Miracle-dz/UniFAR-FTUs huggingface dataset:
Blue Skies The Next Grand Challenges Integrating the Internet of Things and Data Science

In the last decade, we have been transitioning from a datapoor to a data-rich world with the promise of unparalleled intelligence. Such transition will definitely require significant investments in every aspect in our societies including social, political, economic and cultural. Much of the (unprecedented) increase in data generation can be attributed to the abundance of mobile devices and wearables, the increase of instrumentation in every industry vertical, the mass adoption of social networks and the digitization of every aspect of our lives. Generically, the bulk of such data collection falls under the Internet of Things (IoT). 1–5 IoT data comes from a variety of sources that can be classified into (a) machine-based (e.g., environmental, weather, air quality, water quality, flows, traffic speeds, people flows and GPS location) or (b) people-based (e.g., social media, crowd sourced data collection, and simple text messaging) providing data and situational observations associated with events.
Characterizations of Controlled Generation of Right Linear Grammars with Unknown Behaviors

This paper deals with the control generation of right linear grammars with unknown behaviors (RLUBs, for short) in which derivation behavior is not determined completely. In particular, we consider a physical property of control devices used in control systems and formulate it as a partial order over control alphabet of the control system. We give necessary and sufficient conditions for given finite language classes to be generated by RLUBs and their control systems using a given partial order over control alphabet.
NLPre: a revised approach towards language-centric benchmarking of Natural Language Preprocessing systems

With the advancements of transformer-based architectures, we observe the rise of natural language preprocessing (NLPre) tools capable of solving preliminary NLP tasks (e.g. tokenisation, part-of-speech tagging, dependency parsing, or morphological analysis) without any external linguistic guidance. It is arduous to compare novel solutions to well-entrenched preprocessing toolkits, relying on rule-based morphological analysers or dictionaries. Aware of the shortcomings of existing NLPre evaluation approaches, we investigate a novel method of reliable and fair evaluation and performance reporting. Inspired by the GLUE benchmark, the proposed language-centric benchmarking system enables comprehensive ongoing evaluation of multiple NLPre tools, while credibly tracking their performance. The prototype application is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark. Based on this benchmark, we conduct an extensive evaluation of a variety of Polish NLPre systems. To facilitate the construction of benchmarking environments for other languages, e.g. NLPre-GA for Irish or NLPre-ZH for Chinese, we ensure full customization of the publicly released source code of the benchmarking system. The links to all the resources (deployed platforms, source code, trained models, datasets etc.) can be found on the project website: https://sites.google. com/view/nlpre-benchmark.
Matrix decompositions in Quantum Optics: Takagi/Autonne, Bloch-Messiah/Euler, Iwasawa, and Williamson

In this note we summarize four important matrix decompositions commonly used in quantum optics, namely the Takagi/Autonne, Bloch-Messiah/Euler, Iwasawa, and Williamson decompositions. The first two of these decompositions are specialized versions of the singular-value decomposition when applied to symmetric or symplectic matrices. The third factors any symplectic matrix in a unique way in terms of matrices that belong to different subgroups of the symplectic group. The last one instead gives the symplectic diagonalization of real, positive definite matrices of even size. While proofs of the existence of these decompositions exist in the literature, we focus on providing explicit constructions to implement these decompositions using standard linear algebra packages and functionalities such as singular-value, polar, Schur and QR decompositions, and matrix square roots and inverses.
Quantum-enhanced joint estimation of phase and phase diffusion

Accurate phase estimation in the presence of unknown phase diffusive noise is a crucial yet challenging task in noisy quantum metrology. This problem is particularly interesting due to the detrimental impact of the associated noise. Here, we investigate the joint estimation of phase and phase diffusion using generalized Holland-Burnett states, known for their experimental accessibility. These states provide performance close to the optimal state in single-parameter phase estimation, even in the presence of photon losses. We adopt a twofold approach by analyzing the joint information extraction through the double homodyne measurement and the joint information availability across all probe states. Through our analysis, we find that the highest sensitivities are obtained by using states created by directing all input photons into one port of a balanced beam splitter. Furthermore, we infer that good levels of sensitivity persist even in the presence of moderate photon losses, illustrating the remarkable resilience of our probe states under lossy conditions.
A contact map method to capture the features of knot conformations

