OpenRaiser/Intern-Atlas huggingface dataset:
CBP: Coordinated management of cache partitioning, bandwidth partitioning and prefetch throttling
Venue: arXiv cs.AR 2021
Year: 2021

Reducing the average memory access time is crucial for improving the performance of applications running on multi-core architectures. With workload consolidation this becomes increasingly challenging due to shared resource contention. Techniques for partitioning of shared resources - cache and bandwidth - and prefetching throttling have been proposed to mitigate contention and reduce the average memory access time. However, existing proposals only employ a single or a subset of these techniques and are therefore not able to exploit the full potential of coordinated management of cache, bandwidth and prefetching. Our characterization results show that application performance, in several cases, is sensitive to prefetching, cache and bandwidth allocation. Furthermore, the results show that managing these together provides higher performance potential during workload consolidation as it enables more resource trade-offs. In this paper, we propose CBP a coordination mechanism for dynamically managing prefetching throttling, cache and bandwidth partitioning, in order to reduce average memory access time and improve performance. CBP works by employing individual resource managers to determine the appropriate setting for each resource and a coordinating mechanism in order to enable inter-resource trade-offs. Our evaluation on a 16-core CMP shows that CBP, on average, improves performance by 11% compared to the state-of-the-art technique that manages cache partitioning and prefetching and by 50% compared to the baseline without cache partitioning, bandwidth partitioning and prefetch throttling.
Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches
Venue: arXiv cs.CY 2021
Year: 2021

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios due to the constraints of expensive data transmission and data privacy protection. Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation. In this paper, we solve this problem from the perspective of noisy label learning, since the given pre-trained model can pre-generate noisy label for unlabeled target data via directly network inference. Under this problem modeling, incorporating self-supervised learning, we propose a novel Self-Supervised Noisy Label Learning method, which can effectively fine-tune the pre-trained model with pre-generated label as well as selfgenerated label on the fly. Extensive experiments had been conducted to validate its effectiveness. Our method can easily achieve state-of-the-art results and surpass other methods by a very large margin. Code will be released.
A large, crowdsourced evaluation of gesture generation systems on common data: The GENEA Challenge 2020
Venue: arXiv cs.HC 2021
Year: 2021

Co-speech gestures, gestures that accompany speech, play an important role in human communication. Automatic co-speech gesture generation is thus a key enabling technology for embodied conversational agents (ECAs), since humans expect ECAs to be capable of multi-modal communication. Research into gesture generation is rapidly gravitating towards data-driven methods. Unfortunately, individual research efforts in the field are difficult to compare: there are no established benchmarks, and each study tends to use its own dataset, motion visualisation, and evaluation methodology. To address this situation, we launched the GENEA Challenge, a gesture-generation challenge wherein participating teams built automatic gesture-generation systems on a common dataset, and the resulting systems were evaluated in parallel in a large, crowdsourced user study using the same motion-rendering pipeline. Since differences in evaluation outcomes between systems now are solely attributable to differences between the motion-generation methods, this enables benchmarking recent approaches against one another in order to get a better impression of the state of the art in the field. This paper reports on the purpose, design, results, and implications of our challenge.
Decision Power of Weak Asynchronous Models of Distributed Computing
Venue: arXiv cs.FL 2021
Year: 2021

Esparza and Reiter have recently conducted a systematic comparative study of models of distributed computing consisting of a network of identical finite-state automata that cooperate to decide if the underlying graph of the network satisfies a given property. The study classifies models according to four criteria, and shows that twenty initially possible combinations collapse into seven equivalence classes with respect to their decision power, i.e. the properties that the automata of each class can decide. However, Esparza and Reiter only show (proper) inclusions between the classes, and so do not characterise their decision power. In this paper we do so for labelling properties, i.e. properties that depend only on the labels of the nodes, but not on the structure of the graph. In particular, majority (whether more nodes carry label $a$ than $b$) is a labelling property. Our results show that only one of the seven equivalence classes identified by Esparza and Reiter can decide majority for arbitrary networks. We then study the expressive power of the classes on bounded-degree networks, and show that three classes can. In particular, we present an algorithm for majority that works for all bounded-degree networks under adversarial schedulers, i.e. even if the scheduler must only satisfy that every node makes a move infinitely often, and prove that no such algorithm can work for arbitrary networks.
The Channel Between Randomly Oriented Dipoles: Statistics and Outage in the Near and Far Field
Venue: arXiv cs.IT 2021
Year: 2021

