Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming

Certifying the safety or robustness of neural networks against input uncertainties and adversarial attacks is an emerging challenge in the area of safe machine learning and control. To provide such a guarantee, one must be able to bound the output of neural networks when their input changes within a bounded set. In this article, we propose a semidefinite programming (SDP) framework to address this problem for feed-forward neural networks with general activation functions and input uncertainty sets. Our main idea is to abstract various properties of activation functions (e.g., monotonicity, bounded slope, bounded values, and repetition across layers) with the formalism of quadratic constraints. We then analyze the safety properties of the abstracted network via the S-procedure and SDP. Our framework spans the tradeoff between conservatism and computational efficiency and applies to problems beyond safety verification. We evaluate the performance of our approach via numerical problem instances of various sizes.
Interpreting Active Learning Methods Through Information Losses

We propose a new way of interpreting active learning methods by analyzing the information `lost' upon sampling a random variable. We use some recent analytical developments of these losses to formally prove that facility location methods reduce these losses under mild assumptions, and to derive a new data dependent bound on information losses that can be used to evaluate other active learning methods. We show that this new bound is extremely tight to experiment, and further show that the bound has a decent predictive power for classification accuracy.
Learning to Find Hard Instances of Graph Problems

Finding hard instances, which need a long time to solve, of graph problems such as the graph coloring problem and the maximum clique problem, is important for (1) building a good benchmark for evaluating the performance of algorithms, and (2) analyzing the algorithms to accelerate them. The existing methods for generating hard instances rely on parameters or rules that are found by domain experts; however, they are specific to the problem. Hence, it is difficult to generate hard instances for general cases. To address this issue, in this paper, we formulate finding hard instances of graph problems as two equivalent optimization problems. Then, we propose a method to automatically find hard instances by solving the optimization problems. The advantage of the proposed algorithm over the existing rule based approach is that it does not require any task specific knowledge. To the best of our knowledge, this is the first non-trivial method in the literature to automatically find hard instances. Through experiments on various problems, we demonstrate that our proposed method can generate instances that are a few to several orders of magnitude harder than the random based approach in many settings. In particular, our method outperforms rule-based algorithms in the 3-coloring problem.
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. The Bayesian optimization routine involves learning a response surface and maximizing a score to select the most valuable inputs to be queried at the next iteration. These key steps are subject to the curse of dimensionality so that Bayesian optimization does not scale beyond 10--20 parameters. In this work, we address this issue and propose a high-dimensional BO method that learns a nonlinear low-dimensional manifold of the input space. We achieve this with a multi-layer neural network embedded in the covariance function of a Gaussian process. This approach applies unsupervised dimensionality reduction as a byproduct of a supervised regression solution. This also allows exploiting data efficiency of Gaussian process models in a Bayesian framework. We also introduce a nonlinear mapping from the manifold to the high-dimensional space based on multi-output Gaussian processes and jointly train it end-to-end via marginal likelihood maximization. We show this intrinsically low-dimensional optimization outperforms recent baselines in high-dimensional BO literature on a set of benchmark functions in 60 dimensions.
An IDEA: An Ingestion Framework for Data Enrichment in AsterixDB

Big Data today is being generated at an unprecedented rate from various sources such as sensors, applications, and devices, and it often needs to be enriched based on other reference information to support complex analytical queries. Depending on the use case, the enrichment operations can be compiled code, declarative queries, or machine learning models with different complexities. For enrichments that will be frequently used in the future, it can be advantageous to push their computation into the ingestion pipeline so that they can be stored (and queried) together with the data. In some cases, the referenced information may change over time, so the ingestion pipeline should be able to adapt to such changes to guarantee the currency and/or correctness of the enrichment results. In this paper, we present a new data ingestion framework that supports data ingestion at scale, enrichments requiring complex operations, and adaptiveness to reference data changes. We explain how this framework has been built on top of Apache AsterixDB and investigate its performance at scale under various workloads.
Epileptic seizure classification using statistical sampling and a novel feature selection algorithm

