OpenRaiser/Intern-Atlas huggingface dataset:
Reverse Engineering Code Dependencies: Converting Integer-Based Variability to Propositional Logic
Venue: arXiv cs.SE 2021
Year: 2021

A number of SAT-based analysis concepts and tools for software product lines exist, that extract code dependencies in propositional logic from the source code assets of the product line. On these extracted conditions, SAT-solvers are used to reason about the variability. However, in practice, a lot of software product lines use integer-based variability. The variability variables hold integer values, and integer operators are used in the conditions. Most existing analysis tools can not handle this kind of variability; they expect pure Boolean conditions. This paper introduces an approach to convert integer-based variability conditions to propositional logic. Running this approach as a preparation on an integer-based product line allows the existing SAT-based analyses to work without any modifications. The pure Boolean formulas, that our approach builds as a replacement for the integer-based conditions, are mostly equivalent to the original conditions with respect to satisfiability. Our approach was motivated by and implemented in the context of a real-world industrial case-study, where such a preparation was necessary to analyze the variability. Our contribution is an approach to convert conditions, that use integer variables, into propositional formulas, to enable easy usage of SAT-solvers on the result. It works well on restricted variables (i.e. variables with a small range of allowed values); unrestricted integer variables are handled less exact, but still retain useful variability information.
Fourier-based Video Prediction through Relational Object Motion
Venue: arXiv cs.CV 2021
Year: 2021

The ability to predict future outcomes conditioned on observed video frames is crucial for intelligent decision-making in autonomous systems. Recently, deep recurrent architectures have been applied to the task of video prediction. However, this often results in blurry predictions and requires tedious training on large datasets. Here, we explore a different approach by (1) using frequency-domain approaches for video prediction and (2) explicitly inferring object-motion relationships in the observed scene. The resulting predictions are consistent with the observed dynamics in a scene and do not suffer from blur.
Monocular Depth Estimation with Sharp Boundary
Venue: arXiv cs.CV 2021
Year: 2021

Monocular depth estimation is the base task in computer vision. It has a tremendous development in the decade with the development of deep learning. But the boundary blur of the depth map is still a serious problem. Research finds the boundary blur problem is mainly caused by two factors, first, the low-level features containing boundary and structure information may loss in deeper networks during the convolution process., second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary in the whole areas during the backpropagation. In order to mitigate the boundary blur problem, we focus on the above two impact factors. Firstly, we design a scene understanding module to learn the global information with low- and high-level features, and then to transform the global information to different scales with our proposed scale transform module according to the different phases in the decoder. Secondly, we propose a boundary-aware depth loss function to pay attention to the effects of the boundary's depth value. The extensive experiments show that our method can predict the depth maps with clearer boundaries, and the performance of the depth accuracy base on NYU-depth v2 and SUN RGB-D is competitive.
MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification
Venue: arXiv cs.CV 2021
Year: 2021

As a challenging task, unsupervised person ReID aims to match the same identity with query images which does not require any labeled information. In general, most existing approaches focus on the visual cues only, leaving potentially valuable auxiliary metadata information (e.g., spatio-temporal context) unexplored. In the real world, such metadata is normally available alongside captured images, and thus plays an important role in separating several hard ReID matches. With this motivation in mind, we propose~\textbf{MGH}, a novel unsupervised person ReID approach that uses meta information to construct a hypergraph for feature learning and label refinement. In principle, the hypergraph is composed of camera-topology-aware hyperedges, which can model the heterogeneous data correlations across cameras. Taking advantage of label propagation on the hypergraph, the proposed approach is able to effectively refine the ReID results, such as correcting the wrong labels or smoothing the noisy labels. Given the refined results, We further present a memory-based listwise loss to directly optimize the average precision in an approximate manner. Extensive experiments on three benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.
Fast Static Analyses of Software Product Lines -- An Example With More Than 42,000 Metrics
Venue: arXiv cs.SE 2021
Year: 2021

