OpenRaiser/Intern-Atlas huggingface dataset:
Content-based subject classification at article level in biomedical context
Venue: arXiv cs.DL 2021
Year: 2021

Subject classification is an important task to analyze scholarly publications. In general, mainly two kinds of approaches are used: classification at a journal level and classification at the article level. We propose a mixed approach, leveraging on embeddings technique in NLP to train classifiers with article metadata (title, abstract, keywords in particular) labelled with the journal-level classification FoR (Fields of Research) and then apply these classifiers at the article level. We use this approach in the context of biomedical publications using metadata from Pubmed. Fasttext classifiers are trained with FoR codes and used to classify publications based on their available metadata. Results show that using a stratification sampling strategy for training help reduce the bias due to unbalanced field distribution. An implementation of the method is proposed on the repository https://github.com/dataesr/scientific_tagger
GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions
Venue: arXiv cs.CV 2021
Year: 2021

Generating videos from text is a challenging task due to its high computational requirements for training and infinite possible answers for evaluation. Existing works typically experiment on simple or small datasets, where the generalization ability is quite limited. In this work, we propose GODIVA, an open-domain text-to-video pretrained model that can generate videos from text in an auto-regressive manner using a three-dimensional sparse attention mechanism. We pretrain our model on Howto100M, a large-scale text-video dataset that contains more than 136 million text-video pairs. Experiments show that GODIVA not only can be fine-tuned on downstream video generation tasks, but also has a good zero-shot capability on unseen texts. We also propose a new metric called Relative Matching (RM) to automatically evaluate the video generation quality. Several challenges are listed and discussed as future work.
Single-Source SIE for Two-Dimensional Arbitrarily Connected Penetrable and PEC Objects with Nonconformal Meshes
Venue: arXiv cs.CE 2021
Year: 2021

We proposed a simple and efficient modular single-source surface integral equation (SS-SIE) formulation for electromagnetic analysis of arbitrarily connected penetrable and perfectly electrical conductor (PEC) objects in two-dimensional space. In this formulation, a modular equivalent model for each penetrable object consisting of the composite structure is first independently constructed through replacing it by the background medium, no matter whether it is surrounded by the background medium, other media, or partially connected objects, and enforcing an equivalent electric current density on the boundary to remain fields in the exterior region unchanged. Then, by combining all the modular models and any possible PEC objects together, an equivalent model for the composite structure can be derived. The troublesome junction handling techniques are not needed and non-conformal meshes are intrinsically supported. The proposed SS-SIE formulation is simple to implement, efficient, and flexible, which shows significant performance improvement in terms of CPU time compared with the original SS-SIE formulation and the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation. Several numerical examples including the coated dielectric cuboid, the large lossy objects, the planar layered dielectric structure, and the partially connected dielectric and PEC structure are carried out to validate its accuracy, efficiency and robustness.
Scaling End-to-End Models for Large-Scale Multilingual ASR
Venue: arXiv cs.CL 2021
Year: 2021

Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data. Existing work has shown positive transfer from high resource to low resource languages. However, degradations on high resource languages are commonly observed due to interference from the heterogeneous multilingual data and reduction in per-language capacity. We conduct a capacity study on a 15-language task, with the amount of data per language varying from 7.6K to 53.5K hours. We adopt GShard [1] to efficiently scale up to 10B parameters. Empirically, we find that (1) scaling the number of model parameters is an effective way to solve the capacity bottleneck - our 500M-param model already outperforms monolingual baselines and scaling it to 1B and 10B brought further quality gains; (2) larger models are not only more data efficient, but also more efficient in terms of training cost as measured in TPU days - the 1B-param model reaches the same accuracy at 34% of training time as the 500M-param model; (3) given a fixed capacity budget, adding depth works better than width and large encoders do better than large decoders; (4) with continuous training, they can be adapted to new languages and domains.
RSSI-Based Machine Learning with Pre- and Post-Processing for Cell-Localization in IWSNs
Venue: arXiv cs.IT 2021
Year: 2021

