Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning

Cellular-connected unmanned aerial vehicles (UAVs) have gained increasing attention due to their potential to enhance conventional UAV capabilities by leveraging existing cellular infrastructure for reliable communications between UAVs and base stations. They have been used for various applications, including weather forecasting and search and rescue operations. However, under extreme weather conditions such as rainfall, it is challenging for the trajectory design of cellular UAVs, due to weak coverage regions in the sky, limitations of UAV flying time, and signal attenuation caused by raindrops. To this end, this paper proposes a physics-based trajectory design approach for cellular-connected UAVs in rainy environments. A physics-based electromagnetic simulator is utilized to take into account detailed environment information and the impact of rain on radio wave propagation. The trajectory optimization problem is formulated to jointly consider UAV flying time and signal-to-interference ratio, and is solved through a Markov decision process using deep reinforcement learning algorithms based on multi-step learning and double Q-learning. Optimal UAV trajectories are compared in examples with homogeneous atmosphere medium and rain medium. Additionally, a thorough study of varying weather conditions on trajectory design is provided, and the impact of weight coefficients in the problem formulation is discussed. The proposed approach has demonstrated great potential for UAV trajectory design under rainy weather conditions.
Distraction-free Embeddings for Robust VQA

The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding (VLU) tasks such as Video Question Answering (VQA). However, most existing methods for VLU focus on sparsely sampling or fine-graining the input information (e.g., sampling a sparse set of frames or text tokens), or adding external knowledge. We present a novel"DRAX: Distraction Removal and Attended Cross-Alignment"method to rid our cross-modal representations of distractors in the latent space. We do not exclusively confine the perception of any input information from various modalities but instead use an attention-guided distraction removal method to increase focus on task-relevant information in latent embeddings. DRAX also ensures semantic alignment of embeddings during cross-modal fusions. We evaluate our approach on a challenging benchmark (SUTD-TrafficQA dataset), testing the framework's abilities for feature and event queries, temporal relation understanding, forecasting, hypothesis, and causal analysis through extensive experiments.
L ARGE C ONTENT AND B EHAVIOR M ODELS TO U N - DERSTAND , S IMULATE , AND O PTIMIZE C ONTENT AND B EHAVIOR

Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of “behavior tokens” in LLMs’ training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, etc. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior. Equal Contribution. Contact ykumar@adobe.com for questions and suggestions. 1 ar X iv :2 30 9. 00 35 9v 2 [ cs . C L ] 8 S ep 2 02 3
Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution

Using diffusion models to solve inverse problems is a growing field of research. Current methods assume the degradation to be known and provide impressive results in terms of restoration quality and diversity. In this work, we leverage the efficiency of those models to jointly estimate the restored image and unknown parameters of the degradation model. In particular, we designed an algorithm based on the well-known Expectation-Minimization (EM) estimation method and diffusion models. Our method alternates between approximating the expected log-likelihood of the inverse problem using samples drawn from a diffusion model and a maximization step to estimate unknown model parameters. For the maximization step, we also introduce a novel blur kernel regularization based on a Plug \&Play denoiser. Diffusion models are long to run, thus we provide a fast version of our algorithm. Extensive experiments on blind image deblurring demonstrate the effectiveness of our method when compared to other state-of-the-art approaches.
Construction Grammar and Artificial Intelligence

In this chapter, we argue that it is highly beneficial for the contemporary construction grammarian to have a thorough understanding of the strong relationship between the research fields of construction grammar and artificial intelligence. We start by unravelling the historical links between the two fields, showing that their relationship is rooted in a common attitude towards human communication and language. We then discuss the first direction of influence, focussing in particular on how insights and techniques from the field of artificial intelligence play an important role in operationalising, validating and scaling constructionist approaches to language. We then proceed to the second direction of influence, highlighting the relevance of construction grammar insights and analyses to the artificial intelligence endeavour of building truly intelligent agents. We support our case with a variety of illustrative examples and conclude that the further elaboration of this relationship will play a key role in shaping the future of the field of construction grammar.
Planning for Accessibility: in Theory and in Practice

