OpenRaiser/Intern-Atlas huggingface dataset:
AI Song Contest: Human-AI Co-Creation in Songwriting
Venue: arXiv cs.SD 2020
Year: 2020

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression.
Exploiting Local Optimality in Metaheuristic Search
Venue: arXiv cs.AI 2020
Year: 2020

A variety of strategies have been proposed for overcoming local optimality in metaheuristic search. This paper examines characteristics of moves that can be exploited to make good decisions about steps that lead away from a local optimum and then lead toward a new local optimum. We introduce strategies to identify and take advantage of useful features of solution history with an adaptive memory metaheuristic, to provide rules for selecting moves that offer promise for discovering improved local optima. Our approach uses a new type of adaptive memory based on a construction called exponential extrapolation. The memory operates by means of threshold inequalities that ensure selected moves will not lead to a specified number of most recently encountered local optima. Associated thresholds are embodied in choice rule strategies that further exploit the exponential extrapolation concept. Together these produce a threshold based Alternating Ascent (AA) algorithm that opens a variety of research possibilities for exploration.
Implicit Subspace Prior Learning for Dual-Blind Face Restoration
Venue: arXiv cs.CV 2020
Year: 2020

Face restoration is an inherently ill-posed problem, where additional prior constraints are typically considered crucial for mitigating such pathology. However, real-world image prior are often hard to simulate with precise mathematical models, which inevitably limits the performance and generalization ability of existing prior-regularized restoration methods. In this paper, we study the problem of face restoration under a more practical ``dual blind'' setting, i.e., without prior assumptions or hand-crafted regularization terms on the degradation profile or image contents. To this end, a novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration, with two key elements: 1) an implicit formulation to circumvent the ill-defined restoration mapping and 2) a subspace prior decomposition and fusion mechanism to dynamically handle inputs at varying degradation levels with consistent high-quality restoration results. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods for a variety of restoration subtasks, including a 3.69db PSNR and 45.8% FID gain against ESRGAN, the 2018 NTIRE SR challenge winner. Overall, we prove that it is possible to capture and utilize prior knowledge without explicitly formulating it, which will help inspire new research paradigms towards low-level vision tasks.
Fat Pad Cages for Facial Posing
Venue: arXiv cs.GR 2020
Year: 2020

We introduce Fat Pad cages for posing facial meshes. It combines cage representation and facial anatomical elements, and enables users with no artistic skill to quickly sketch realistic facial expressions. The model relies on one or several cage(s) that deform(s) the mesh following the human fat pads map. We propose a new function to filter Green Coordinates using geodesic distances preventing global deformation while ensuring smooth deformations at the borders. Lips, nostrils and eyelids are processed slightly differently to allow folding up and opening. Cages are automatically created and fit any new unknown facial mesh. To validate our approach, we present a user study comparing our Fat Pad cages to regular Green Coordinates. Results show that Fat Pad cages bring a significant improvement in reproducing existing facial expressions.
Meta-Context Transformers for Domain-Specific Response Generation
Venue: arXiv cs.CL 2020
Year: 2020

Despite the tremendous success of neural dialogue models in recent years, it suffers a lack of relevance, diversity, and some times coherence in generated responses. Lately, transformer-based models, such as GPT-2, have revolutionized the landscape of dialogue generation by capturing the long-range structures through language modeling. Though these models have exhibited excellent language coherence, they often lack relevance and terms when used for domain-specific response generation. In this paper, we present DSRNet (Domain Specific Response Network), a transformer-based model for dialogue response generation by reinforcing domain-specific attributes. In particular, we extract meta attributes from context and infuse them with the context utterances for better attention over domain-specific key terms and relevance. We study the use of DSRNet in a multi-turn multi-interlocutor environment for domain-specific response generation. In our experiments, we evaluate DSRNet on Ubuntu dialogue datasets, which are mainly composed of various technical domain related dialogues for IT domain issue resolutions and also on CamRest676 dataset, which contains restaurant domain conversations. Trained with maximum likelihood objective, our model shows significant improvement over the state-of-the-art for multi-turn dialogue systems supported by better BLEU and semantic similarity (BertScore) scores. Besides, we also observe that the responses produced by our model carry higher relevance due to the presence of domain-specific key attributes that exhibit better overlap with the attributes of the context. Our analysis shows that the performance improvement is mostly due to the infusion of key terms along with dialogues which result in better attention over domain-relevant terms. Other contributing factors include joint modeling of dialogue context with the domain-specific meta attributes and topics.
Growth of Random Trees by Leaf Attachment
Venue: arXiv cs.DS 2020
Year: 2020