Industrial wireless sensor networks are becoming crucial for modern manufacturing. If the sensors in those networks are mobile, the position information, besides the sensor data itself, can be of high relevance. E.g. this position information can increase the trustability of a wireless sensor measurement by assuring that the sensor is not physically removed, off track, or otherwise compromised. In certain applications, localization information at cell-level, whether the sensor is inside or outside a room or cell, is sufficient. For this, localization using Received Signal Strength Indicator (RSSI) measurements is very popular since RSSI values are available in almost all existing technologies and no direct interaction with the mobile sensor node and its communication in the network is needed. For this scenario, we propose methods to improve the robustness and accuracy of common machine learning classifiers, by using features based on short-term moments and a second classification stage using Hidden Markov Models. With the data from an extensive measurement campaign, we show the applicability of our method and achieve a cell-level localization accuracy of 93.5\%.
Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
Venue: arXiv cs.CV 2021
Year: 2021

Vehicle Re-identification aims to identify a specific vehicle across time and camera view. With the rapid growth of intelligent transportation systems and smart cities, vehicle Re-identification technology gets more and more attention. However, due to the difference of shooting angle and the high similarity of vehicles belonging to the same brand, vehicle re-identification becomes a great challenge for existing method. In this paper, we propose a vehicle attribute-guided method to re-rank vehicle Re-ID result. The attributes used include vehicle orientation and vehicle brand . We also focus on the camera information and introduce camera mutual exclusion theory to further fine-tune the search results. In terms of feature extraction, we combine the data augmentations of multi-resolutions with the large model ensemble to get a more robust vehicle features. Our method achieves mAP of 63.73% and rank-1 accuracy 76.61% in the CVPR 2021 AI City Challenge.
Deep Learning Based Steel Pipe Weld Defect Detection
Venue: arXiv cs.CV 2021
Year: 2021

Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc. If there is some defect in steel pipes, it will lead to serious adverse consequences. Applying object detection in the field of deep learning to pipe weld defect detection and identification can effectively improve inspection efficiency and promote the development of industrial automation. Most predecessors used traditional computer vision methods applied to detect defects of steel pipe weld seams. However, traditional computer vision methods rely on prior knowledge and can only detect defects with a single feature, so it is difficult to complete the task of multi-defect classification, while deep learning is end-to-end. In this paper, the state-of-the-art single-stage object detection algorithm YOLOv5 is proposed to be applied to the field of steel pipe weld defect detection, and compared with the two-stage representative object detection algorithm Faster R-CNN. The experimental results show that applying YOLOv5 to steel pipe weld defect detection can greatly improve the accuracy, complete the multi-classification task, and meet the criteria of real-time detection.
Post-training deep neural network pruning via layer-wise calibration
Venue: arXiv cs.CV 2021
Year: 2021

We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices. We propose a data-free extension of the approach for computer vision models based on automatically-generated synthetic fractal images. We obtain state-of-the-art results for data-free neural network pruning, with ~1.5% top@1 accuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real data, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in 8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We release the code as a part of the OpenVINO(TM) Post-Training Optimization tool.
Safety-Control of Mobile Robots Under Time-Delay Using Barrier Certificates and a Two-Layer Predictor
Venue: arXiv cs.RO 2021
Year: 2021

Performing swift and agile maneuvers is essential for the safe operation of autonomous mobile robots. Moreover, the presence of time-delay restricts the response time of the system and hinders the safety performance. Thus, this paper proposes a modular and scalable safety-control design that utilizes the Smith predictor and barrier certificates to safely and consistently avoid obstacles with different footprints. The proposed solution includes a two-layer predictor to compensate for the time-delay in the servo-system and angle control loops. The proposed predictor configuration dramatically improves the transient performance and reduces response time. Barrier certificates are used to determine the safe range of the robot's heading angle to avoid collisions. The proposed obstacle avoidance technique conveniently integrates with various trajectory tracking algorithms, which enhances design flexibility. The angle condition is adaptively calculated and corrects the robot's heading angle and angular velocity. Also, the proposed method accommodates multiple obstacles and decouples the control structure from the obstacles' shape, count, and distribution. The control structure has only eight tunable parameters facilitating control calibration and tuning in large systems of mobile robots. Extensive experimental results verify the effectiveness of the proposed safety-control.
Some constructive variants of S4 with the finite model property
Venue: arXiv cs.LO 2021
Year: 2021