Inspired by recent advances in the chromosome capture techniques, a method is proposed to study the structural organization of systems of polymers rings with topological constraints.To this purpose, the system is divided into compartments and a simple condition is provided in order to determine if two compartments are in contact or not. Next, a set of contact matrices $\bar T_{ab}$ is defined that count how many times during a simulation a compartment $a$ was found in contact with a non-contiguous compartment $b$ in conformations with a given energy or temperature. Similar strategies based on correlation maps have been applied to the study of knotted polymers in the recent past. The advantage of the present approach is that is coupled with the Wang-Landau algorithm. Once the density of states is computed, it is possible to generate the contact matrices at any temperature. This gives an immediate overview over the changes of phases that polymer systems undergo. The information on the structure of knotted polymers and links stored in the contact matrices is the result of averaging hundred of billions of conformations and visualized by means of colormaps. The obtained color patterns allow to identify the main properties of the structure of the system under investigation at any temperature. The method is applied to detect the structural rearrangements following the phase transitions of a knotted polymer ring and a circular polycatenane composed by four rings in a solution. It is shown that the colormaps have a finite number of patterns that can be clearly associated with the different phases of these systems. Colormaps also bring new knowledge, for instance predicting the average number of tails appearing in the conformations of the considered polymers at a given temperature.
Discriminative Probing and Tuning for Text-to-Image Generation

Despite advancements in text-to-image generation (T2I), prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However, the inherent alignment capabilities of T2I models are still inadequate. By reviewing the link between generative and discriminative modeling, we posit that T2I models' discriminative abilities may reflect their text-image alignment proficiency during generation. In this light, we advocate bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment. As a bonus of the discriminative adapter, a self-correction mechanism can leverage discriminative gradients to better align generated images to text prompts during inference. Comprehensive evaluations across three benchmark datasets, including both in-distribution and out-of-distribution scenarios, demonstrate our method's superior generation performance. Meanwhile, it achieves state-of-the-art discriminative performance on the two discriminative tasks compared to other generative models.
Potential inundated coastal area estimation in Shanghai with multi-platform SAR and altimetry data

As global warming problem is becoming serious in recent decades, the global sea level is continuously rising. This will cause damages to the coastal deltas with the characteristics of low-lying land, dense population, and developed economy. Continuously reclamation costal intertidal and wetland areas are making Shanghai, the mega city of Yangtze River Delta, more vulnerable to sea level rise. In this paper, we investigate the land subsidence temporal evolution of patterns and processes on a stretch of muddy coast located between the Yangtze River Estuary and Hangzou Bay with differential synthetic aperture radar interferometry (DInSAR) analyses. By exploiting a set of 31 SAR images acquired by the ENVISAT/ASAR from February 2007 to May 2010 and a set of 48 SAR images acquired by the COSMO-SkyMed (CSK) sensors from December 2013 to March 2016, coherent point targets as long as land subsidence velocity maps and time series are identified by using the Small Baseline Subset (SBAS) algorithm. With the DInSAR constrained land subsidence model, we predict the land subsidence trend and the expected cumulative subsidence in 2020, 2025 and 2030. Meanwhile, we used altimetrydata and densely distributed in the coastal region are identified (EEMD) algorithm to obtain the average sea level rise rate in the East China Sea. With the land subsidence predictions, sea level rise predictions, and high-precision digital elevation model (DEM), we analyze the combined risk of land subsidence and sea level rise on the coastal areas of Shanghai. The potential inundated areas are mapped under different scenarios.
Asymptotic Theory for Linear Functionals of Kernel Ridge Regression

An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert space is equivalent to a Sobolev space. The theory covers a wide variety of linear functionals, including point evaluations, evaluation of derivatives, $L_2$ inner products, etc. We establish the upper and lower bounds of the estimates and their asymptotic normality. It is shown that $\lambda\sim n^{-1}$ is the universal optimal order of magnitude for the smoothing parameter to balance the variance and the worst-case bias. The theory also implies that the optimal $L_\infty$ error of kernel ridge regression can be attained under the optimal smoothing parameter $\lambda\sim n^{-1}\log n$. These optimal rates for the smoothing parameter differ from the known optimal rate $\lambda\sim n^{-\frac{2m}{2m+d}}$ that minimizes the $L_2$ error of the kernel ridge regression.
Scalable, Simulation-Guided Compliant Tactile Finger Design