Pick up a transportation plan for a major metropolitan area in the US and chances are you are likely to find improved mobility and accessibility highlighted as goals. For example, the 2020 Regional Transportation Plan for the Austin region stated that, “The primary goal of the CAMPO 2020 Plan is to provide an acceptable level of mobility and accessibility for the region’s residents with the least detrimental effects. ” The 2020 Regional Transportation Plan for the Chicago region aimed to “provide an integrated and coordinated transportation system that maximizes accessibility and includes a variety of mobility options that serve the needs of residents and businesses in the region. ” Such statements are likely influenced by the Transportation Equity Act for the 21st Century, which is commonly know as TEA-21 that established seven planning factors for consideration in the transportation planning process, including “increasing the accessibility and mobility options available to people and freight. ” What exactly these plans mean by mobility and accessibility is not clear, nor whether the agencies that are writing these plans themselves have a clear sense of what they mean in using these terms. If so, they may have missed an important opportunity to clarify their objectives and direct their planning efforts more effectively. The position of this paper is that mobility and accessibility are distinct concepts with vastly different implications for planning. First, the paper looks at these concepts in theory, articulating a distinction between mobility and accessibility and the outlines the possible implications of planning for mobility versus planning for accessibility. Second, the paper looks at the current use of these concepts in practice, by examining a sample of regional transport plans. This exploration yields a mixed picture: although these plans continue to reflect a traditional concern with mobility, they also show many indications of a concern with planning for accessibility, even if they don’t label their efforts as such.
Deep Segmented DMP Networks for Learning Discontinuous Motions

Discontinuous motion which is a motion composed of multiple continuous motions with sudden change in direction or velocity in between, can be seen in state-aware robotic tasks. Such robotic tasks are often coordinated with sensor information such as image. In recent years, Dynamic Movement Primitives (DMP) which is a method for generating motor behaviors suitable for robotics has garnered several deep learning based improvements to allow associations between sensor information and DMP parameters. While the implementation of deep learning framework does improve upon DMP's inability to directly associate to an input, we found that it has difficulty learning DMP parameters for complex motion which requires large number of basis functions to reconstruct. In this paper we propose a novel deep learning network architecture called Deep Segmented DMP Network (DSDNet) which generates variable-length segmented motion by utilizing the combination of multiple DMP parameters predicting network architecture, double-stage decoder network, and number of segments predictor. The proposed method is evaluated on both artificial data (object cutting & pick-and-place) and real data (object cutting) where our proposed method could achieve high generalization capability, task-achievement, and data-efficiency compared to previous method on generating discontinuous long-horizon motions.
On representation equivalence and multiplicity measure for lattices in Lie Groups

We prove an analogue of the strong multiplicity one theorem in the context of finite covolume lattices in the group $G ={ \rm SO}(2,1)^\circ$. We show that the multiplicity measures of two lattices are same if they agree outside a finite measure subset of $\widehat G$.
Mathematical models of Plasmodium vivax transmission: A scoping review