Epilepsy is a well-known neuronal disorder that can be identified by interpretation of the electroencephalogram (EEG) signal. Usually, the length of an EEG signal is quite long which is challenging to interpret manually. In this work, we propose an automated epileptic seizure detection method by applying a two-step minimization technique: first, we reduce the data points using a statistical sampling technique and then, we minimize the number of features using our novel feature selection algorithm. We then apply different machine learning algorithms for performance measurement of the proposed feature selection algorithm. The experimental results outperform some of the state-of-the-art methods for seizure detection using the reduced data points and the least number of features.
Pancreas Segmentation via Spatial Context based U-net and Bidirectional LSTM

Pancreas is characterized by small size and irregular shape, so achieving accurate pancreas segmentation is challenging. Traditional 2D pancreas segmentation network based on the independent 2D image slices, which often leads to spatial discontinuity problem. Therefore, how to utility spatial context information is the key point to improve the segmentation quality. In this paper, we proposed a divide-and-conquer strategy, divided the abdominal CT scans into several isometric blocks. And we designed a multiple channels convolutional neural network to learn the local spatial context characteristics from blocks called SCU-Net. SCU-Net is a partial 3D segmentation idea, which transforms overall pancreas segmentation into a combination of multiple local segmentation results. In order to improve the segmentation accuracy for each layer, we also proposed a new loss function for inter-slice constrain and regularization. Thereafter, we introduced the BiCLSTM network for stimulating the interaction between bidirectional segmentation sequence, thus making up the boundary defect and fault problem of the segmentation results. We trained SCU-Net+BiLSTM network respectively, and evaluated segmentation result on the NIH data set. Keywords: Pancreas Segmentation, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning, Inter-slice Regularization
Pushing the Boundaries of Asymptotic Optimality for Sampling-based Roadmaps In Motion And Task Planning

Sampling-based roadmaps have been popular methods for robot motion and task planning, given their generality and effectiveness in high-dimensional configuration spaces (C-spaces). Following advances in random geometric graphs, a seminal analysis result argued the conditions for asymptotic optimality of these approaches. In particular, a connection radius for each new C-space sample needs to be in the order of $ \gamma (\log n / n)^{1/d} $, where $n$ is the existing number of roadmap nodes and $d$ is the dimensionality of the C-space. This prior analysis, as well as subsequent efforts, also specified a sufficient lower bound for the constant $\gamma$ for asymptotic optimality. All of these results assumed that for a finite number of samples there is a path with positive clearance from obstacles. Nevertheless, manipulation task planning requires solving problems were the start and the goal lie on the boundary of the configuration space. The current work builds on previous work, to: a) obtain an estimate of $\gamma$ in terms of a bound on the dispersion of the samples; and b) propose the modifications necessary to make asymptotic optimality hold when the start and goal lie on the boundary of the C-space under certain assumptions regarding the boundary. The last point generalizes these properties to manipulation task planning and reduces the method's requirements for a connection radius that achieves asymptotic optimality in this domain as well as the assumptions regarding the boundary's smoothness relative to prior work.
Enhancing Clinical Concept Extraction with Contextual Embedding

OBJECTIVE Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). MATERIALS AND METHODS Both off-the-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical Information Mart for Intensive Care III) are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings and compare these on 4 concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pretraining time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. RESULTS Contextual embeddings pretrained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 80.74, and 81.65. CONCLUSIONS We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate that contextual embeddings encode valuable semantic information not accounted for in traditional word representations.
High-performance polyimides based on pyridine and xanthene pendant groups; synthesis, characterization, photoactivity, thermal, antibacterial, and Cr(VI) ion adsorption properties

