OpenRaiser/Intern-Atlas huggingface dataset:
Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing
Venue: arXiv cs.CE 2023
Year: 2023

Sensing the fluid flow around an arbitrary geometry entails extrapolating from the physical quantities perceived at its surface in order to reconstruct the features of the surrounding fluid. This is a challenging inverse problem, yet one that if solved could have a significant impact on many engineering applications. The exploitation of such an inverse logic has gained interest in recent years with the advent of widely available cheap but capable MEMS-based sensors. When combined with novel data-driven methods, these sensors may allow for flow reconstruction around immersed structures, benefiting applications such as unmanned airborne/underwater vehicle path planning or control and structural health monitoring of wind turbine blades. In this work, we train deep reversible Graph Neural Networks (GNNs) to perform flow sensing (flow reconstruction) around two-dimensional aerodynamic shapes: airfoils. Motivated by recent work, which has shown that GNNs can be powerful alternatives to mesh-based forward physics simulators, we implement a Message-Passing Neural Network to simultaneously reconstruct both the pressure and velocity fields surrounding simulated airfoils based on their surface pressure distributions, whilst additionally gathering useful farfield properties in the form of context vectors. We generate a unique dataset of Computational Fluid Dynamics simulations by simulating random, yet meaningful combinations of input boundary conditions and airfoil shapes. We show that despite the challenges associated with reconstructing the flow around arbitrary airfoil geometries in high Reynolds turbulent inflow conditions, our framework is able to generalize well to unseen cases.
Doc2Query--: When Less is More
Venue: arXiv cs.IR 2023
Year: 2023

Doc2Query -- the process of expanding the content of a document before indexing using a sequence-to-sequence model -- has emerged as a prominent technique for improving the first-stage retrieval effectiveness of search engines. However, sequence-to-sequence models are known to be prone to "hallucinating" content that is not present in the source text. We argue that Doc2Query is indeed prone to hallucination, which ultimately harms retrieval effectiveness and inflates the index size. In this work, we explore techniques for filtering out these harmful queries prior to indexing. We find that using a relevance model to remove poor-quality queries can improve the retrieval effectiveness of Doc2Query by up to 16%, while simultaneously reducing mean query execution time by 23% and cutting the index size by 33%. We release the code, data, and a live demonstration to facilitate reproduction and further exploration at https://github.com/terrierteam/pyterrier_doc2query.
Construction of Optimal Binary Z-Complementary Code Sets with New Lengths
Venue: arXiv cs.IT 2023
Year: 2023

Z-complementary code sets (ZCCSs) are used in multicarrier code-division multiple access (MC-CDMA) systems, for interference-free communication over multiuser and quasi-asynchronous environments. In this letter, we propose three new constructions of optimal binary $\left(R2^{k+1},2^{k+1}, Rγ,γ\right)$-ZCCS, $\left(R2^{k+1},2^{k+1}, R2^{m_{2}},2^{m_{2}}\right)$-ZCCS and $\left(2^{k+1},2^{k+1},3γ,2γ\right)$-ZCCS based on generalized Boolean functions (GBFs), where $γ=2^{m_{1}-1}+2^{m_{1}-3}, m_{1}\geq 5, k\geq 1,m_{2}\geq 1$ and $R$ is any even number. The proposed ZCCSs cover many unreported lengths and large set sizes.
ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization
Venue: arXiv cs.NE 2023
Year: 2023

The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity, and convergence. However, it is nontrivial to balance these three elements simultaneously by using a single tradeoff model since the importance of each element varies in different evolutionary phases. As an alternative, we adapt different tradeoff models in different phases and propose a novel algorithm called ATM-R. In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence", which can facilitate the discovering of enough feasible regions and speed up the search for the feasible Pareto optima in succession. In the feasible phase, the tradeoff between diversity and convergence is considered to attain a set of well-converged and well-distributed feasible solutions. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a wide range of benchmark test functions demonstrate that ATM-R is effective and competitive, compared against five state-of-the-art constrained multiobjective optimization evolutionary algorithms.
Sparse Geometric Set Systems and the Beck-Fiala Conjecture
Venue: arXiv cs.CG 2023
Year: 2023