Context: Software metrics, as one form of static analyses, is a commonly used approach in software engineering in order to understand the state of a software system, in particular to identify potential areas prone to defects. Family-based techniques extract variability information from code artifacts in Software Product Lines (SPLs) to perform static analysis for all available variants. Many different types of metrics with numerous variants have been defined in literature. When counting all metrics including such variants, easily thousands of metrics can be defined. Computing all of them for large product lines can be an extremely expensive process in terms of performance and resource consumption. Objective: We address these performance and resource challenges while supporting customizable metric suites, which allow running both, single system and variability-aware code metrics. Method: In this paper, we introduce a partial parsing approach used for the efficient measurement of more than 42,000 code metric variations. The approach covers variability information and restricts parsing to the relevant parts of the Abstract Syntax Tree (AST). Conclusions: This partial parsing approach is designed to cover all relevant information to compute a broad variety of variability-aware code metrics on code artifacts containing annotation-based variability, e.g., realized with C-preprocessor statements. It allows for the flexible combination of single system and variability-aware metrics, which is not supported by existing tools. This is achieved by a novel representation of partially parsed product line code artifacts, which is tailored to the computation of the metrics. Our approach consumes considerably less resources, especially when computing many metric variants in parallel.
Bridging the Band Gap: What Device Physicists Need to Know About Machine Learning
Venue: arXiv cs.ET 2021
Year: 2021

This article surveys the landscape of semiconductor materials and devices research for the acceleration of machine learning (ML) algorithms. We observe a disconnect between the semiconductor and device physics and engineering communities, and the digital logic and computer hardware architecture communities. The article first provides an overview of the principles of computational complexity and fundamental physical limits to computing and their relation to physical systems. The article then provides an introduction to ML by presenting three key components of ML systems: representation, evaluation, and optimisation. The article then discusses and provides examples of the application of emerging technologies from the demiconductor and device physics domains as solutions to computational problems, alongside a brief overview of emerging devices for computing applications. The article then reviews the landscape of ML accelerators, comparing fixed-function and reprogrammable digital logic with novel devices such as memristors, resistive memories, magnetic memories, and probabilistic bits. We observe broadly lower performance of ML accelerators based on novel devices and materials when compared to those based on digital complimentary metal-oxide semiconductor (CMOS) technology, particularly in the MNIST optical character recognition task, a common ML benchmark, and also highlight the lack of a trend of progress in approaches based on novel materials and devices. Lastly, the article proposes figures of merit for meaningful evaluation and comparison of different ML implementations in the hope of fostering a dialogue between the materials science, device physics, digital logic, and computer architecture communities by providing a common frame of reference for their work.
Does it matter who pays back Technical Debt? An empirical study of self-fixed TD
Venue: arXiv cs.SE 2021
Year: 2021

Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the corresponding TD issues. Objective: The goal of our study is to investigate self-fixed technical debt, especially the extent in which TD is self-fixed, which types of TD are more likely to be self-fixed, whether the remediation time of self-fixed TD is shorter than non-self-fixed TD and how development behaviors are related to self-fixed TD. Method: We report on an empirical study that analyzes the self-fixed issues of five types of TD (i.e., Code, Defect, Design, Documentation and Test), captured via static analysis, in more than 44,000 commits obtained from 20 Python and 16 Java projects of the Apache Software Foundation. Results: The results show that about half of the fixed issues are self-fixed and that the likelihood of contained TD issues being self-fixed is negatively correlated with project size, the number of developers and total issues. Moreover, there is no significant difference of the survival time between self-fixed and non-self-fixed issues. Furthermore, developers are more keen to pay back their own TD when it is related to lower code level issues, e.g., Defect Debt and Code Debt. Finally, developers who are more dedicated to or knowledgeable about the project contribute to a higher chance of self-fixing TD. Conclusions: These results can benefit both researchers and practitioners by aiding the prioritization of TD remediation activities and refining strategies within development teams, and by informing the development of TD management tools.
C3PU: Cross-Coupling Capacitor Processing Unit Using Analog-Mixed Signal In-Memory Computing for AI Inference
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents a novel cross-coupling capacitor processing unit (C3PU) that supports analog-mixed signal in memory computing to perform multiply-and-accumulate (MAC) operations. The C3PU consists of a capacitive unit, a CMOS transistor, and a voltage-to-time converter (VTC). The capacitive unit serves as a computational element that holds the multiplier operand and performs multiplication once the multiplicand is applied at the terminal. The multiplicand is the input voltage that is converted to a pulse width signal using a low power VTC. The transistor transfers this multiplication where a voltage level is generated. A demonstrator of 5x4 C3PU array that is capable of implementing 4 MAC units is presented. The design has been verified using Monte Carlo simulation in 65 nm technology. The 5x4 C3PU consumed energy of 66.4 fJ/MAC at 0.3 V voltage supply with an error of 5.7%. The proposed unit achieves lower energy and occupies a smaller area by 3.4x and 3.6x, respectively, with similar error value when compared to a digital-based 8x4-bit fixed point MAC unit. The C3PU has been utilized through an iris fower classification utilizing an artificial neural network which achieved a 90% classification accuracy compared to ideal accuracy of 96.67% using MATLAB.
Gated Information Bottleneck for Generalization in Sequential Environments
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution (OOD) detection. Meanwhile, we also establish the connection between IB theory and invariant causal representation learning, and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization (IRM) fails. Code of GIB is available at https://github.com/falesiani/GIB
Photonic Networks-on-Chip Employing Multilevel Signaling: A Cross-Layer Comparative Study
Venue: arXiv cs.ET 2021
Year: 2021