We consider the class of wireless links whose propagation characteristics are described by a dipole model. This comprises free-space links between dipole antennas and magneto-inductive links between coils, with important communication and power transfer applications. A dipole model describes the channel coefficient as a function of link distance and antenna orientations. In many use cases the orientations are random, causing a random fading channel. This paper presents a closed-form description of the channel statistics and the resulting outage performance for the case of i.i.d. uniformly distributed antenna orientations in 3D space. For reception in AWGN after active transmission, we show that the high-SNR outage probability scales like $p_\mathrm{e} \propto \mathrm{SNR}^{-1/2}$ in the near- or far-field region, i.e. the diversity exponent is just 1/2 (even 1/4 with backscatter or load modulation). The diversity exponent improves to 1 in the near-far-field transition due to polarization diversity. Analogous statements are made for the power transfer efficiency and outage capacity.
Uncertainty-aware Generalized Adaptive CycleGAN
Venue: arXiv cs.CV 2021
Year: 2021

Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner. Existing methods often learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty, leading to performance degradation when encountering unseen out-of-distribution (OOD) patterns at test time. To address this limitation, we propose a novel probabilistic method called Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC), which models the per-pixel residual by generalized Gaussian distribution, capable of modelling heavy-tailed distributions. We compare our model with a wide variety of state-of-the-art methods on two challenging tasks: unpaired image denoising in the natural image and unpaired modality prorogation in medical image domains. Experimental results demonstrate that our model offers superior image generation quality compared to recent methods in terms of quantitative metrics such as signal-to-noise ratio and structural similarity. Our model also exhibits stronger robustness towards OOD test data.
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Venue: arXiv cs.LG 2021
Year: 2021

Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and in practice, these are obtained by MCMC algorithms such as Langevin dynamics. However, since MCMC in high-dimensional space converges extremely slowly, the current understanding of maximum likelihood training, which assumes approximate samples from the model can be drawn, is problematic. In this paper, we try to understand this training procedure by replacing Langevin dynamics with deterministic solutions of the associated gradient descent ODE. Doing so allows us to study the density induced by the dynamics (if the dynamics are invertible), and connect with GANs by treating the dynamics as generator models, the initial values as latent variables and the loss as optimizing a critic defined by the very same energy that determines the generator through its gradient. Hence the term - self-adversarial loss. We show that reintroducing the noise in the dynamics does not lead to a qualitative change in the behavior, and merely reduces the quality of the generator. We thus show that EBM training is effectively a self-adversarial procedure rather than maximum likelihood estimation.
SpotCheck: On-Device Anomaly Detection for Android
Venue: arXiv cs.CR 2021
Year: 2021

In recent years the PC has been replaced by mobile devices for many security sensitive operations, both from a privacy and a financial standpoint. While security mechanisms are deployed at various levels, these are frequently put under strain by previously unseen malware. An additional protection layer capable of novelty detection is therefore needed. In this work we propose SpotCheck, an anomaly detector intended to run on Android devices. It samples app executions and submits suspicious apps to more thorough processing by malware sandboxes. We compare Kernel Principal Component Analysis (KPCA) and Variational Autoencoders (VAE) on app execution representations based on the well-known system call traces, as well as a novel approach based on memory dumps. Results show that when using VAE, SpotCheck attains a level of effectiveness comparable to what has been previously achieved for network anomaly detection. Interestingly this is also true for the memory dump approach, relinquishing the need for continuous app monitoring.
Conditional Lower Bounds for Variants of Dynamic LIS
Venue: arXiv cs.DS 2021
Year: 2021