Compliant grippers enable robots to work with humans in unstructured environments. In general, these grippers can improve with tactile sensing to estimate the state of objects around them to precisely manipulate objects. However, co-designing compliant structures with high-resolution tactile sensing is a challenging task. We propose a simulation framework for the end-to-end forward design of GelSight Fin Ray sensors. Our simulation framework consists of mechanical simulation using the finite element method (FEM) and optical simulation including physically based rendering (PBR). To simulate the fluorescent paint used in these GelSight Fin Rays, we propose an efficient method that can be directly integrated in PBR. Using the simulation framework, we investigate design choices available in the compliant grippers, namely gel pad shapes, illumination conditions, Fin Ray gripper sizes, and Fin Ray stiffness. This infrastructure enables faster design and prototype time frames of new Fin Ray sensors that have various sensing areas, ranging from 48 mm $\times$ \18 mm to 70 mm $\times$ 35 mm. Given the parameters we choose, we can thus optimize different Fin Ray designs and show their utility in grasping day-to-day objects.
Federated Recommendation via Hybrid Retrieval Augmented Generation

Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discrete identities (IDs), suffering from performance degradation due to the data sparsity and heterogeneity in FR. On the other hand, Large Language Models (LLMs) as recommenders have proven effective across various recommendation scenarios. Yet, LLM-based recommenders encounter challenges such as low inference efficiency and potential hallucination, compromising their performance in real-world scenarios. To this end, we propose GPT-FedRec, a federated recommendation framework leveraging ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism. GPT-FedRec is a two-stage solution. The first stage is a hybrid retrieval process, mining ID-based user patterns and text-based item features. Next, the retrieved results are converted into text prompts and fed into GPT for re-ranking. Our proposed hybrid retrieval mechanism and LLM-based re-rank aims to extract generalized features from data and exploit pretrained knowledge within LLM, overcoming data sparsity and heterogeneity in FR. In addition, the RAG approach also prevents LLM hallucination, improving the recommendation performance for real-world users. Experimental results on diverse benchmark datasets demonstrate the superior performance of GPT-FedRec against state-of-the-art baseline methods.
Expanding CGL: The CGL double-adiabatic approximation in the Expanding Solar Wind

Different in situ satellite observations within 0.3 to 1 AU from the Sun reveal deviations in the thermodynamics of solar wind expansion. Specifically, these deviations challenge the applicability of the double adiabatic or CGL theory, indicating potential influences such as perpendicular heating and/or parallel cooling of ions. The study aims to investigate the plasma expansion phenomena using the Expanding Box Model (EBM) coupled with an ideal MHD description of the plasma. The primary objective is to understand the observed deviations from the CGL predictions, and how the expansion can affect the conservation of the adiabatic invariants, particularly focusing on the impact of transverse expansion on the CGL equations. To address the plasma expansion, we employed the Expanding Box Model (EBM) coupled with the ideal-MHD formalism used for CGL theory. This model provides a unique system of reference co-moving with the solar wind, allowing for the incorporation of transverse expansion into the double adiabatic equations. Solving the equations for different magnetic field profiles, we compute the evolution of anisotropy and plasma beta, which deviates from CGL predictions and empirical observations. This deviation is attributed to the plasma cooling effect induced by the Expanding Box Model (EBM). Results suggest that heating mechanisms play a crucial role in counteracting plasma cooling during expansion.
Density and Affinity Dependent Social Segregation and Arbitrage Equilibrium in a Multi-class Schelling Game

Contrary to the widely believed hypothesis that larger, denser cities promote socioeconomic mixing, a recent study (Nilforoshan et al. 2023) reports the opposite behavior, i.e. more segregation. Here, we present a game-theoretic model that predicts such a density-dependent segregation outcome in both one- and two-class systems. The model provides key insights into the analytical conditions that lead to such behavior. Furthermore, the arbitrage equilibrium outcome implies the equality of effective utilities among all agents. This could be interpreted as all agents being equally"happy"in their respective environments in our ideal society. We believe that our model contributes towards a deeper mathematical understanding of social dynamics and behavior, which is important as we strive to develop more harmonious societies.
Super-resolution on network telemetry time series