We investigate the combinatorial discrepancy of geometric set systems having bounded shallow cell complexity in the \emph{Beck-Fiala} setting, where each point belongs to at most $t$ ranges. For set systems with shallow cell complexity $ψ(m,k)=g(m)k^{c}$, where $(i)$ $g(m) = o(m^{\varepsilon})$ for any $\varepsilon\in (0,1],$ $(ii)$ $ψ$ is non-decreasing in $m$, and $(iii)$ $c>0$ is independent of $m$ and $k$, we get a discrepancy bound of \[ O\left(\sqrt{\left(\log n+\left(t^{c}g(n)\right)^{\frac{1}{1+c}}\right)\log n}\right).\] For $t=ω(\log^2 n)$, in several cases, such as for set systems of points and half-planes / disks / pseudo-disks in $\mathbb{R}^2$, points and orthants in $\mathbb{R}^3$ etc., these bounds are $o(\sqrt{t})$, which verifies (and improves upon) the conjectured bound of Beck and Fiala~\emph{(Disc. Appl. Math., 1981)}. Our bounds are obtained by showing the existence of \emph{matchings with low crossing number}, using the multiplicative weights update method of Welzl \emph{(SoCG, 1988)}, together with the recent bound of Mustafa \emph{(Disc. Comp. Geom., 2015)} on \emph{shallow packings} of set systems in terms of their shallow cell complexity. For set systems of shallow cell complexity $ψ(m,k)=m^{c_1}g(m)k^{c}$, we obtain matchings with crossing number at most \[ O\left(\left(n^{c_1}g(n)t^{c}\right)^{\frac{1}{1+c_1+c}}\right).\] These are of independent interest.
Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by $k$-means clustering to segment the image. Specifically for the image smoothing step, we replace the least-squares fidelity for Gaussian noise in the Mumford-Shah model with a maximum posterior (MAP) term to deal with Poisson noise and we incorporate the weighted difference of anisotropic and isotropic total variation (AITV) as a regularization to promote the sparsity of image gradients. For such a nonconvex model, we develop a specific splitting scheme and utilize a proximal operator to apply the alternating direction method of multipliers (ADMM). Convergence analysis is provided to validate the efficacy of the ADMM scheme. Numerical experiments on various segmentation scenarios (grayscale/color and multiphase) showcase that our proposed method outperforms a number of segmentation methods, including the original SaT.
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing
Venue: arXiv cs.PL 2023
Year: 2023

We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fast convergence in a traditional statistics setting. Unfortunately, discontinuities, which are readily expressible in programming languages, can compromise the correctness of this approach. We consider a simple (higher-order, probabilistic) programming language with conditionals, and we endow our language with both a measurable and a smoothed (approximate) value semantics. We present type systems which establish technical pre-conditions. Thus we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem. Besides, we can solve the original problem up to any error tolerance by choosing an accuracy coefficient suitably. Empirically we demonstrate that our approach has a similar convergence as a key competitor, but is simpler, faster, and attains orders of magnitude reduction in work-normalised variance.
Parallel Reasoning Network for Human-Object Interaction Detection
Venue: arXiv cs.CV 2023
Year: 2023

Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
Strong Collapse of Random Simplicial Complexes
Venue: arXiv cs.CG 2023
Year: 2023

