OpenRaiser/Intern-Atlas huggingface dataset:
Multilingual Sequence-to-Sequence Models for Hebrew NLP
Venue: arXiv cs.CL 2022
Year: 2022

Recent work attributes progress in NLP to large language models (LMs) with increased model size and large quantities of pretraining data. Despite this, current state-of-the-art LMs for Hebrew are both under-parameterized and under-trained compared to LMs in other languages. Additionally, previous work on pretrained Hebrew LMs focused on encoder-only models. While the encoder-only architecture is beneficial for classification tasks, it does not cater well for sub-word prediction tasks, such as Named Entity Recognition, when considering the morphologically rich nature of Hebrew. In this paper we argue that sequence-to-sequence generative architectures are more suitable for LLMs in the case of morphologically rich languages (MRLs) such as Hebrew. We demonstrate that by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a specialized, morpheme-based, separately fine-tuned decoder. Using this approach, our experiments show substantial improvements over previously published results on existing Hebrew NLP benchmarks. These results suggest that multilingual sequence-to-sequence models present a promising building block for NLP for MRLs.
Rigidity in Mechanism Design and its Applications
Venue: arXiv cs.GT 2022
Year: 2022

We introduce the notion of rigidity in auction design and use it to analyze some fundamental aspects of mechanism design. We focus on single-item auctions where the values of the bidders are drawn from some (possibly correlated) distribution $\mathcal F$. Let $f$ be the allocation function of an optimal mechanism for $\mathcal F$. Informally, $S$ is (linearly) rigid in $\mathcal F$ if for every mechanism $M'$ with an allocation function $f'$ where $f$ and $f'$ agree on the allocation of at most $x$-fraction of the instances of $S$, the expected revenue of $M'$ is at most an $x$ fraction of the optimal revenue. We use rigidity to explain the singular success of Cremer and McLean's auction. Recall that the revenue of Cremer and McLean's auction is the optimal welfare if the distribution obeys a certain ``full rank'' condition, but no analogous constructions are known if this condition does not hold. Note that the Kolmogorov complexity of the allocation function of Cremer and McLean's auction is logarithmic, whereas we use rigidity to show that for some distributions that do not obey the full rank condition, the Kolmogorov complexity of the allocation function of every mechanism that provides a constant approximation is almost linear. We further investigate rigidity assuming different notions of individual rationality. Assuming ex-post individual rationality, if there is a rigid set, the structure of the optimal mechanism is simple: the player with the highest value ``usually'' wins the item and contributes most of the revenue. In contrast, assuming interim individual rationality, there are distributions with a rigid set $S$ where the optimal mechanism has no obvious allocation pattern (i.e., its Kolmogorov complexity is high). Our results help explain why we have little hope of developing good, simple and generic approximation mechanisms in the interim individual rationality world.
Continuous Semi-Supervised Nonnegative Matrix Factorization
Venue: arXiv cs.CL 2022
Year: 2022

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In this paper, we show this factorization can be combined with regression on a continuous response variable. In practice, the method performs better than regression done after topics are identified and retrains interpretability.
CompuMat: A Computational Composite Material for Tangible Interaction
Venue: arXiv cs.HC 2022
Year: 2022

This paper introduces a computational composite material comprising layers for actuation, computation and energy storage. Key to its design is inexpensive materials assembled from traditionally available fabrication machines to support the rapid exploration of applications from computational composites. The actuation layer is a soft magnetic sheet that is programmed to either bond, repel, or remain agnostic to other areas of the sheet. The computation layer is a flexible PCB made from copper-clad kapton engraved by a fiber laser, powered by a third energy-storage layer comprised of 0.4mm-thin lithium polymer batteries. We present the material layup and an accompanying digital fabrication process enabling users to rapidly prototype their own untethered, interactive and tangible prototypes. The material is low-profile, inexpensive, and fully untethered, capable of being used for a variety of applications in HCI and robotics including structural origami and proprioception.
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
Venue: arXiv cs.LG 2022
Year: 2022

Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filtering, especially when side information over users or items is available. Bayesian formulations of FMs have been proposed to provide confidence intervals over the predictions made by the model, however they usually involve Markov-chain Monte Carlo methods that require many samples to provide accurate predictions, resulting in slow training in the context of large-scale data. In this paper, we propose a variational formulation of factorization machines that allows us to derive a simple objective that can be easily optimized using standard mini-batch stochastic gradient descent, making it amenable to large-scale data. Our algorithm learns an approximate posterior distribution over the user and item parameters, which leads to confidence intervals over the predictions. We show, using several datasets, that it has comparable or better performance than existing methods in terms of prediction accuracy, and provide some applications in active learning strategies, e.g., preference elicitation techniques.
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach
Venue: arXiv cs.CV 2022
Year: 2022

Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality.
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
Venue: arXiv cs.LG 2022
Year: 2022

We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the approach very attractive for predictive maintenance of the heat exchanger in digital twin environments.
Efficient Liquidity Providing via Margin Liquidity
Venue: arXiv cs.CE 2022
Year: 2022

The limit order book mechanism has been the core trading mechanism of the modern financial market. In the cryptocurrency market, centralized exchanges also adopt this limit order book mechanism and a centralized matching engine dynamically connects the traders to the orders of market makers. Recently, decentralized exchanges have been introduced and received considerable attention in the cryptocurrency community. A decentralized exchange typically adopts an automated market maker, which algorithmically arbitrates the trades between liquidity providers and traders through a pool of crypto assets. Meanwhile, the liquidity of the exchange is the most important factor when traders choose an exchange. However, the amount of liquidity provided by the liquidity providers in decentralized exchanges is insufficient when compared to centralized exchanges. This is because the liquidity providers in decentralized exchanges suffer from the risk of divergence loss inherent to the automated market making system. To this end, we introduce a new concept called margin liquidity and leverage this concept to propose a highly profitable margin liquidity-providing position. Then, we extend this margin liquidity-providing position to a virtual margin liquidity-providing position to alleviate the risk of divergence loss for the liquidity providers and encourage them to provide more liquidity to the pool. Furthermore, we introduce a representative strategy for the margin liquidity-providing position and backtest the strategy with historical data from the BTC/ETH market. Our strategy outperforms a simple holding baseline. We also show that our proposed margin liquidity is 8K times more capital efficient than the concentrated liquidity proposed in Uniswap V3.
A Survey on Pretrained Language Models for Neural Code Intelligence
Venue: arXiv cs.SE 2022
Year: 2022

As the complexity of modern software continues to escalate, software engineering has become an increasingly daunting and error-prone endeavor. In recent years, the field of Neural Code Intelligence (NCI) has emerged as a promising solution, leveraging the power of deep learning techniques to tackle analytical tasks on source code with the goal of improving programming efficiency and minimizing human errors within the software industry. Pretrained language models have become a dominant force in NCI research, consistently delivering state-of-the-art results across a wide range of tasks, including code summarization, generation, and translation. In this paper, we present a comprehensive survey of the NCI domain, including a thorough review of pretraining techniques, tasks, datasets, and model architectures. We hope this paper will serve as a bridge between the natural language and programming language communities, offering insights for future research in this rapidly evolving field.
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion
Venue: arXiv cs.SD 2022
Year: 2022

Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires significant data and hardware resources. Manufacturers often entrust this process to a third-party platform. This makes the training process uncontrollable, where attackers can implant backdoors in the model by manipulating third-party training data. An effective backdoor attack can force the model to make specified judgments under certain conditions, i.e., triggers. In this paper, we design a backdoor attack scheme based on Voiceprint Selection and Voice Conversion, abbreviated as VSVC. Experimental results demonstrated that VSVC is feasible to achieve an average attack success rate close to 97% in four victim models when poisoning less than 1% of the training data.
Minimizing interference-to-signal ratios in multi-cell networks
Venue: arXiv cs.SI 2022
Year: 2022

In contemporary wireless communication networks, base-stations are organized into coordinated clusters (called cells) to jointly serve the users. However, such fixed systems are plagued by the so-called cell-edge problem: near the boundaries, the interference between neighboring clusters can result in very poor interference-to-signal-power ratios. To achieve a high quality service, it is an important objective to minimize the sum of these ratios over the cells. The most common approach to solve this minimization problem is arguably the spectral clustering method. In this paper, we propose a new clustering approach, which is deterministic and computationally much less demanding than current methods. Simulating on synthetic instances indicates that our methods typically provide higher quality solutions than earlier methods. An earlier version of this algorithm was reported in arXiv:2111.00885.
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
Venue: arXiv cs.AI 2022
Year: 2022

Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep learning models, which are infamously difficult to interpret. This problem is particularly exasperated in the context of recommendation scenarios, as it erodes the user's trust in the RS. In contrast, the newly introduced Tsetlin Machines (TM) possess some valuable properties due to their inherent interpretability. TMs are still fairly young as a technology. As no RS has been developed for TMs before, it has become necessary to perform some preliminary research regarding the practicality of such a system. In this paper, we develop the first RS based on TMs to evaluate its practicality in this application domain. This paper compares the viability of TMs with other machine learning models prevalent in the field of RS. We train and investigate the performance of the TM compared with a vanilla feed-forward deep learning model. These comparisons are based on model performance, interpretability/explainability, and scalability. Further, we provide some benchmark performance comparisons to similar machine learning solutions relevant to RSs.
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis
Venue: arXiv cs.CL 2022
Year: 2022