We study the growth of a time-ordered rooted tree by probabilistic attachment of new vertices to leaves. We construct a likelihood function of the leaves based on the connectivity of the tree. We take such connectivity to be induced by the merging of directed ordered paths from leaves to the root. Combining the likelihood with an assigned prior distribution leads to a posterior leaf distribution from which we sample attachment points for new vertices. We present computational examples of such Bayesian tree growth. Although the discussion is generic, the initial motivation for the paper is the concept of a distributed ledger, which may be regarded as a time-ordered random tree that grows by probabilistic leaf attachment.
Cardiac Cohort Classification based on Morphologic and Hemodynamic Parameters extracted from 4D PC-MRI Data
Venue: arXiv cs.LG 2020
Year: 2020

An accurate assessment of the cardiovascular system and prediction of cardiovascular diseases (CVDs) are crucial. Measured cardiac blood flow data provide insights about patient-specific hemodynamics, where many specialized techniques have been developed for the visual exploration of such data sets to better understand the influence of morphological and hemodynamic conditions on CVDs. However, there is a lack of machine learning approaches techniques that allow a feature-based classification of heart-healthy people and patients with CVDs. In this work, we investigate the potential of morphological and hemodynamic characteristics, extracted from measured blood flow data in the aorta, for the classification of heart-healthy volunteers and patients with bicuspid aortic valve (BAV). Furthermore, we research if there are characteristic features to classify male and female as well as older heart-healthy volunteers and BAV patients. We propose a data analysis pipeline for the classification of the cardiac status, encompassing feature selection, model training and hyperparameter tuning. In our experiments, we use several feature selection methods and classification algorithms to train separate models for the healthy subgroups and BAV patients. We report on classification performance and investigate the predictive power of morphological and hemodynamic features with regard to the classification of the defined groups. Finally, we identify the key features for the best models.
The Wireless Train Communication Network: Roll2Rail vision
Venue: arXiv cs.NI 2020
Year: 2020

This paper explains the main results obtained from the research carried out in the work package 2 (WP2) of the Roll2Rail (R2R) project. This project aims to develop key technologies and to remove already identified blocking points for radical innovation in the field of railway vehicles, to increase their operational reliability and to reduce life-cycle costs. This project started in May 2015 and has been funded by the Horizon 2020 program of the European Commission. The goal for WP2 is to research on both technologies and architectures to develop a new wireless Train Communication Network (TCN) within IEC61375 standard series. This TCN is today entirely wired and is used for Train Control and Monitoring System (TCMS) functions (some of them safetyrelated), operator-oriented services and customer-oriented services. This paradigm shift from wired to wireless means a removal of wirings implies, among other benefits, a significant reduction of life cycle costs due to the removal of cables, and the simplification of the train coupling procedure, among others.
Incomplete Directed Perfect Phylogeny in Linear Time
Venue: arXiv cs.DS 2020
Year: 2020

Reconstructing the evolutionary history of a set of species is a central task in computational biology. In real data, it is often the case that some information is missing: the Incomplete Directed Perfect Phylogeny (IDPP) problem asks, given a collection of species described by a set of binary characters with some unknown states, to complete the missing states in such a way that the result can be explained with a perfect directed phylogeny. Pe'er et al. proposed a solution that takes $\tilde{O}(nm)$ time for $n$ species and $m$ characters. Their algorithm relies on pre-existing dynamic connectivity data structures: a computational study recently conducted by Fern{á}ndez-Baca and Liu showed that, in this context, complex data structures perform worse than simpler ones with worse asymptotic bounds. This gives us the motivation to look into the particular properties of the dynamic connectivity problem in this setting, so as to avoid the use of sophisticated data structures as a blackbox. Not only are we successful in doing so, and give a much simpler $\tilde{O}(nm)$-time algorithm for the IDPP problem; our insights into the specific structure of the problem lead to an asymptotically faster algorithm, that runs in optimal $O(nm)$ time.
A Lower Bound for Polynomial Calculus with Extension Rule
Venue: arXiv cs.CC 2020
Year: 2020

In this paper we study an extension of the Polynomial Calculus proof system where we can introduce new variables and take a square root. We prove that an instance of the subset-sum principle, the binary value principle, requires refutations of exponential bit size over rationals in this system. Part and Tzameret proved an exponential lower bound on the size of Res-Lin (Resolution over linear equations) refutations of the binary value principle. We show that our system p-simulates Res-Lin and thus we get an alternative exponential lower bound for the size of Res-Lin refutations of the binary value principle.
Modelling Lexical Ambiguity with Density Matrices
Venue: arXiv cs.CL 2020
Year: 2020