Photonic network-on-chip (PNoC) architectures employ photonic links with dense wavelength-division multiplexing (DWDM) to enable high throughput on-chip transfers. Unfortunately, increasing the DWDM degree (i.e., using a larger number of wavelengths) to achieve higher aggregated datarate in photonic links, and hence higher throughput in PNoCs, requires sophisticated and costly laser sources along with extra photonic hardware. This extra hardware can introduce undesired noise to the photonic link and increase the bit-error-rate (BER), power, and area consumption of PNoCs. To mitigate these issues, the use of 4-pulse amplitude modulation (4-PAM) signaling, instead of the conventional on-off keying (OOK) signaling, can halve the wavelength signals utilized in photonic links for achieving the target aggregate datarate while reducing the overhead of crosstalk noise, BER, and photonic hardware. There are various designs of 4- PAM modulators reported in the literature. For example, the signal superposition (SS), electrical digital-to-analog converter (EDAC), and optical digital-to-analog converter (ODAC) based designs of 4-PAM modulators have been reports. However, it is yet to be explored how these SS, EDAC, and ODAC based 4-PAM modulators can be utilized to design DWDM-based photonic links and PNoC architectures. In this paper, we provide an extensive link-level and system-level of the SS, EDAC, and ODAC types of 4-PAM modulators from prior work with regards to their applicability and utilization overheads. From our link-level and PNoC-level evaluation, we have observed that the 4-PAM EDAC based variants of photonic links and PNoCs exhibit better performance and energy-efficiency compared to the OOK, 4-PAM SS, and 4-PAM ODAC based links and PNoCs.
Finding Relevant Points for Nearest-Neighbor Classification
Venue: arXiv cs.DS 2021
Year: 2021

In nearest-neighbor classification problems, a set of $d$-dimensional training points are given, each with a known classification, and are used to infer unknown classifications of other points by using the same classification as the nearest training point. A training point is relevant if its omission from the training set would change the outcome of some of these inferences. We provide a simple algorithm for thinning a training set down to its subset of relevant points, using as subroutines algorithms for finding the minimum spanning tree of a set of points and for finding the extreme points (convex hull vertices) of a set of points. The time bounds for our algorithm, in any constant dimension $d\ge 3$, improve on a previous algorithm for the same problem by Clarkson (FOCS 1994).
Planning Sensing Sequences for Subsurface 3D Tumor Mapping
Venue: arXiv cs.RO 2021
Year: 2021

Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able to sense and map subsurface anatomy, such as tumors, efficiently and accurately. In this work, we present a method that plans a sequence of sensing actions to map the 3D geometry of subsurface tumors. We leverage a sequential Bayesian Hilbert map to create a 3D probabilistic occupancy model that represents the likelihood that any given point in the anatomy is occupied by a tumor, conditioned on sensor readings. We iteratively update the map, utilizing Bayesian optimization to determine sensing poses that explore unsensed regions of anatomy and exploit the knowledge gained by previous sensing actions. We demonstrate our method's efficiency and accuracy in three anatomical scenarios including a liver tumor scenario generated from a real patient's CT scan. The results show that our proposed method significantly outperforms comparison methods in terms of efficiency while detecting subsurface tumors with high accuracy.
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI
Venue: arXiv cs.CL 2021
Year: 2021

Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot learning setup and examine the effects of natural language explanations on OOD generalization. We leverage the templates in the HANS dataset and construct templated natural language explanations for each template. Although generated explanations show competitive BLEU scores against groundtruth explanations, they fail to improve prediction performance. We further show that generated explanations often hallucinate information and miss key elements that indicate the label.
Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning
Venue: arXiv cs.CV 2021
Year: 2021

Lesion images are frequently taken in open-set settings. Because of this, the image data generated is extremely varied in nature.It is difficult for a convolutional neural network to find proper features and generalise well, as a result content based image retrieval (CBIR) system for lesion images are difficult to build. This paper explores this domain and proposes multiple similarity measures which uses Style Loss and Dice Coefficient via a novel similarity measure called I1-Score. Out of the CBIR similarity measures proposed, pure style loss approach achieves a remarkable accuracy increase over traditional approaches like Euclidean Distance and Cosine Similarity. The I1-Scores using style loss performed better than traditional approaches by a small margin, whereas, I1-Scores with dice-coefficient faired very poorly. The model used is trained using ensemble learning for better generalization.
The VLSAT-2 Benchmark Suite
Venue: arXiv cs.DS 2021
Year: 2021

This report presents VLSAT-2 (an acronym for "Very Large Boolean SATisfiability problems),the second part of a benchmark suite to be used in scientific experiments and softwarecompetitions addressing SAT-solving issues.VLSAT-2 contains 100 benchmarks (50 satisfiable and 50 unsatisfiable formulas)of increasing complexity, proposed in DIMACS CNF format undera permissive Creative Commons license.25% of these benchmarks have been used during the 2020 and 2021 editionsof the International SAT Competition.
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication
Venue: arXiv cs.RO 2021
Year: 2021

Multi-agent mapping is a fundamentally important capability for autonomous robot task coordination and execution in complex environments. While successful algorithms have been proposed for mapping using individual platforms, cooperative online mapping for teams of robots remains largely a challenge. We focus on probabilistic variants of mapping due to its potential utility in downstream tasks such as uncertainty-aware path-planning. A critical question to enabling this capability is how to process and aggregate incrementally observed local information among individual platforms, especially when their ability to communicate is intermittent. We put forth an Incremental Sparse Gaussian Process (GP) methodology for multi-robot mapping, where the regression is over a truncated signed-distance field (TSDF). Doing so permits each robot in the network to track a local estimate of a pseudo-point approximation GP posterior and perform weighted averaging of its parameters with those of its (possibly time-varying) set of neighbors. We establish conditions on the pseudo-point representation, as well as communication protocol, such that robots' local GPs converge to the one with globally aggregated information. We further provide experiments that corroborate our theoretical findings for probabilistic multi-robot mapping.
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants
Venue: arXiv cs.CV 2021
Year: 2021

In multimodal assistant, where vision is also one of the input modalities, the identification of user intent becomes a challenging task as visual input can influence the outcome. Current digital assistants take spoken input and try to determine the user intent from conversational or device context. So, a dataset, which includes visual input (i.e. images or videos for the corresponding questions targeted for multimodal assistant use cases, is not readily available. The research in visual question answering (VQA) and visual question generation (VQG) is a great step forward. However, they do not capture questions that a visually-abled person would ask multimodal assistants. Moreover, many times questions do not seek information from external knowledge. In this paper, we provide a new dataset, MMIU (MultiModal Intent Understanding), that contains questions and corresponding intents provided by human annotators while looking at images. We, then, use this dataset for intent classification task in multimodal digital assistant. We also experiment with various approaches for combining vision and language features including the use of multimodal transformer for classification of image-question pairs into 14 intents. We provide the benchmark results and discuss the role of visual and text features for the intent classification task on our dataset.