The logics CS4 and IS4 are intuitionistic variants of the modal logic S4. Whether the finite model property holds for each of these logics has been a long-standing open problem. In this paper we introduce two logics closely related to IS4: GS4, obtained by adding the Godel-Dummett axiom to IS4, and S4I, obtained by reversing the roles of the modal and intuitionistic relations. We then prove that CS4, GS4, and S4I all enjoy the finite model property.
Fast Compilation and Execution of SQL Queries with WebAssembly
Venue: arXiv cs.DB 2021
Year: 2021

Interpreted execution of queries, as in the vectorized model, suffers from interpretation overheads. By compiling queries this interpretation overhead is eliminated at the cost of a compilation phase that delays execution, sacrificing latency for throughput. For short-lived queries, minimizing latency is important, while for long-running queries throughput outweighs latency. Because neither a purely interpretive model nor a purely compiling model can provide low latency and high throughput, adaptive solutions emerged. Adaptive systems seamlessly transition from interpreted to compiled execution, achieving low latency for short-lived queries and high throughput for long-running queries. However, these adaptive systems pose an immense development effort and require expert knowledge in both interpreter and compiler design. In this work, we investigate query execution by compilation to WebAssembly. We are able to compile even complex queries in less than a millisecond to machine code with near-optimal performance. By delegating execution of WebAssembly to the V8 engine, we are able to seamlessly transition from rapidly compiled yet non-optimized code to thoroughly optimized code during execution. Our approach provides both low latency and high throughput, is adaptive out of the box, and is straight forward to implement. The drastically reduced compilation times even enable us to explore generative programming of library code, that is fully inlined by construction. Our experimental evaluation confirms that our approach yields competitive and sometimes superior performance.
GTN-ED: Event Detection Using Graph Transformer Networks
Venue: arXiv cs.CL 2021
Year: 2021

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Networks (GTN). We integrate GTNs to leverage dependency relations on two existing homogeneous-graph-based models, and demonstrate an improvement in the F1 score on the ACE dataset.
Latent Factor Decomposition Model: Applications for Questionnaire Data
Venue: arXiv cs.LG 2021
Year: 2021

The analysis of clinical questionnaire data comes with many inherent challenges. These challenges include the handling of data with missing fields, as well as the overall interpretation of a dataset with many fields of different scales and forms. While numerous methods have been developed to address these challenges, they are often not robust, statistically sound, or easily interpretable. Here, we propose a latent factor modeling framework that extends the principal component analysis for both categorical and quantitative data with missing elements. The model simultaneously provides the principal components (basis) and each patients' projections on these bases in a latent space. We show an application of our modeling framework through Irritable Bowel Syndrome (IBS) symptoms, where we find correlations between these projections and other standardized patient symptom scales. This latent factor model can be easily applied to different clinical questionnaire datasets for clustering analysis and interpretable inference.
Stealthy Backdoors as Compression Artifacts
Venue: arXiv cs.CR 2021
Year: 2021

In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a small trigger pattern. Model compression is a widely-used approach for reducing the size of deep learning models without much accuracy loss, enabling resource-hungry models to be compressed for use on resource-constrained devices. In this paper, we study the risk that model compression could provide an opportunity for adversaries to inject stealthy backdoors. We design stealthy backdoor attacks such that the full-sized model released by adversaries appears to be free from backdoors (even when tested using state-of-the-art techniques), but when the model is compressed it exhibits highly effective backdoors. We show this can be done for two common model compression techniques -- model pruning and model quantization. Our findings demonstrate how an adversary may be able to hide a backdoor as a compression artifact, and show the importance of performing security tests on the models that will actually be deployed not their precompressed version.
Explanation-Based Human Debugging of NLP Models: A Survey
Venue: arXiv cs.CL 2021
Year: 2021

Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions.
Ethics-Based Auditing to Develop Trustworthy AI
Venue: arXiv cs.CY 2021
Year: 2021

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.
Negative 3D gadgets in origami extrusions with a supporting triangle on the back side
Venue: arXiv cs.CG 2021
Year: 2021

