OpenRaiser/Intern-Atlas huggingface dataset:
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Venue: arXiv cs.LG 2021
Year: 2021

Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on designing networks to maximize accuracy, little work has been conducted to understand the compatibility of architecture design spaces to varying hardware. In this paper, we analyze the neural blocks used to build Once-for-All (MobileNetV3), ProxylessNAS and ResNet families, in order to understand their predictive power and inference latency on various devices, including Huawei Kirin 9000 NPU, RTX 2080 Ti, AMD Threadripper 2990WX, and Samsung Note10. We introduce a methodology to quantify the friendliness of neural blocks to hardware and the impact of their placement in a macro network on overall network performance via only end-to-end measurements. Based on extensive profiling results, we derive design insights and apply them to hardware-specific search space reduction. We show that searching in the reduced search space generates better accuracy-latency Pareto frontiers than searching in the original search spaces, customizing architecture search according to the hardware. Moreover, insights derived from measurements lead to notably higher ImageNet top-1 scores on all search spaces investigated.
Blind Interference Alignment in 6G Optical Wireless Communications
Venue: arXiv cs.IT 2021
Year: 2021

In recent years, the demand for high speed wireless networking has increased considerably due to the enormous number of devices connected to the Internet. In this context, optical wireless communication (OWC) has received tremendous interest in the context of next generation wireless networks where OWC offers a huge unlicensed bandwidth using optical bands. OWC systems are directional and can naturally provide multiple-input and multiple-output (MIMO) configurations serving multiple users using a high number of transmitters in the indoor environment to ensure coverage. Therefore, multiuser interference must be managed efficiently to enhance the performance of OWC networks considering different metrics. A transmission scheme referred to as blind interference alignment (BIA) is proposed for OWC systems to maximize the multiplexing gain without the need for channel state information (CSI) at the transmitters, which is difficult to achieve in MIMO scenarios. However, standard BIA avoids the need for CSI at the cost of requiring channel coherence time large enough for transmitting the whole transmission block. Moreover, the methodology of BIA results in increased noise with increase in the number of transmitters and users. Therefore, various network topologies such as network centric (NC) and user centric (UC) designs are proposed to relax the limitations of BIA where these topologies divide the receiving area into multiple clusters. The results show a significant enhancement in the performance of topological BIA compared with standard BIA.
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
Venue: arXiv cs.LG 2021
Year: 2021

Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design. To address this issue, this paper develops a physics-informed convolutional neural network (CNN) for the thermal simulation surrogate. The network can learn a mapping from heat source layout to the steady-state temperature field without labeled data, which equals solving an entire family of partial difference equations (PDEs). To realize the physics-guided training without labeled data, we employ the heat conduction equation and finite difference method to construct the loss function. Since the solution is sensitive to boundary conditions, we properly impose hard constraints by padding in the Dirichlet and Neumann boundary conditions. In addition, the neural network architecture is well-designed to improve the prediction precision of the problem at hand, and pixel-level online hard example mining is introduced to overcome the imbalance of optimization difficulty in the computation domain. The experiments demonstrate that the proposed method can provide comparable predictions with numerical method and data-driven deep learning models. We also conduct various ablation studies to investigate the effectiveness of the network component and training methods proposed in this paper.
A Simple Self-calibration Method for The Internal Time Synchronization of MEMS LiDAR
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the proposed method. It should be noted that the calibration can be implemented in a simple laboratory environment without any ranging equipment and artificial participation, which greatly accelerate the progress of research and development in practical applications.
Structure-Preserving Image Super-Resolution
Venue: arXiv cs.CV 2021
Year: 2021

Structures matter in single image super-resolution (SISR). Benefiting from generative adversarial networks (GANs), recent studies have promoted the development of SISR by recovering photo-realistic images. However, there are still undesired structural distortions in the recovered images. In this paper, we propose a structure-preserving super-resolution (SPSR) method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details. Firstly, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects. On the one hand, we restore high-resolution gradient maps by a gradient branch to provide additional structure priors for the SR process. On the other hand, we propose a gradient loss to impose a second-order restriction on the super-resolved images, which helps generative networks concentrate more on geometric structures. Secondly, since the gradient maps are handcrafted and may only be able to capture limited aspects of structural information, we further extend SPSR-G by introducing a learnable neural structure extractor (NSE) to unearth richer local structures and provide stronger supervision for SR. We propose two self-supervised structure learning methods, contrastive prediction and solving jigsaw puzzles, to train the NSEs. Our methods are model-agnostic, which can be potentially used for off-the-shelf SR networks. Experimental results on five benchmark datasets show that the proposed methods outperform state-of-the-art perceptual-driven SR methods under LPIPS, PSNR, and SSIM metrics. Visual results demonstrate the superiority of our methods in restoring structures while generating natural SR images. Code is available at https://github.com/Maclory/SPSR.
Using Soft Labels to Model Uncertainty in Medical Image Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Medical image segmentation is inherently uncertain. For a given image, there may be multiple plausible segmentation hypotheses, and physicians will often disagree on lesion and organ boundaries. To be suited to real-world application, automatic segmentation systems must be able to capture this uncertainty and variability. Thus far, this has been addressed by building deep learning models that, through dropout, multiple heads, or variational inference, can produce a set - infinite, in some cases - of plausible segmentation hypotheses for any given image. However, in clinical practice, it may not be practical to browse all hypotheses. Furthermore, recent work shows that segmentation variability plateaus after a certain number of independent annotations, suggesting that a large enough group of physicians may be able to represent the whole space of possible segmentations. Inspired by this, we propose a simple method to obtain soft labels from the annotations of multiple physicians and train models that, for each image, produce a single well-calibrated output that can be thresholded at multiple confidence levels, according to each application's precision-recall requirements. We evaluated our method on the MICCAI 2021 QUBIQ challenge, showing that it performs well across multiple medical image segmentation tasks, produces well-calibrated predictions, and, on average, performs better at matching physicians' predictions than other physicians.
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
Venue: arXiv cs.CL 2021
Year: 2021