Fine-grained monitoring is crucial for multiple data-driven tasks such as debugging, provisioning, and securing networks. Yet, practical constraints in collecting, extracting, and storing data often force operators to use coarse-grained sampled monitoring, degrading the performance of the various tasks. In this work, we explore the feasibility of leveraging the correlations among coarse-grained time series to impute their fine-grained counterparts in software. We present Zoom2Net, a transformer-based model for network imputation that incorporates domain knowledge through operational and measurement constraints, ensuring that the imputed network telemetry time series are not only realistic but also align with existing measurements and are plausible. This approach enhances the capabilities of current monitoring infrastructures, allowing operators to gain more insights into system behaviors without the need for hardware upgrades. We evaluate Zoom2Net on four diverse datasets (e.g. cloud telemetry and Internet data transfer) and use cases (such as bursts analysis and traffic classification). We demonstrate that Zoom2Net consistently achieves high imputation accuracy with a zoom-in factor of up to 100 and performs better on downstream tasks compared to baselines by an average of 38%.
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks

Graph anomaly detection plays a vital role for identifying abnormal instances in complex networks. Despite advancements of methodology based on deep learning in recent years, existing benchmarking approaches exhibit limitations that hinder a comprehensive comparison. In this paper, we revisit datasets and approaches for unsupervised node-level graph anomaly detection tasks from three aspects. Firstly, we introduce outlier injection methods that create more diverse and graph-based anomalies in graph datasets. Secondly, we compare methods employing message passing against those without, uncovering the unexpected decline in performance associated with message passing. Thirdly, we explore the use of hyperbolic neural networks, specifying crucial architecture and loss design that contribute to enhanced performance. Through rigorous experiments and evaluations, our study sheds light on general strategies for improving node-level graph anomaly detection methods.
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation

The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods still rely on single-view images as input, leading to potential limitations, e.g., limited field-of-view and ambiguity in depth. To address these problems, adding another camera to better capture the shape of hands is a practical direction. However, existing multi-view hand pose estimation methods suffer from two main drawbacks: 1) Requiring multi-view annotations for training, which are expensive. 2) During testing, the model becomes inapplicable if camera parameters/layout are not the same as those used in training. In this paper, we propose a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pre-trained single-view estimator to dual views. Compared with existing multi-view training methods, 1) our adaptation process is unsupervised, eliminating the need for multi-view annotation. 2) Moreover, our method can handle arbitrary dual-view pairs with unknown camera parameters, making the model applicable to diverse camera settings. Specifically, S2DHand is built on certain stereo constraints, including pair-wise cross-view consensus and invariance of transformation between both views. These two stereo constraints are used in a complementary manner to generate pseudo-labels, allowing reliable adaptation. Evaluation results reveal that S2DHand achieves significant improvements on arbitrary camera pairs under both in-dataset and cross-dataset settings, and outperforms existing adaptation methods with leading performance. Project page: https://github.com/MickeyLLG/S2DHand.
Prediction Of Air Pollutant From Poultry Houses By A Modified Gaussian Plume Model

Title of Thesis: PREDICTION OF AIR POLLUTANT FROM POULTRY HOUSES BY A MODIFIED GAUSSIAN PLUME MODEL Zijiang Yang, Master of Science, 2017 Dr. Alba Torrents Department of Civil and Environmental Engineering, University of Maryland Concentrated animal feeding operations release a variety of potential pollutants, such as ammonia and particulate matters (PM). Field measurements are time consuming, costly, and only provide a limited amount of spatial and temporal information. Air dispersion models can serve as an alternative solution, especially if coupled with field sampling. The Gaussian plume model (GPM) is a mathematical model that assumes steady state condition. Previous studies have used the GPM to evaluate and analyze source. However, much less is known about utilizing GPM to simulate plumes from horizontal sources, such as the exhaust fans from poultry houses. The purpose of this study is to modify and validate a GPM to predict air pollutant emissions from the poultry houses. Two major assumptions were applied on the model, 1) a virtual releasing point was proposed behind the ventilation fan, and 2) ventilation fan was considered as the dominant wind direction in the model for short distance (< 50 m). The modified model was validated with field experimental data. Performance and sensitivity of the model were also evaluated. Fraction of predictions within a factor of two of observations (FAC2) of NH3 and PM were 0.609 and 0.625. Model-predicted concentrations of NH3 were 1.5 times of the measured values on average. Model-predicted concentrations of PM was 0.98 times of the observed values on average. PREDICTION OF AIR POLLUTANT FROM POULTRY HOUSES BY A MODIFIED GAUSSIAN PLUME MODEL