A group of new polyimides (PIs) containing xanthene bulky groups and heterocyclic pyridine ring were synthesized by polycondensation reaction by using new symmetric and asymmetric diamines with available dianhydrides. The prepared diamines and PIs were characterized by Fourier transform infrared, elemental analysis, and proton nuclear magnetic resonance spectroscopy and carbon nuclear magnetic resonance spectroscopy. The PIs have desirable solubility in common aprotic solvents like dimethyl sulfoxide, N-methyl-2-pyrrolidone, dimethylacetamide, and pyridine as a less polar solvent. The PIs with inherent viscosities in the range of 0.51–0.72 dL/g were amorphous in nature, showed glass transition temperatures (Tg s) of about 270–300°C, and 10% weight loss at temperatures 384–443°C. They showed fluorescence emission (quantum yield 7–12%) due to the presence of xanthene and pyridine groups in their structure. The prepared PIs showed tensile strength of 81–91 MPa with elongation at break of 19–23%. The antibacterial properties of the PIs against Gram negative and Gram positive bacteria and funguses were investigated. Also, a membrane of PI prepared by electrospinning method was used for the removal of different concentrations of Cr(VI) from aqueous solutions.
Actions Generation from Captions

Sequence transduction models have been widely explored in many natural language processing tasks. However, the target sequence usually consists of discrete tokens which represent word indices in a given vocabulary. We barely see the case where target sequence is composed of continuous vectors, where each vector is an element of a time series taken successively in a temporal domain. In this work, we introduce a new data set, named Action Generation Data Set (AGDS) which is specifically designed to carry out the task of caption-to-action generation. This data set contains caption-action pairs. The caption is comprised of a sequence of words describing the interactive movement between two people, and the action is a captured sequence of poses representing the movement. This data set is introduced to study the ability of generating continuous sequences through sequence transduction models. We also propose a model to innovatively combine Multi-Head Attention (MHA) and Generative Adversarial Network (GAN) together. In our model, we have one generator to generate actions from captions and three discriminators where each of them is designed to carry out a unique functionality: caption-action consistency discriminator, pose discriminator and pose transition discriminator. This novel design allowed us to achieve plausible generation performance which is demonstrated in the experiments.
Data-Driven Gait Segmentation for Walking Assistance in a Lower-Limb Assistive Device

Hybrid systems, such as bipedal walkers, are challenging to control because of discontinuities in their nonlinear dynamics. Little can be predicted about the systems’ evolution without modeling the guard conditions that govern transitions between hybrid modes, so even systems with reliable state sensing can be difficult to control. We propose an algorithm that allows for determining the hybrid mode of a system in real-time using data-driven analysis. The algorithm is used with data-driven dynamics identification to enable model predictive control based entirely on data. Two examples—a simulated hopper and experimental data from a bipedal walker—are used. In the context of the first example, we are able to closely approximate the dynamics of a hybrid SLIP model and then successfully use them for control in simulation. In the second example, we demonstrate gait partitioning of human walking data, accurately differentiating between stance and swing, as well as selected subphases of swing. We identify contact events, such as heel strike and toe-off, without a contact sensor using only kinematics data from the knee and hip joints, which could be particularly useful in providing online assistance during walking. Our algorithm does not assume a predefined gait structure or gait phase transitions, lending itself to segmentation of both healthy and pathological gaits. With this flexibility, impairment-specific rehabilitation strategies or assistance could be designed.
Using Causal Analysis to Learn Specifications from Task Demonstrations

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work we show that it is possible to learn a generative model for distinct user behavioral types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use this model to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable model. An advantage of structuring generative models in this way is that it allows us to extract causal relationships between symbols that might form part of the user's specification of the task, as manifested in the demonstrations. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task.
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring

In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techniques may suffer from poor generalization performances on opaque containers or concerning measuring precision. To tackle these drawbacks, we propose to make use of audio vibration sensing and design a deep neural network PouringNet to predict the liquid height from the audio fragment during the robotic pouring task. PouringNet is trained on our collected real-world pouring dataset with multimodal sensing data, which contains more than 3000 recordings of audio, force feedback, video and trajectory data of the human hand that performs the pouring task. Each record represents a complete pouring procedure. We conduct several evaluations on PouringNet with our dataset and robotic hardware. The results demonstrate that our PouringNet generalizes well across different liquid containers, positions of the audio receiver, initial liquid heights and types of liquid, and facilitates a more robust and accurate audio-based perception for robotic pouring.
Shapes From Echoes: Uniqueness From Point-to-Plane Distance Matrices