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node's graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://github.com/kellymarchisio/euc-v-graph-bli.
Linear Policies are Sufficient to Realize Robust Bipedal Walking on Challenging Terrains
Venue: arXiv cs.RO 2021
Year: 2021

In this work, we demonstrate robust walking in the bipedal robot Digit on uneven terrains by just learning a single linear policy. In particular, we propose a new control pipeline, wherein the high-level trajectory modulator shapes the end-foot ellipsoidal trajectories, and the low-level gait controller regulates the torso and ankle orientation. The foot-trajectory modulator uses a linear policy and the regulator uses a linear PD control law. As opposed to neural network-based policies, the proposed linear policy has only 13 learnable parameters, thereby not only guaranteeing sample efficient learning but also enabling simplicity and interpretability of the policy. This is achieved with no loss of performance on challenging terrains like slopes, stairs and outdoor landscapes. We first demonstrate robust walking in the custom simulation environment, MuJoCo, and then directly transfer to hardware with no modification of the control pipeline. We subject the biped to a series of pushes and terrain height changes, both indoors and outdoors, thereby validating the presented work.
Dynamic Risk Assessment for Geologic CO2 Sequestration
Venue: arXiv cs.IT 2021
Year: 2021

At a geologic CO2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO2 saturations and pressures in potentially leaky wellbores, CO2/brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the NRAP-Open-IAM risk assessment tool coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.
Automated Multi-Process CTC Detection using Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor prognosis. However, the process of identification and later enumeration of CTCs require manual labor, which is error-prone and time-consuming. The recent developments in object detection via Deep Learning using Mask-RCNNs and wider availability of pre-trained models have enabled sensitive tasks with limited data of such to be tackled with unprecedented accuracy. In this report, we present a novel 3-stage detection model for automated identification of Circulating Tumor Cells in multi-channel darkfield microscopic images comprised of: RetinaNet based identification of Cytokeratin (CK) stains, Mask-RCNN based cell detection of DAPI cell nuclei and Otsu thresholding to detect CD-45s. The training dataset is composed of 46 high variance data points, with 10 Negative and 36 Positive data points. The test set is composed of 420 negative data points. The final accuracy of the pipeline is 98.81%.
Investigations on Automorphism Groups of Quantum Stabilizer Codes
Venue: arXiv cs.IT 2021
Year: 2021

The stabilizer formalism for quantum error-correcting codes has been, without doubt, the most successful at producing examples of quantum codes with strong error-correcting properties. In this paper, we discuss strong automorphism groups of stabilizer codes, beginning with the analogous notion from the theory of classical codes. Two weakenings of this concept, the weak automorphism group and Clifford-twisted automorphism group, are also discussed, along with many examples highlighting the possible relationships between the types of "automorphism groups". In particular, we construct an example of a $[[10,0,4]]$ stabilizer code showing how the Clifford-twisted automorphism groups might be connected to the Mathieu groups. Finally, nonexistence results are proved regarding stabilizer codes with highly transitive strong and weak automorphism groups, suggesting a potential inverse relationship between the error-correcting properties of a quantum code and the transitivity of those automorphism groups.
Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning
Venue: arXiv cs.CL 2021
Year: 2021

Scientific literature is one of the most significant resources for sharing knowledge. Researchers turn to scientific literature as a first step in designing an experiment. Given the extensive and growing volume of literature, the common approach of reading and manually extracting knowledge is too time consuming, creating a bottleneck in the research cycle. This challenge spans nearly every scientific domain. For the materials science, experimental data distributed across millions of publications are extremely helpful for predicting materials properties and the design of novel materials. However, only recently researchers have explored computational approaches for knowledge extraction primarily for inorganic materials. This study aims to explore knowledge extraction for organic materials. We built a research dataset composed of 855 annotated and 708,376 unannotated sentences drawn from 92,667 abstracts. We used named-entity-recognition (NER) with BiLSTM-CNN-CRF deep learning model to automatically extract key knowledge from literature. Early-phase results show a high potential for automated knowledge extraction. The paper presents our findings and a framework for supervised knowledge extraction that can be adapted to other scientific domains.
Self-Replicating Neural Programs
Venue: arXiv cs.NE 2021
Year: 2021