Plasmodium vivax is one of the most geographically widespread malaria parasites in the world, primarily found across South-East Asia, Latin America, and parts of Africa. One of the significant characteristics of the P. vivax parasite is its ability to remain dormant in the human liver as hypnozoites and subsequently reactivate after the initial infection (i.e. relapse infections). Mathematical modelling approaches have been widely applied to understand P. vivax dynamics and predict the impact of intervention outcomes. Models that capture P. vivax dynamics differ from those that capture P. falciparum dynamics, as they must account for relapses caused by the activation of hypnozoites. In this article, we provide a scoping review of mathematical models that capture P. vivax transmission dynamics published between January 1988 and May 2023. The primary objective of this work is to provide a comprehensive summary of the mathematical models and techniques used to model P. vivax dynamics. In doing so, we aim to assist researchers working on mathematical epidemiology, disease transmission, and other aspects of P. vivax malaria by highlighting best practices in currently published models and highlighting where further model development is required. We categorise P. vivax models according to whether a deterministic or agent-based approach was used. We provide an overview of the different strategies used to incorporate the parasite’s biology, use of multiple scales (within-host and population-level), superinfection, immunity, and treatment interventions. In most of the published literature, the rationale for different modelling approaches was driven by the research question at hand. Some models focus on the parasites’ complicated biology, while others incorporate simplified assumptions to avoid model complexity. Overall, the existing literature on mathematical models for P. vivax encompasses various aspects of the parasite’s dynamics. We recommend that future research should focus on refining how key aspects of P. vivax dynamics are modelled, including spatial heterogeneity in exposure risk and heterogeneity in susceptibility to infection, the accumulation of hypnozoite variation, the interaction between P. falciparum and P. vivax, acquisition of immunity, and recovery under superinfection.
Intuitionistic Gödel-Löb logic, à la Simpson: labelled systems and birelational semantics

We derive an intuitionistic version of G\"odel-L\"ob modal logic ($\sf{GL}$) in the style of Simpson, via proof theoretic techniques. We recover a labelled system, $\sf{\ell IGL}$, by restricting a non-wellfounded labelled system for $\sf{GL}$ to have only one formula on the right. The latter is obtained using techniques from cyclic proof theory, sidestepping the barrier that $\sf{GL}$'s usual frame condition (converse well-foundedness) is not first-order definable. While existing intuitionistic versions of $\sf{GL}$ are typically defined over only the box (and not the diamond), our presentation includes both modalities. Our main result is that $\sf{\ell IGL}$ coincides with a corresponding semantic condition in birelational semantics: the composition of the modal relation and the intuitionistic relation is conversely well-founded. We call the resulting logic $\sf{IGL}$. While the soundness direction is proved using standard ideas, the completeness direction is more complex and necessitates a detour through several intermediate characterisations of $\sf{IGL}$.
Multiple closed geodesics on Finsler $3$-dimensional sphere

In 1973, Katok constructed a non-degenerate (also called bumpy) Finsler metric on $S^3$ with exactly four prime closed geodesics. And then Anosov conjectured that four should be the optimal lower bound of the number of prime closed geodesics on every Finsler $S^3$. In this paper, we proved this conjecture for bumpy Finsler $S^{3}$ if the Morse index of any prime closed geodesic is nonzero.
Evaluation of Sun Glint Correction Algorithms for High-Spatial Resolution Hyperspectral Imagery

Abstract : Sun glint correction algorithms were tested on a hyperspectral image containing cross-track sun glint. Spatial profiles of pixel radiance by pixel position were compared and slope values were calculated. The algorithms of Hedley et al., Lyzenga et al., and Joyce over-corrected for sun glint in the visible and near-infrared wavelengths. The method proposed by Kuster et al. was the weakest performer during visual comparison with the other method results. Spectral plots of corrected spectra to the original spectra were compared. Comparisons were performed on spectra from pixels with low and high amounts of sun glint. Spectra were compared within the sun glint corrected images and between the corrected images and the original image. Correlation values were calculated for each spectral comparison and averaged for each sun glint correction algorithm. The Lyzenga et al. sun glint correction algorithm had the highest average correlation value of 0.977 and is recommended for reducing sun glint in hyperspectral imagery when spectral integrity is required.
Providing Realtime Support for Containerized Edge Services

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, providing real-time support at the containerized edge systems (CESs) remains a challenge. In this paper, we propose EdgeMan, a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Additionally, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of time-sensitive services run with EdgeMan is reduced by 85.9% on average compared with that of existing methods in both industry and academia.
Terrestrial Hydrological Data from NASA's Hydrology Data and Information Services Center (HDISC): Products, Services, and Applications