The \emph{strong collapse} of a simplicial complex, proposed by Barmak and Minian (\emph{Disc. Comp. Geom. 2012}), is a combinatorial collapse of a complex onto its sub-complex. Recently, it has received attention from computational topology researchers, owing to its empirically observed usefulness in simplification and size-reduction of the size of simplicial complexes while preserving the homotopy class. We consider the strong collapse process on random simplicial complexes. For the Erdős-Rényi random clique complex $X(n,c/n)$ on $n$ vertices with edge probability $c/n$ with $c>1$, we show that after any maximal sequence of strong collapses the remaining subcomplex, or \emph{core} must have $(1-γ)(1-cγ) n+o(n)$ vertices asymptotically almost surely (a.a.s.), where $γ$ is the least non-negative fixed point of the function $f(x) = \exp\left(-c(1-x)\right)$ in the range $(0,1)$. These are the first theoretical results proved for strong collapses on random (or non-random) simplicial complexes.
A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System
Venue: arXiv cs.CR 2023
Year: 2023

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.
Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models
Venue: arXiv cs.LG 2023
Year: 2023

In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets (generic models) predicted safety outcomes better than the company-specific models. We experimented with full generic models (trained on all data), per-domain generic models (construction, electric T&D, oil & gas), and with ensembles of generic and specific models. Results are very positive, with generic models outperforming the company-specific models in most cases while also generating finer-grained, hence more useful, forecasts. Successful generic models remove the needs for training company-specific models, saving a lot of time and resources, and give small companies, whose accident datasets are too limited to train their own models, access to safety outcome predictions. It may still however be advantageous to train specific models to get an extra boost in performance through ensembling with the generic models. Overall, by learning lessons from a pool of datasets whose accumulated experience far exceeds that of any single company, and making these lessons easily accessible in the form of simple forecasts, generic models tackle the holy grail of safety cross-organizational learning and dissemination in the construction industry.
Machine Learning Applied to Peruvian Vegetables Imports
Venue: arXiv cs.LG 2023
Year: 2023

The current research work is being developed as a training and evaluation object. the performance of a predictive model to apply it to the imports of vegetable products into Peru using artificial intelligence algorithms, specifying for this study the Machine Learning models: LSTM and PROPHET. The forecast is made with data from the monthly record of imports of vegetable products(in kilograms) from Peru, collected from the years 2021 to 2022. As part of applying the training methodology for automatic learning algorithms, the exploration and construction of an appropriate dataset according to the parameters of a Time Series. Subsequently, the model with better performance will be selected, evaluating the precision of the predicted values so that they account for sufficient reliability to consider it a useful resource in the forecast of imports in Peru.
Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics
Venue: arXiv cs.DC 2023
Year: 2023

The diversity of workload requirements and increasing hardware heterogeneity in emerging high performance computing (HPC) systems motivate resource disaggregation. Resource disaggregation allows compute and memory resources to be allocated individually as required to each workload. However, it is unclear how to efficiently realize this capability and cost-effectively meet the stringent bandwidth and latency requirements of HPC applications. To that end, we describe how modern photonics can be co-designed with modern HPC racks to implement flexible intra-rack resource disaggregation and fully meet the bit error rate (BER) and high escape bandwidth of all chip types in modern HPC racks. Our photonic-based disaggregated rack provides an average application speedup of 11% (46% maximum) for 25 CPU and 61% for 24 GPU benchmarks compared to a similar system that instead uses modern electronic switches for disaggregation. Using observed resource usage from a production system, we estimate that an iso-performance intra-rack disaggregated HPC system using photonics would require 4x fewer memory modules and 2x fewer NICs than a non-disaggregated baseline.
Hierarchical core-periphery structure in networks
Venue: arXiv cs.SI 2023
Year: 2023

We study core-periphery structure in networks using inference methods based on a flexible network model that allows for traditional onion-like cores within cores, but also for hierarchical tree-like structures and more general non-nested types of structure. We propose an efficient Monte Carlo scheme for fitting the model to observed networks and report results for a selection of real-world data sets. Among other things, we observe an empirical distinction between networks showing traditional core-periphery structure with a dense core weakly connected to a sparse periphery, and an alternative structure in which the core is strongly connected both within itself and to the periphery. Networks vary in whether they are better represented by one type of structure or the other. We also observe structures that are a hybrid between core-periphery structure and community structure, in which networks have a set of non-overlapping cores that correspond roughly to communities, surrounded by a single undifferentiated periphery. Computer code implementing our methods is available.
OpenMP Advisor
Venue: arXiv cs.DC 2023
Year: 2023

