pwc-archive/papers-with-abstracts huggingface dataset:
Chance Constrained Stochastic Optimal Control Based on Sample Statistics With Almost Surely Probabilistic Guarantees
Authors: Shawn Priore, Meeko Oishi
Date: 2023-03-29 00:00:00
Tasks: Collision Avoidance

While techniques have been developed for chance constrained stochastic optimal control using sample disturbance data that provide a probabilistic confidence bound for chance constraint satisfaction, far less is known about how to use sample data in a manner that can provide almost surely guarantees of chance constraint satisfaction. In this paper, we develop a method for solving chance constrained stochastic optimal control problems for linear time-invariant systems with unknown but sampled additive disturbances. We propose an open-loop control scheme for multi-vehicle planning, with both target sets and collision avoidance constraints. To this end, we derive a concentration inequality to bound the tail probability of a random variable based on sample statistics. Using the derived concentration inequality, we reformulate each chance constraint in terms of the sample mean and sample standard deviation. While the reformulated bounds are conservative with respect to the original bounds, they have a simple and closed form, guarantee chance constraint satisfaction almost surely, and are amenable to difference of convex optimization techniques. We demonstrate our approach on two multi-satellite rendezvous problems.
Cooperative Lane Changing in Mixed Traffic can be Robust to Human Driver Behavior
Authors: Anni Li, Andres S. Chavez Armijos, Christos G. Cassandras
Date: 2023-03-29 00:00:00
Tasks: Bilevel Optimization

We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires designing the best possible response of a CAV to actions by its neighboring HDVs. This interaction is formulated using a bilevel optimization setting with an appropriate behavioral model for an HDV's. Then, an iterated best response (IBR) method is used to determine a Nash equilibrium. However, we also show that when a common and simple-to-detect condition applies, the optimal lane-changing policy is in fact independent of HDV behavior with a CAV changing lanes by cooperating with another CAV in the target lane and always merging ahead of it. Thus, the dependence on the interaction between CAVs and HDVs may be eliminated in such cases. Simulation results are included to show the effectiveness of our controllers in terms of cost, safety guarantees, and disruption to the traffic flow when uncontrollable HDVs are present.
Data-driven multiscale modeling for correcting dynamical systems
Authors: Karl Otness, Laure Zanna, Joan Bruna
Date: 2023-03-24 00:00:00
Tasks: Prediction

We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine directions. We envision this method being generally applicable to problems with significant self-similarity or in which the prediction task is challenging and where stability of a learned model's impact on the target dynamical system is important. We evaluate our approach on a climate subgrid parameterization task in which our multiscale networks correct chaotic underlying models to reflect the contributions of unresolved, fine-scale dynamics.
Precise and scalable self-organization in mammalian pseudo-embryos
Authors: Melody Merle, Leah Friedman, Corinne Chureau, Armin Shoushtarizadeh. Thomas Gregor
Date: 2023-03-30 00:00:00

Gene expression is inherently noisy, posing a challenge to understanding how precise and reproducible patterns of gene expression emerge in mammals. We investigate this phenomenon using gastruloids, an in vitro model for early mammalian development. Our study reveals intrinsic reproducibility in the self-organization of gastruloids, encompassing growth dynamics and gene expression patterns. We observe a remarkable degree of control over gene expression along the main body axis, with pattern boundaries positioned at single-cell precision. Furthermore, as gastruloids grow, both their physical proportions and gene expression patterns scale proportionally with system size. Notably, these properties emerge spontaneously in self-organizing cell aggregates, distinct from many in vivo systems constrained by fixed boundary conditions. Our findings shed light on the intricacies of developmental precision, reproducibility, and size scaling within a mammalian system, suggesting that these phenomena might constitute fundamental features of multicellularity.
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
Authors: Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, Guolin Ke
Date: 2023-03-16 00:00:00
Tasks: Benchmarking, Graph Regression, Initial Structure to Relaxed Energy (IS2RE), Direct, Prediction, Property Prediction

Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at \url{https://github.com/dptech-corp/Uni-Mol}.
Capitalising the Network Externalities of New Land Supply in the Metaverse
Authors: Kanis Saengchote, Voraprapa Nakavachara, Yishuang Xu
Date: 2023-03-30 00:00:00