In this work, a neural network is trained to replicate the code that trains it using only its own output as input. A paradigm for evolutionary self-replication in neural programs is introduced, where program parameters are mutated, and the ability for the program to more efficiently train itself leads to greater reproductive success. This evolutionary paradigm is demonstrated to produce more efficient learning in organisms from a setting without any explicit guidance, solely based on natural selection favoring organisms with faster reproductive maturity.
Emotional Speech Synthesis for Companion Robot to Imitate Professional Caregiver Speech
Venue: arXiv cs.RO 2021
Year: 2021

When people try to influence others to do something, they subconsciously adjust their speech to include appropriate emotional information. In order for a robot to influence people in the same way, the robot should be able to imitate the range of human emotions when speaking. To achieve this, we propose a speech synthesis method for imitating the emotional states in human speech. In contrast to previous methods, the advantage of our method is that it requires less manual effort to adjust the emotion of the synthesized speech. Our synthesizer receives an emotion vector to characterize the emotion of synthesized speech. The vector is automatically obtained from human utterances by using a speech emotion recognizer. We evaluated our method in a scenario when a robot tries to regulate an elderly person's circadian rhythm by speaking to the person using appropriate emotional states. For the target speech to imitate, we collected utterances from professional caregivers when they speak to elderly people at different times of the day. Then we conducted a subjective evaluation where the elderly participants listened to the speech samples generated by our method. The results showed that listening to the samples made the participants feel more active in the early morning and calmer in the middle of the night. This suggests that the robot may be able to adjust the participants' circadian rhythm and that the robot can potentially exert influence similarly to a person.
Distributionally Robust Multi-Output Regression Ranking
Venue: arXiv cs.LG 2021
Year: 2021

Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introduce a new listwise LTR model called Distributionally Robust Multi-output Regression Ranking (DRMRR). Different from existing methods, the scoring function of DRMRR was designed as a multivariate mapping from a feature vector to a vector of deviation scores, which captures local context information and cross-document interactions. DRMRR uses a Distributionally Robust Optimization (DRO) framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution defined by a Wasserstein ball. We show that this is equivalent to a regularized regression problem with a matrix norm regularizer. Our experiments were conducted on two real-world applications, medical document retrieval, and drug response prediction, showing that DRMRR notably outperforms state-of-the-art LTR models. We also conducted a comprehensive analysis to assess the resilience of DRMRR against various types of noise: Gaussian noise, adversarial perturbations, and label poisoning. We show that DRMRR is not only able to achieve significantly better performance than other baselines, but it can maintain a relatively stable performance as more noise is added to the data.
Neighbor Discovery for VANET with Gossip Mechanism and Multi-packet Reception
Venue: arXiv cs.NI 2021
Year: 2021

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicular ad hoc network (VANET). However, the convergence efficiency of ND is facing the requirements of multi-vehicle fast networking of VANET with frequent topology changes. This paper proposes gossip-based information dissemination and sensing information assisted ND with MPR (GSIM-ND) algorithm for VANET. GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multi-packet reception (MPR). Besides, through multi-target detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered is derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this paper can always reach the short-term convergence quickly. Moreover, GSIM-ND algorithm is more efficient and stable compared with completely random algorithm (CRA), scan-based algorithm (SBA) and gossip-based algorithm. The convergence time of GSIM-ND algorithm is 40\%-90\% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.
How does fake news spread? Understanding pathways of disinformation spread through APIs
Venue: arXiv cs.CY 2021
Year: 2021

What are the pathways for spreading disinformation on social media platforms? This article addresses this question by collecting, categorising, and situating an extensive body of research on how application programming interfaces (APIs) provided by social media platforms facilitate the spread of disinformation. We first examine the landscape of official social media APIs, then perform quantitative research on the open-source code repositories GitHub and GitLab to understand the usage patterns of these APIs. By inspecting the code repositories, we classify developers' usage of the APIs as official and unofficial, and further develop a four-stage framework characterising pathways for spreading disinformation on social media platforms. We further highlight how the stages in the framework were activated during the 2016 US Presidential Elections, before providing policy recommendations for issues relating to access to APIs, algorithmic content, advertisements, and suggest rapid response to coordinate campaigns, development of collaborative, and participatory approaches as well as government stewardship in the regulation of social media platforms.
MFAQ: a Multilingual FAQ Dataset
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset. Our experiments reveal that a multilingual model based on XLM-RoBERTa achieves the best results, except for English. Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones. Our qualitative analysis reveals the brittleness of the model on simple word changes. We publicly release our dataset, model and training script.