We study the problem of localizing a configuration of points and planes from the collection of point-to-plane distances. This problem models simultaneous localization and mapping from acoustic echoes as well as the “structure from sound” approach to microphone localization with unknown sources. In our earlier work we proposed computational methods for localization from point-to-plane distances and noted that such localization suffers from various ambiguities beyond the usual rigid body motions; in this paper we provide a complete characterization of uniqueness. We enumerate all cases of configurations which lead to the same distance measurements as a function of the number of planes and points, and algebraically characterize the related transformations in both 2D and 3D.
HISTEX (HISTory EXerciser) : A tool for testing the implementation of Isolation Levels of Relational Database Management Systems

We present a multi-process application called HISTEX (HISTory EXerciser), which executes input histories in a generic transactional notation on commercial DBMS platforms. HISTEX could be used to discover potential errors in the implementation of Isolation Levels by Relational Database Management Systems or cases where a system behaves over restrictively. It can also be used for performance measurements related to database workloads executing on real database systems instead of simulated environments. HISTEX has been implemented in C by utilizing Embedded SQL. However, many of its ideas could be reincarnated in new implementations that could rely on other database connectivity paradigms such as JDBC, JPA etc. We expect that by presenting some of the ideas behind its development we could re-invigorate some fresh interest and involvement in the research community regarding such tools.
Tensor-variate Mixture of Experts

When data are organized in matrices or arrays of higher dimensions (tensors), classical regression methods first transform these data into vectors, therefore ignoring the underlying structure of the data and increasing the dimensionality of the problem. This flattening operation typically leads to overfitting when only few training data is available. In this paper, we present a mixture of experts model that exploits tensorial representations for regression of tensor-valued data. The proposed formulation takes into account the underlying structure of the data and remains efficient when few training data are available. Evaluation on artificially generated data, as well as offline and real-time experiments recognizing hand movements from tactile myography prove the effectiveness of the proposed approach.
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves over time due to the influences of weather, events, holidays, etc. Existing solutions to the traffic volume inference problem often rely on dense GPS trajectories, which inevitably fail to account for the vehicles which carry no GPS devices or have them turned off. Consequently, the results are biased to taxicabs because they are almost always online for GPS tracking. In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems. Our approach employs a high-fidelity traffic simulator and deep reinforcement learning to recover full vehicle movements from the incomplete trajectories. In order to jointly model the recovered trajectories and dense GPS trajectories, we construct spatiotemporal graphs and use multi-view graph embedding to encode the multi-hop correlations between road segments into real-valued vectors. Finally, we infer the citywide traffic volumes by propagating the traffic values of monitored road segments to the unmonitored ones through masked pairwise similarities. Extensive experiments with two big regions in a provincial capital city in China verify the effectiveness of our approach.
Predicting Algorithm Classes for Programming Word Problems

We introduce the task of algorithm class prediction for programming word problems. A programming word problem is a problem written in natural language, which can be solved using an algorithm or a program. We define classes of various programming word problems which correspond to the class of algorithms required to solve the problem. We present four new datasets for this task, two multiclass datasets with 550 and 1159 problems each and two multilabel datasets having 3737 and 3960 problems each. We pose the problem as a text classification problem and train neural network and non-neural network based models on this task. Our best performing classifier gets an accuracy of 62.7 percent for the multiclass case on the five class classification dataset, Codeforces Multiclass-5 (CFMC5). We also do some human-level analysis and compare human performance with that of our text classification models. Our best classifier has an accuracy only 9 percent lower than that of a human on this task. To the best of our knowledge, these are the first reported results on such a task. We make our code and datasets publicly available.