When land becomes more connected, its value can change because of network externalities. This idea is intuitive and appealing to developers and policymakers, but documenting their importance is empirically challenging because it is difficult to isolate the determinants of land value in practice. We address this challenge with real estate in The Sandbox, a virtual economy built on blockchain, which provides a series of natural experiments that can be used to estimate the causal impact of land-based of network externalities. Our results show that when new land becomes available, the network value of existing land increases, but there is a trade-off as new land also competes with existing supply. Our work illustrates the benefits of using virtual worlds to conduct policy experiments.
Entrepreneurial Capability And Engagement Of Persons With Disabilities Toward A Framework For Inclusive Entrepreneurship
Authors: Xavier Lawrence D. Mendoza
Date: 2023-03-30 00:00:00
Tasks: Descriptive

The study was designed to determine the entrepreneurial capability and engagement of persons with disabilities toward a framework for inclusive entrepreneurship. The researcher used descriptive and correlational approaches through purposive random sampling. The sample came from the City of General Trias and the Municipality of Rosario, registered under their respective Persons with Disabilities Affairs Offices (PDAO). The findings indicated that the respondents are from the working class, are primarily female, are mostly single, have college degrees, live in a medium-sized home, and earn the bare minimum. Furthermore, PWDs' perceived capability level in entrepreneurship was somehow capable, and the majority of engagement level responses were somehow engaged. Considerably, age and civil status have significant relationships with most of the variables under study. Finally, the perceived challenges of PWDs' respondents noted the following: lack of financial capacity, access to credit and other financial institutions, absence of business information, absence of access to data, lack of competent business skills, lack of family support, and lack of personal motivation. As a result, the author proposed a framework that emphasizes interaction and cooperation between national and local government units in the formulation of policies promoting inclusive entrepreneurship for people with disabilities.
Liquidity Constraints, Cash Windfalls, and Entrepreneurship: Evidence from Administrative Data on Lottery Winners
Authors: Hsuan-Hua Huang, Hsing-Wen Han, Kuang-Ta Lo, Tzu-Ting Yang
Date: 2023-03-29 00:00:00

Using administrative data on Taiwanese lottery winners, this paper examines the effects of cash windfalls on entrepreneurship. We compare the start-up decisions of households winning more than 1.5 million NTD (50,000 USD) in the lottery in a particular year with those of households winning less than 15,000 NTD (500 USD). Our results suggest that a substantial windfall increases the likelihood of starting a business by 1.5 percentage points (125% from the baseline mean). Startup wealth elasticity is 0.25 to 0.36. Moreover, households who tend to be liquidity-constrained drive the windfall-induced entrepreneurial response. Finally, we examine how households with a business react to a cash windfall and find that serial entrepreneurs are more likely to start a new business but do not change their decision to continue the current business.
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference
Authors: Aytac Özkan, Yi-Hsin Li, Thomas Sikora
Date: 2023-03-30 00:00:00
Tasks: Denoising, Image Denoising, Mixture-of-Experts, regression

In this paper we introduce a novel block-based regression strategy for image denoising based on edge-aware Steered-Mixture-of-Experts (SMoE) models. SMoEs provide very sparse image representations, able to model sharp edges as well as smooth transitions in images efficiently with few parameters. A multi-model inference strategy is developed that improves significantly the denoising capacity of single SMoE models. We show that the important edge reconstruction properties of SMoEs are well preserved, even when many models are fused under severe noise. We investigate model-inference from local neighborhood blocks as well as from distant blocks using block-matching as in BM3D. Our initial results indicate that SMoE multi-model regression can provide promising results compared to state-of-the-art BM3D with excellent edge quality.
Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration
Authors: Lisa Y. W. Tang
Date: 2023-03-23 00:00:00
Tasks: Medical Image Analysis

