poljak [28] for $d < n^{0.721}$ ($\omega$ is the matrix multiplication exponent).
Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information

Unsupervised neural machine translation (UNMT) requires only monolingual data of similar language pairs during training and can produce bi-directional translation models with relatively good performance on alphabetic languages (Lample et al., 2018). However, no research has been done to logographic language pairs. This study focuses on Chinese-Japanese UNMT trained by data containing sub-character (ideograph or stroke) level information which is decomposed from character level data. BLEU scores of both character and sub-character level systems were compared against each other and the results showed that despite the effectiveness of UNMT on character level data, sub-character level data could further enhance the performance, in which the stroke level system outperformed the ideograph level system.