Words can have multiple senses. Compositional distributional models of meaning have been argued to deal well with finer shades of meaning variation known as polysemy, but are not so well equipped to handle word senses that are etymologically unrelated, or homonymy. Moving from vectors to density matrices allows us to encode a probability distribution over different senses of a word, and can also be accommodated within a compositional distributional model of meaning. In this paper we present three new neural models for learning density matrices from a corpus, and test their ability to discriminate between word senses on a range of compositional datasets. When paired with a particular composition method, our best model outperforms existing vector-based compositional models as well as strong sentence encoders.
Average Consensus: A Little Learning Goes A Long Way
Venue: arXiv cs.DC 2020
Year: 2020

When networked systems of autonomous agents carry out complex tasks, the control and coordination sought after generally depend on a few fundamental control primitives. Chief among these primitives is consensus, where agents are to converge to a common estimate within the range of initial values, which becomes average consensus when the joint limit should be the average of the initial values. To provide reliable services that are easy to deploy, these primitives should operate even when the network is subject to frequent and unpredictable changes. Moreover, they should mobilize few computational resources so that low powered, deterministic, and anonymous agents can partake in the network. In this stringent adversarial context, we investigate the distributed implementation of these primitives over networks with bidirectional, but potentially short-lived, communication links. Inspired by the classic EqualNeighbor and Metropolis agreement rules for multi-agent systems, we design distributed algorithms for consensus and average consensus, which we show to operate in polynomial time in a synchronous temporal model. These algorithms are fully distributed, requiring neither symmetry-breaking devices such as unique identifiers, nor global control or knowledge of the network. Our strategy consists in making agents learn simple structural parameters of the network -- namely, their largest degrees -- which constitutes enough information to build simple update rules, implementable locally with little computational and memory overhead.
On the Security of Group Communication Schemes
Venue: arXiv cs.CR 2020
Year: 2020

Secure group communications are a mechanism facilitating protected transmission of messages from a sender to multiple receivers, and many emerging applications in both wired and wireless networks need the support of such a mechanism. There have been many secure group communication schemes in wired networks, which can be directly adopted in, or appropriately adapted to, wireless networks such as mobile ad hoc networks (MANETs) and sensor networks. In this paper we show that the popular group communication schemes that we have examined are vulnerable to the following attack: An outside adversary who compromises a certain legitimate group member could obtain {\em all} past and present group keys (and thus all the messages protected by them); this is in sharp contrast to the widely-accepted belief that a such adversary can only obtain the present group key (and thus the messages protected by it). In order to understand and deal with the attack, we formalize two security models for stateful and stateless group communication schemes. We show that some practical methods can make a {\em subclass} of existing group communication schemes immune to the attack.
Revisiting Neural Architecture Search
Venue: arXiv cs.LG 2020
Year: 2020

Neural Architecture Search (NAS) is a collection of methods to craft the way neural networks are built. Current NAS methods are far from ab initio and automatic, as they use manual backbone architectures or micro building blocks (cells), which have had minor breakthroughs in performance compared to random baselines. They also involve a significant manual expert effort in various components of the NAS pipeline. This raises a natural question - Are the current NAS methods still heavily dependent on manual effort in the search space design and wiring like it was done when building models before the advent of NAS? In this paper, instead of merely chasing slight improvements over state-of-the-art (SOTA) performance, we revisit the fundamental approach to NAS and propose a novel approach called ReNAS that can search for the complete neural network without much human effort and is a step closer towards AutoML-nirvana. Our method starts from a complete graph mapped to a neural network and searches for the connections and operations by balancing the exploration and exploitation of the search space. The results are on-par with the SOTA performance with methods that leverage handcrafted blocks. We believe that this approach may lead to newer NAS strategies for a variety of network types.
Graph Square Roots of Small Distance from Degree One Graphs
Venue: arXiv cs.DS 2020
Year: 2020