Ground-glass opacity is a hallmark of numerous lung diseases, including patients with COVID19 and pneumonia, pulmonary fibrosis, and tuberculosis. This brief note presents experimental results of a proof-of-concept framework that got implemented and tested over three days as driven by the third challenge entitled "COVID-19 Competition", hosted at the AI-Enabled Medical Image Analysis Workshop of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Using a newly built virtual environment (created on March 17, 2023), we investigated various pre-trained two-dimensional convolutional neural networks (CNN) such as Dense Neural Network, Residual Neural Networks (ResNet), and Vision Transformers, as well as the extent of fine-tuning. Based on empirical experiments, we opted to fine-tune them using ADAM's optimization algorithm with a standard learning rate of 0.001 for all CNN architectures and apply early-stopping whenever the validation loss reached a plateau. For each trained CNN, the model state with the best validation accuracy achieved during training was stored and later reloaded for new classifications of unseen samples drawn from the validation set provided by the challenge organizers. According to the organizers, few of these 2D CNNs yielded performance comparable to an architecture that combined ResNet and Recurrent Neural Network (Gated Recurrent Units). As part of the challenge requirement, the source code produced during the course of this exercise is posted at https://github.com/lisatwyw/cov19. We also hope that other researchers may find this light prototype consisting of few Python files based on PyTorch 1.13.1 and TorchVision 0.14.1 approachable.
Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images
Authors: Rasmus Netterstrøm, Nikolay Kutuzov, Sune Darkner, Maurits Jørring Pallesen, Martin Johannes Lauritzen, Kenny Erleben, Francois Lauze
Date: 2023-03-17 00:00:00

Tracking single molecules is instrumental for quantifying the transport of molecules and nanoparticles in biological samples, e.g., in brain drug delivery studies. Existing intensity-based localisation methods are not developed for imaging with a scanning microscope, typically used for in vivo imaging. Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules. Using data-driven models is challenging due to low data volumes, typical for in vivo experiments. We developed a 2PM image simulator to supplement scarce training data. The simulator mimics realistic motion blur, background fluorescence, and shot noise observed in vivo imaging. Training a data-driven model with simulated data improves localisation quality in simulated images and shows why intensity-based methods fail.
Reconfigurable Electromagnetic Environments: A General Framework
Authors: Davide Dardari
Date: 2023-03-30 00:00:00

The recent introduction of the smart radio environments (SREs) paradigm, enabled by reconfigurable intelligent surfaces (RISs), has put in evidence the need for physically-consistent models and design tools for communication systems integrating electromagnetic (EM) and signal processing theories. In this perspective, starting from rigorous EM arguments, in this paper we propose a general framework for the characterization and design of programmable EM environments. We first show that any linear EM environment in the presence of boundary conditions can be interpreted as a space-variant linear feedback filter. Then we provide a methodology to characterize programmable EM systems as a linear graph described by matrix operators thus leading to the determination of the transfer function of the EM system. Finally, some examples are given related to the characterization and design of RISs, also showing that some previous results in the literature are just particular cases of our general framework.
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation
Authors: Tomer Gafni, Kobi Cohen, Yonina C. Eldar
Date: 2023-03-30 00:00:00
Tasks: Federated Learning

Federated learning (FL) is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Recently, over-the-air (OTA) FL has been suggested to reduce the bandwidth and energy consumption, by allowing the users to transmit their data simultaneously over a Multiple Access Channel (MAC). However, this approach results in channel noise directly affecting the optimization procedure, which may degrade the accuracy of the trained model. In this paper we jointly exploit the prior distribution of local weights and the channel distribution, and develop an OTA FL algorithm based on a Bayesian approach for signal aggregation. Our proposed algorithm, dubbed Bayesian Air Aggregation Federated learning (BAAF), is shown to effectively mitigate noise and fading effects induced by the channel. To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm. In addition to using a Bayesian approach to average the channel output, COBAAF controls the drift in local updates using a judicious design of correction terms. We analyze the convergence of the learned global model using BAAF and COBAAF in noisy and heterogeneous environment, showing their ability to achieve a convergence rate similar to that achieved over error-free channels. Simulation results demonstrate the improved convergence of BAAF and COBAAF over existing algorithms in machine learning tasks.
Probabilistic Shaping for High-Speed Unamplified IM/DD Systems with an O-Band EML
Authors: Md Sabbir-Bin Hossain, Georg Bocherer, Talha Rahman, Tom Wettlin, Nebojsa Stojanovic, Stefano Calabro, Stephan Pachnicke
Date: 2023-03-30 00:00:00