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. The North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) have been generating a series of land surface states (soil moisture, snow, and temperature) and fluxes (evapotranspiration, radiation, and heat flux) variables. These data, hosted at and available from NASA’s Hydrology Data and Information Services Center (HDISC), include the NLDAS hourly 1/8 degree products and the GLDAS 3-hourly 0.25 and 1.0 degree products. HDISC provides easy access and visualization and analysis capabilities for these products, thus reducing the time and resources spent by scientists on data management and facilitating hydrological research. Users can perform spatial and parameter subsetting, data format transformation, and data analysis operations without needing to first download the data. HDISC is continually being developed as a data and services portal that supports weather and climate forecasts, and water and energy cycle research.
Towards Efficient Universal Neural Machine Translation

Humans benefit from communication but suffer from language barriers. Machine translation (MT) aims to overcome such barriers by automatically transforming information from one language to another. With the rapid development of deep neural networks, neural machine translation (NMT) – especially Transformer (Vaswani et al., 2017) – has achieved great success in recent years, delivering state-of-the-art and even near human performance on many bilingual text-based translation tasks (Akhbardeh et al., 2021). However, challenges remain particularly in 1) efficiency where a massive NMT model is a computational bottleneck for training and decoding, and 2) universality where extending NMT beyond bilingual and textbased scenarios (such as multilingual and speechto-text translation) is still non-trivial. In this thesis, we investigate ways of developing simple and effective neural architectures to address these two challenges. NMT is resource-hungry. Achieving highquality translation demands complex network architectures and a large number of model parameters, which often takes hundreds or even thousands of training GPU hours and leads to slow inference. We tackle this computational inefficiency issue via three aspects: 1) simplifying model architectures, where we propose a lightweight recurrent network and root mean square layer normalization to enable higher model parallelization, as well as a merged attention network paired with depth-scaled initialization to improve deep Transformer; 2) exploring representation redundancy, where we demonstrate the feasibility of sparsifying encoder outputs
Quality in Human and Machine Translation: An Interdisciplinary Survey

Quality assurance is a central component of human and machine translation. In translation studies, translation quality focuses on human evaluation and dimensions, such as purpose, comprehensibility, target audience among many more. Within the field of machine translation, more operationalized definitions of quality lead to automated metrics relying on reference translations or quality estimation. A joint approach to defining and assessing translation quality holds the promise to be mutually beneficial. To contribute towards that objective, this systematic survey provides an interdisciplinary analysis of the concept of translation quality from both perspectives. Thereby, it seeks to inspire cross-fertilization between both fields and further development of an interdisciplinary concept of translation quality.
HPLT: High Performance Language Technologies

We describe the High Performance Language Technologies project (HPLT), a 3-year EU-funded project started in September 2022. HPLT will build a space combining petabytes of natural language data with large-scale model training. It will derive monolingual and bilingual datasets from the Internet Archive and CommonCrawl and build efficient and solid machine translation (MT) as well as large language models (LLMs). HPLT aims at providing free, sustainable and reusable datasets, models and workflows at scale using high-performance computing (HPC).
Improving Machine Translation in the E-commerce Luxury Space. A case study

This case study presents a Multilingual e-commerce Project, which principal aim is to create an improved system that translates product titles and descriptions, plus other content in multiple languages. The project consisted of two main phases; a research-intensive solution using state-of-the-art Machine Translation systems and baseline language models for two language pairs, and the development of a Machine Translation system. The features implemented included Quality Estimation, model benchmarking, entity recognizers, and automatic domain detection. mBART model was used to create the system for the specific domain of e-commerce, for luxury items.
GoSt-ParC-Sign: Gold Standard Parallel Corpus of Sign and spoken language

Good quality training data for Sign Language Machine Translation (SLMT) is extremely scarce, and this is one of the challenges that any project focusing on Machine Translation (MT) which also targets sign languages is currently facing. The goal of this ongoing project is to create a parallel corpus of authentic Flemish Sign Language (VGT) and written Dutch which can be employed as gold standard in automated sign language translation. The availability of a gold standard corpus like Gost-ParC-Sign can facilitate the advances of SLMT; consequently, it supports and promotes inclusiveness in MT and, on a more general level, in language technology