In our previous two papers, we studied (positive) 3D gadgets in origami extrusions which create a top face parallel to the ambient paper and two side faces sharing a ridge with two simple outgoing pleats. Then a natural problem comes up whether it is possible to construct a `negative' 3D gadget from any positive one having the same net without changing the outgoing pleats, that is, to sink the top and two side faces of any positive 3D gadget to the reverse side without changing the outgoing pleats. Of course, simply sinking the faces causes a tear of the paper, and thus we have to modify the crease pattern. There are two known constructions of negative 3D gadgets before ours, but they do not solve this problem because their outgoing pleats are different from positive ones. In the present paper we give an affirmative solution to the above problem. For this purpose, we present three constructions of negative 3D gadgets with a supporting triangle on the back side, which are based on our previous ones of positive 3D gadgets. The first two are an extension of those presented in our previous paper, and the third is new. We prove that our first and third constructions solve the problem. Our solutions enable us to deal with positive and negative 3D gadgets on the same basis, so that we can construct from an origami extrusion constructed with 3D gadgets its negative using the same pleats if there are no interferences among the 3D gadgets. We also treat repetition/division of negative 3D gadgets under certain conditions, which reduces their interferences with others.
Types are Internal $\infty$-Groupoids
Venue: arXiv cs.LO 2021
Year: 2021

By extending type theory with a universe of definitionally associative and unital polynomial monads, we show how to arrive at a definition of opetopic type which is able to encode a number of fully coherent algebraic structures. In particular, our approach leads to a definition of $\infty$-groupoid internal to type theory and we prove that the type of such $\infty$-groupoids is equivalent to the universe of types. That is, every type admits the structure of an $\infty$-groupoid internally, and this structure is unique.
Memory Reduction using a Ring Abstraction over GPU RDMA for Distributed Quantum Monte Carlo Solver
Venue: arXiv cs.DC 2021
Year: 2021

Scientific applications that run on leadership computing facilities often face the challenge of being unable to fit leading science cases onto accelerator devices due to memory constraints (memory-bound applications). In this work, the authors studied one such US Department of Energy mission-critical condensed matter physics application, Dynamical Cluster Approximation (DCA++), and this paper discusses how device memory-bound challenges were successfully reduced by proposing an effective "all-to-all" communication method -- a ring communication algorithm. This implementation takes advantage of acceleration on GPUs and remote direct memory access (RDMA) for fast data exchange between GPUs. Additionally, the ring algorithm was optimized with sub-ring communicators and multi-threaded support to further reduce communication overhead and expose more concurrency, respectively. The computation and communication were also analyzed by using the Autonomic Performance Environment for Exascale (APEX) profiling tool, and this paper further discusses the performance trade-off for the ring algorithm implementation. The memory analysis on the ring algorithm shows that the allocation size for the authors' most memory-intensive data structure per GPU is now reduced to 1/p of the original size, where p is the number of GPUs in the ring communicator. The communication analysis suggests that the distributed Quantum Monte Carlo execution time grows linearly as sub-ring size increases, and the cost of messages passing through the network interface connector could be a limiting factor.
GPU Acceleration of 3D Agent-Based Biological Simulations
Venue: arXiv cs.DC 2021
Year: 2021

Researchers in biology are faced with the tough challenge of developing high-performance computer simulations of their increasingly complex agent-based models. BioDynaMo is an open-source agent-based simulation platform that aims to alleviate researchers from the intricacies that go into the development of high-performance computing. Through a high-level interface, researchers can implement their models on top of BioDynaMo's multi-threaded core execution engine to rapidly develop simulations that effectively utilize parallel computing hardware. In biological agent-based modeling, the type of operations that are typically the most compute-intensive are those that involve agents interacting with their local neighborhood. In this work, we investigate the currently implemented method of handling neighborhood interactions of cellular agents in BioDynaMo, and ways to improve the performance to enable large-scale and complex simulations. We propose to replace the kd-tree implementation to find and iterate over the neighborhood of each agent with a uniform grid method that allows us to take advantage of the massively parallel architecture of graphics processing units (GPUs). We implement the uniform grid method in both CUDA and OpenCL to address GPUs from all major vendors and evaluate several techniques to further improve the performance. Furthermore, we analyze the performance of our implementations for models with a varying density of neighboring agents. As a result, the performance of the mechanical interactions method improved by up to two orders of magnitude in comparison to the multithreaded baseline version. The implementations are open-source and publicly available on Github.