In this note, we consider the complexity of maintaining the longest increasing subsequence (LIS) of an array under (i) inserting an element, and (ii) deleting an element of an array. We show that no algorithm can support queries and updates in time $\mathcal{O}(n^{1/2-ε})$ and $\mathcal{O}(n^{1/3-ε})$ for the dynamic LIS problem, for any constant $ε>0$, when the elements are weighted or the algorithm supports 1D-queries (on subarrays), respectively, assuming the All-Pairs Shortest Paths (APSP) conjecture or the Online Boolean Matrix-Vector Multiplication (OMv) conjecture. The main idea in our construction comes from the work of Abboud and Dahlgaard [FOCS 2016], who proved conditional lower bounds for dynamic planar graph algorithm. However, this needs to be appropriately adjusted and translated to obtain an instance of the dynamic LIS problem.
Page Layout Analysis System for Unconstrained Historic Documents
Venue: arXiv cs.CV 2021
Year: 2021

Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription. We propose extending a CNN-based text baseline detection system by adding line height and text block boundary predictions to the model output, allowing the system to extract more comprehensive layout information. We also show that pixel-wise text orientation prediction can be used for processing documents with multiple text orientations. We demonstrate that the proposed method performs well on the cBAD baseline detection dataset. Additionally, we benchmark the method on newly introduced PERO layout dataset which we also make public.
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still remain. Recently, methods using generative models to combat bias towards classes seen during training have pushed the state of the art of ZSL, but these generative models can be slow or computationally expensive to train. Additionally, while many previous ZSL methods assume a one-time adaptation to unseen classes, in reality, the world is always changing, necessitating a constant adjustment for deployed models. Models unprepared to handle a sequential stream of data are likely to experience catastrophic forgetting. We propose a meta-continual zero-shot learning (MCZSL) approach to address both these issues. In particular, by pairing self-gating of attributes and scaled class normalization with meta-learning based training, we are able to outperform state-of-the-art results while being able to train our models substantially faster ($>100\times$) than expensive generative-based approaches. We demonstrate this by performing experiments on five standard ZSL datasets (CUB, aPY, AWA1, AWA2 and SUN) in both generalized zero-shot learning and generalized continual zero-shot learning settings.
STEP: Segmenting and Tracking Every Pixel
Venue: arXiv cs.CV 2021
Year: 2021

The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-world setting requiring dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. To overcome this, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP. The datasets contain long video sequences, providing challenging examples and a test-bed for studying long-term pixel-precise segmentation and tracking under real-world conditions. We further propose a novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly balances semantic and tracking aspects of this task and is more appropriate for evaluating sequences of arbitrary length. Finally, we provide several baselines to evaluate the status of existing methods on this new challenging dataset. We have made our datasets, metric, benchmark servers, and baselines publicly available, and hope this will inspire future research.
Generative Modelling of BRDF Textures from Flash Images
Venue: arXiv cs.GR 2021
Year: 2021

We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters (diffuse albedo, normals, roughness, specular albedo) that subsequently allows rendering in complex scenes and illuminations, matching the appearance of the input photograph. Technically, we jointly embed all flash images into a latent space using a convolutional encoder, and -- conditioned on these latent codes -- convert random spatial fields into fields of BRDF parameters using a convolutional neural network (CNN). We condition these BRDF parameters to match the visual characteristics (statistics and spectra of visual features) of the input under matching light. A user study compares our approach favorably to previous work, even those with access to BRDF supervision.
Investigating Moral Foundations from Web Trending Topics
Venue: arXiv cs.SI 2021
Year: 2021

Moral foundations theory helps understand differences in morality across cultures. In this paper, we propose a model to predict moral foundations (MF) from social media trending topics. We also investigate whether differences in MF influence emotional traits. Our results are promising and leave room for future research avenues.
Feature Importance Explanations for Temporal Black-Box Models
Venue: arXiv cs.LG 2021
Year: 2021