Given a graph class $\mathcal{H}$, the task of the $\mathcal{H}$-Square Root problem is to decide, whether an input graph $G$ has a square root $H$ from $\mathcal{H}$. We are interested in the parameterized complexity of the problem for classes $\mathcal{H}$ that are composed by the graphs at vertex deletion distance at most $k$ from graphs of maximum degree at most one, that is, we are looking for a square root $H$ such that there is a modulator $S$ of size $k$ such that $H-S$ is the disjoint union of isolated vertices and disjoint edges. We show that different variants of the problems with constraints on the number of isolated vertices and edges in $H-S$ are FPT when parameterized by $k$ by demonstrating algorithms with running time $2^{2^{O(k)}}\cdot n^{O(1)}$. We further show that the running time of our algorithms is asymptotically optimal and it is unlikely that the double-exponential dependence on $k$ could be avoided. In particular, we prove that the VC-$k$ Root problem, that asks whether an input graph has a square root with vertex cover of size at most $k$, cannot be solved in time $2^{2^{o(k)}}\cdot n^{O(1)}$ unless Exponential Time Hypothesis fails. Moreover, we point out that VC-$k$ Root parameterized by $k$ does not admit a subexponential kernel unless $P=NP$.
EFSG: Evolutionary Fooling Sentences Generator
Venue: arXiv cs.CL 2020
Year: 2020

Large pre-trained language representation models (LMs) have recently collected a huge number of successes in many NLP tasks. In 2018 BERT, and later its successors (e.g. RoBERTa), obtained state-of-the-art results in classical benchmark tasks, such as GLUE benchmark. After that, works about adversarial attacks have been published to test their generalization proprieties and robustness. In this work, we design Evolutionary Fooling Sentences Generator (EFSG), a model- and task-agnostic adversarial attack algorithm built using an evolutionary approach to generate false-positive sentences for binary classification tasks. We successfully apply EFSG to CoLA and MRPC tasks, on BERT and RoBERTa, comparing performances. Results prove the presence of weak spots in state-of-the-art LMs. We finally test adversarial training as a data augmentation defence approach against EFSG, obtaining stronger improved models with no loss of accuracy when tested on the original datasets.
Viewpoint-Aware Channel-Wise Attentive Network for Vehicle Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

Vehicle re-identification (re-ID) matches images of the same vehicle across different cameras. It is fundamentally challenging because the dramatically different appearance caused by different viewpoints would make the framework fail to match two vehicles of the same identity. Most existing works solved the problem by extracting viewpoint-aware feature via spatial attention mechanism, which, yet, usually suffers from noisy generated attention map or otherwise requires expensive keypoint labels to improve the quality. In this work, we propose Viewpoint-aware Channel-wise Attention Mechanism (VCAM) by observing the attention mechanism from a different aspect. Our VCAM enables the feature learning framework channel-wisely reweighing the importance of each feature maps according to the "viewpoint" of input vehicle. Extensive experiments validate the effectiveness of the proposed method and show that we perform favorably against state-of-the-arts methods on the public VeRi-776 dataset and obtain promising results on the 2020 AI City Challenge. We also conduct other experiments to demonstrate the interpretability of how our VCAM practically assists the learning framework.
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Venue: arXiv cs.CL 2020
Year: 2020

Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. To collect a large corpus of parallel data, heuristic rules are often used but they inevitably let noise into the data, such as phrases in the output which cannot be explained by the input. Consequently, models pick up on the noise and may hallucinate--generate fluent but unsupported text. Our contribution is a simple but powerful technique to treat such hallucinations as a controllable aspect of the generated text, without dismissing any input and without modifying the model architecture. On the WikiBio corpus (Lebret et al., 2016), a particularly noisy dataset, we demonstrate the efficacy of the technique both in an automatic and in a human evaluation.
Emulating the First Principles of Matter: A Probabilistic Roadmap
Venue: arXiv cs.CE 2020
Year: 2020

This chapter provides a tutorial overview of first principles methods to describe the properties of matter at the ground state or equilibrium. It begins with a brief introduction to quantum and statistical mechanics for predicting the electronic structure and diverse static properties of of many-particle systems useful for practical applications. Pedagogical examples are given to illustrate the basic concepts and simple applications of quantum Monte Carlo and density functional theory -- two representative methods commonly used in the literature of first principles modeling. In addition, this chapter highlights the practical needs for the integration of physics-based modeling and data-science approaches to reduce the computational cost and expand the scope of applicability. A special emphasis is placed on recent developments of statistical surrogate models to emulate first principles calculation from a probabilistic point of view. The probabilistic approach provides an internal assessment of the approximation accuracy of emulation that quantifies the uncertainty in predictions. Various recent advances toward this direction establish a new marriage between Gaussian processes and first principles calculation, with physical properties, such as translational, rotational, and permutation symmetry, naturally encoded in new kernel functions. Finally, it concludes with some prospects on future advances in the field toward faster yet more accurate computation leveraging a synergetic combination {of} novel theoretical concepts and efficient numerical algorithms.