With the increasing diversity of heterogeneous architecture in the HPC industry, porting a legacy application to run on different architectures is a tough challenge. In this paper, we present OpenMP Advisor, a first of its kind compiler tool that enables code offloading to a GPU with OpenMP using Machine Learning. Although the tool is currently limited to GPUs, it can be extended to support other OpenMP-capable devices. The tool has two modes: Training mode and Prediction mode. The training mode must be executed on the target hardware. It takes benchmark codes as input, generates and executes every variant of the code that could possibly run on the target device, and then collects data from all of the executed codes to train an ML-based cost model for use in prediction mode. However, in prediction mode the tool does not need any interaction with the target device. It accepts a C code as input and returns the best code variant that can be used to offload the code to the specified device. The tool can determine the kernels that are best suited for offloading by predicting their runtime using a machine learning-based cost model. The main objective behind this tool is to maintain the portability aspect of OpenMP. Using our Advisor, we were able to generate code of multiple applications for seven different architectures, and correctly predict the top ten best variants for each application on every architecture. Preliminary findings indicate that this tool can assist compiler developers and HPC application researchers in porting their legacy HPC codes to the upcoming heterogeneous computing environment.
Protected load balancing problem: Neural-network based approximation for non-convex optimization
Venue: arXiv cs.NI 2023
Year: 2023

Nowadays, centralized Path Computation Elements (PCE) integrate control plane algorithms to optimize routing and load-balancing continuously. When a link fails, the traffic load is automatically transferred to the remaining paths according to the configuration of load-balancers. In this context, we propose a load-balancing method that anticipates load transfers to ensure the protection of traffic against any Shared Risk Link Group (SRLG) failure. The main objective of this approach is to make better use of bandwidth compared to existing methods. It consists in reserving a minimum amount of extra bandwidth on links so that the rerouting of traffic is guaranteed. We propose a non-linear non-convex model for the problem of minimizing the bandwidth reservation cost. We introduce a new approximation approach based on a neural network to convexify the problem and apply Kelley's cutting plane method to solve the problem. Finally, we show that our algorithm significantly improves the CPU time against a compact model solved using the SCIP solver.
Towards Multifaceted Human-Centered AI
Venue: arXiv cs.DB 2023
Year: 2023

Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks. In this paper, we call for a holistic view when designing support mechanisms, such as interaction paradigms, interfaces, and systems, for these multifaceted workflows.
A Multi-Level Framework for the AI Alignment Problem
Venue: arXiv cs.CY 2023
Year: 2023

AI alignment considers how we can encode AI systems in a way that is compatible with human values. The normative side of this problem asks what moral values or principles, if any, we should encode in AI. To this end, we present a framework to consider the question at four levels: Individual, Organizational, National, and Global. We aim to illustrate how AI alignment is made up of value alignment problems at each of these levels, where values at each level affect the others and effects can flow in either direction. We outline key questions and considerations of each level and demonstrate an application of this framework to the topic of AI content moderation.
Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation
Venue: arXiv cs.SD 2023
Year: 2023

Despite advances in deep learning, current state-of-the-art speech emotion recognition (SER) systems still have poor performance due to a lack of speech emotion datasets. This paper proposes augmenting SER systems with synthetic emotional speech generated by an end-to-end text-to-speech (TTS) system based on an extended Tacotron architecture. The proposed TTS system includes encoders for speaker and emotion embeddings, a sequence-to-sequence text generator for creating Mel-spectrograms, and a WaveRNN to generate audio from the Mel-spectrograms. Extensive experiments show that the quality of the generated emotional speech can significantly improve SER performance on multiple datasets, as demonstrated by a higher mean opinion score (MOS) compared to the baseline. The generated samples were also effective at augmenting SER performance.