Models in the supervised learning framework may capture rich and complex representations over the features that are hard for humans to interpret. Existing methods to explain such models are often specific to architectures and data where the features do not have a time-varying component. In this work, we propose TIME, a method to explain models that are inherently temporal in nature. Our approach (i) uses a model-agnostic permutation-based approach to analyze global feature importance, (ii) identifies the importance of salient features with respect to their temporal ordering as well as localized windows of influence, and (iii) uses hypothesis testing to provide statistical rigor.
The SpaceNet Multi-Temporal Urban Development Challenge
Venue: arXiv cs.CV 2021
Year: 2021

Building footprints provide a useful proxy for a great many humanitarian applications. For example, building footprints are useful for high fidelity population estimates, and quantifying population statistics is fundamental to ~1/4 of the United Nations Sustainable Development Goals Indicators. In this paper we (the SpaceNet Partners) discuss efforts to develop techniques for precise building footprint localization, tracking, and change detection via the SpaceNet Multi-Temporal Urban Development Challenge (also known as SpaceNet 7). In this NeurIPS 2020 competition, participants were asked identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centered around a brand new open source dataset of Planet Labs satellite imagery mosaics at 4m resolution, which includes 24 images (one per month) covering ~100 unique geographies. Tracking individual buildings at this resolution is quite challenging, yet the winning participants demonstrated impressive performance with the newly developed SpaceNet Change and Object Tracking (SCOT) metric. This paper details the top-5 winning approaches, as well as analysis of results that yielded a handful of interesting anecdotes such as decreasing performance with latitude.
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
Venue: arXiv cs.AI 2021
Year: 2021

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse and mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper we analyse a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognised until now. Finally, our design patterns extend and refine Kautz' earlier attempt at categorising neuro-symbolic architectures.
Being correct is not enough: efficient verification using robust linear temporal logic
Venue: arXiv cs.LO 2021
Year: 2021

While most approaches in formal methods address system correctness, ensuring robustness has remained a challenge. In this paper we present and study the logic rLTL which provides a means to formally reason about both correctness and robustness in system design. Furthermore, we identify a large fragment of rLTL for which the verification problem can be efficiently solved, i.e., verification can be done by using an automaton, recognizing the behaviors described by the rLTL formula $φ$, of size at most $\mathcal{O} \left( 3^{ |φ|} \right)$, where $|φ|$ is the length of $φ$. This result improves upon the previously known bound of $\mathcal{O}\left(5^{|φ|} \right)$ for rLTL verification and is closer to the LTL bound of $\mathcal{O}\left( 2^{|φ|} \right)$. The usefulness of this fragment is demonstrated by a number of case studies showing its practical significance in terms of expressiveness, the ability to describe robustness, and the fine-grained information that rLTL brings to the process of system verification. Moreover, these advantages come at a low computational overhead with respect to LTL verification.
Characterization and recognition of handwritten digits using Julia
Venue: arXiv cs.CV 2021
Year: 2021

Automatic image and digit recognition is a computationally challenging task for image processing and pattern recognition, requiring an adequate appreciation of the syntactic and semantic importance of the image for the identification ofthe handwritten digits. Image and Pattern Recognition has been identified as one of the driving forces in the research areas because of its shifting of different types of applications, such as safety frameworks, clinical frameworks, diversion, and so on.In this study, for recognition, we implemented a hybrid neural network model that is capable of recognizing the digit of MNISTdataset and achieved a remarkable result. The proposed neural model network can extract features from the image and recognize the features in the layer by layer. To expand, it is so important for the neural network to recognize how the proposed modelcan work in each layer, how it can generate output, and so on. Besides, it also can recognize the auto-encoding system and the variational auto-encoding system of the MNIST dataset. This study will explore those issues that are discussed above, and the explanation for them, and how this phenomenon can be overcome.