The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of natural language generation (NLG) tasks, including machine translation, text summarization, and data-to-text. Recent studies show that considering both major errors (e.g. mistranslated tokens) and minor errors (e.g. imperfections in fluency) can produce high-quality human judgments. This inspires us to approach the final goal of the evaluation metrics (human-like evaluations) by automatic error analysis. To this end, we augment BARTScore by incorporating the human-like error analysis strategies, namely BARTScore++, where the final score consists of both the evaluations of major errors and minor errors. Experimental results show that BARTScore++ can consistently improve the performance of vanilla BARTScore and outperform existing top-scoring metrics in 20 out of 25 test settings. We hope our technique can also be extended to other pre-trained model-based metrics. We will release our code and scripts to facilitate the community.
Reasonable thickness determination for implicit porous sheet structure using persistent homology
Venue: arXiv cs.CG 2022
Year: 2022

Porous structures are widely used in various industries because of their excellent properties. Porous surfaces have no thickness and should be thickened to sheet structures for further fabrication. However, conventional methods for generating sheet structures are inefficient for porous surfaces because of the complexity of the internal structures. In this study, we propose a novel method for generating porous sheet structures directly from point clouds sampled on a porous surface. The generated sheet structure is represented by an implicit B-spline function, which ensures smoothness and closure. Moreover, based on the persistent homology theory, the topology structure of the generated porous sheet structure can be controlled, and a reasonable range of the uniform thickness of the sheet structure can be calculated to ensure manufacturability and pore existence. Finally, the implicitly B-spline represented sheet structures are sliced directly with the marching squares algorithm, and the contours can be used for 3D printing. Experimental results show the superiority of the developed method in efficiency over the traditional methods.
Emotion Selectable End-to-End Text-based Speech Editing
Venue: arXiv cs.SD 2022
Year: 2022

Text-based speech editing allows users to edit speech by intuitively cutting, copying, and pasting text to speed up the process of editing speech. In the previous work, CampNet (context-aware mask prediction network) is proposed to realize text-based speech editing, significantly improving the quality of edited speech. This paper aims at a new task: adding emotional effect to the editing speech during the text-based speech editing to make the generated speech more expressive. To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech. Firstly, we propose an end-to-end emotion-selectable text-based speech editing model. The key idea of the model is to control the emotion of generated speech by introducing additional emotion attributes based on the context-aware mask prediction network. Secondly, to prevent the emotion of the generated speech from being interfered by the emotional components in the original speech, a neutral content generator is proposed to remove the emotion from the original speech, which is optimized by the generative adversarial framework. Thirdly, two data augmentation methods are proposed to enrich the emotional and pronunciation information in the training set, which can enable the model to edit the unseen speaker's speech. The experimental results that 1) Emo-CampNet can effectively control the emotion of the generated speech in the process of text-based speech editing; And can edit unseen speakers' speech. 2) Detailed ablation experiments further prove the effectiveness of emotional selectivity and data augmentation methods. The demo page is available at https://hairuo55.github.io/Emo-CampNet/
Image quality prediction using synthetic and natural codebooks: comparative results
Venue: arXiv cs.CV 2022
Year: 2022

We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.
Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task
Venue: arXiv cs.LG 2022
Year: 2022

From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental processes, building generative models of eye movements is notoriously difficult, and to date the computational objectives guiding eye movements remain largely a mystery. In this work, we tackled these problems in the context of a canonical spatial planning task, maze-solving. We collected eye movement data from human subjects and built deep generative models of eye movements using a novel differentiable architecture for gaze fixations and gaze shifts. We found that human eye movements are best predicted by a model that is optimized not to perform the task as efficiently as possible but instead to run an internal simulation of an object traversing the maze. This not only provides a generative model of eye movements in this task but also suggests a computational theory for how humans solve the task, namely that humans use mental simulation.
Tuning the Tail Latency of Distributed Queries Using Replication
Venue: arXiv cs.DB 2022
Year: 2022

Querying graph data with low latency is an important requirement in application domains such as social networks and knowledge graphs. Graph queries perform multiple hops between vertices. When data is partitioned and stored across multiple servers, queries executing at one server often need to hop to vertices stored by another server. Such distributed traversals represent a performance bottleneck for low-latency queries. To reduce query latency, one can replicate remote data to make distributed traversals unnecessary, but replication is expensive and should be minimized. In this paper, we introduce the problem of finding data replication schemes that satisfy arbitrary user-defined query latency constraints with minimal replication cost. We propose a novel workload model to express data access causality, propose a family of heuristics, and introduce non-trivial sufficient conditions for their correctness. Our evaluation on two representative benchmarks show that our algorithms enable fine-tuning query latency with data replication and can find sweet spots in the latency/replication design space.
DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.