Probabilistic constellation shaping has been used in long-haul optically amplified coherent systems for its capability to approach the Shannon limit and realize fine rate granularity. The availability of high-bandwidth optical-electronic components and the previously mentioned advantages have invigorated researchers to explore probabilistic shaping (PS) in intensity-modulation and direct-detection (IM/DD) systems. This article presents an extensive comparison of uniform 8-ary pulse amplitude modulation (PAM) with PS PAM-8 using cap and cup Maxwell-Boltzmann (MB) distributions as well as MB distributions of different Gaussian orders. We report that in the presence of linear equalization, PS-PAM-8 outperforms uniform PAM-8 in terms of bit error ratio, achievable information rate and operational net bit rate indicating that cap-shaped PS-PAM-8 shows high tolerance against nonlinearities. In this paper, we have focused our investigations on O-band electro-absorption modulated laser unamplified IM/DD systems, which are operated close to the zero dispersion wavelength.
A Joint Model and Data Driven Method for Distributed Estimation
Authors: Meng He, Ran Li, Chuan Huang, Shulong Zhang
Date: 2023-03-30 00:00:00
Tasks: Quantization, Weather Forecasting

This paper considers the problem of distributed estimation in wireless sensor networks (WSN), which is anticipated to support a wide range of applications such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation method by designing the optimal quantizers and fusion center (FC) based on the Bayesian and minimum mean square error (MMSE) criterions. First, universal mean square error (MSE) lower bound for the quantization-based distributed estimation is derived and adopted as the design metric for the quantizers. Then, the optimality of the mean-fusion operation for the FC with MMSE criterion is proved. Next, by exploiting different levels of the statistic information of the desired parameter and observation noise, a joint model and data driven method is proposed to train parts of the quantizer and FC modules as deep neural networks (DNNs), and two loss functions derived from the MMSE criterion are adopted for the sequential training scheme. Furthermore, we extend the above results to the case with multi-bit quantizers, considering both the parallel and one-hot quantization schemes. Finally, simulation results reveal that the proposed method outperforms the state-of-the-art schemes in typical scenarios.
A Novel Pre-Coding Based PAPR Reduction Scheme for IM/DD Systems
Authors: Idris Cinemre, Gokce Hacioglu
Date: 2023-03-29 00:00:00

Orthogonal frequency division multiplexing (OFDM) is critical for high-speed visible light communication (VLC) transmission; however, it suffers from a high peak-to-average power ratio (PAPR) problem. Among PAPR reduction techniques, pre-coding methods have shown promising advantages such as signal independence and no requirement for signaling overhead. In this paper, we present a novel pre-coding method that combines discrete cosine transform (DCT) and discrete sine transform (DST) for VLC systems using OFDM. Our proposed approach, tailored specifically for intensity modulation and direct detection (IM/DD) systems, aims to further improve the PAPR reduction while preserving bit error rate (BER) performance. Simulation results demonstrate that the proposed method achieves more than a 6 dB reduction in PAPR compared to the DCT pre-coded method without significant degradation in BER performance.
Assessing the Socio-economic Impacts of Secure Texting and Anti-Jamming Technologies in Non-Cooperative Networks
Authors: Osoro B Ogutu, Edward J Oughton, Kai Zeng, Brian L. Mark
Date: 2023-03-29 00:00:00

Operating securely over 5G (and legacy) infrastructure is a challenge. In non-cooperative networks, malicious actors may try to decipher, block encrypted messages, or specifically jam wireless radio systems. Such activities can disrupt operations, from causing minor inconvenience, through to fully paralyzing the functionality of critical infrastructure. While technological mitigation measures do exist, there are very few methods capable of assessing the socio-economic impacts from different mitigation strategies. This leads to a lack of robust evidence to inform cost-benefit analysis, and thus support decision makers in industry and government. Consequently, this paper presents two open-source simulation models for assessing the socio-economic impacts of operating in untrusted non-cooperative networks. The first focuses on using multiple non-cooperative networks to transmit a message. The second model simulates a case where a message is converted into alternative plain language to avoid detection, separated into different portions and then transmitted over multiple non-cooperative networks. A probabilistic simulation of the two models is performed for a 15 km by 15 km spatial grid with 5 untrusted non-cooperative networks and intercepting agents. The results are used to estimate economic losses for private, commercial, government and military sectors. The highest probabilistic total losses for military applications include US$300, US$150, and US$75, incurred for a 1, 3 and 5 site multi-transmission approach, respectively, for non-cooperative networks when considering 1,000 texts being sent. These results form a framework for deterministic socio-economic impact analysis of using non-cooperative networks and secure texting as protection against radio network attacks. The simulation data and the open-source codebase is provided for reproducibility.