Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges in Applying Robotics to Retail Store Management

An autonomous retail store management system entails inventory tracking, store monitoring, and anomaly correction. Recent attempts at autonomous retail store management have faced challenges primarily in perception for anomaly detection, as well as new challenges arising in mobile manipulation for executing anomaly correction. Advances in each of these areas along with system integration are necessary for a scalable solution in this domain.
Towards Daily High-resolution Inundation Observations using Deep Learning and EO

Large-scale flood inundation observations, not only provide crucial information for emergency response and decision-making but also for future preparedness or damage assessment. Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used. While satellites do offer timely inundation information when they happen to cover an ongoing flood event, they are limited by their spatiotemporal resolution in terms of their ability to dynamically monitor flood evolution at various scales. Constantly improving access to new satellite data sources as well as big data processing capabilities has unlocked an unprecedented number of possibilities in terms of data-driven solutions to this problem, specifically using deep-learning algorithms with multi-sensor remotely sensed data. Specifically, the fusion of data from satellites, such as the Copernicus Sentinels, which have high spatial and low temporal resolution, with data from NASA’s SMAP and GPM missions, which have low spatial but high temporal resolutions could yield high-resolution flood inundation at a daily scale. Here a Convolutional-Neural-Network is trained using flood inundation maps derived from Sentinel-1 Synthetic Aperture Radar and various hydrological, topographical, and land-use based predictors for the first time, to predict high-resolution probabilistic maps of flood inundation. The performance of UNet and SegNet model architectures for this task is evaluated, using flood masks derived from Sentinel-1 and Sentinel-2, separately with 95%-confidence intervals. The Area under the Curve (AUC) of the 2 Precision Recall Curve (PR-AUC) is used as the main evaluation metric, due to the inherently imbalanced nature of classes in a binary flood mapping problem, with the best model delivering a PRAUC of ~0.85. Feature importance analysis using the permutation feature importance method, showed low importance of precipitation and soil-moisture, due to the large spatial resolution mismatch and the consequent lack of spatial variability per output pixel. Results from this proof-of-concept study indicate that multi-sensor data fusion could yield daily scale high-resolution flood inundation maps, enabling new possibilities for flash flood and dynamic flood evolution monitoring using satellites.
Wildfire Forecasting with Satellite Images and Deep Generative Model

Wildfire forecasting has been one of the most critical tasks that humanities want to thrive. It plays a vital role in protecting human life. Wildfire prediction, on the other hand, is difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images as a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts is based on stochastic image-autoregressive recurrent networks, which raises various performance and application difficulties, such as computational cost and limited efficiency on massive datasets. Another possibility is to use entirely latent temporal models that combine frame synthesis and temporal dynamics. However, due to design and training issues, no such model for stochastic video prediction has yet been proposed in the literature. This paper addresses these issues by introducing a novel stochastic temporal model whose dynamics are driven in a latent space. It naturally predicts video dynamics by allowing our lighter, more interpretable latent model to beat previous state-of-the-art approaches on the GOES-16 dataset. Results will be compared towards various benchmarking models.
Simulation and inference on purely observational methods of monitoring vaccine effectiveness post-deployment: none is reliable without precise information on population behaviour

Two observational methods are currently being used to monitor post-deployment vaccine effectiveness: the obvious crude method comparing rate testing positive per head of vaccinated population with that rate per head of unvaccinated population; and the test-negative case control (TNCC) method. The two methods give very different results. We want to know whether either method is reliable. We assume either a homogeneous population or one partitioned into two homogeneous subsets which differ only in their not-directly-observable healthcare-seeking behaviour including probability of getting vaccinated. We first consider uniform independent priors on the probabilities of being hospitalised conditional on subset, vaccination status, and infection status. We simulate from the resulting model and observe the TNCC estimate, the crude estimate, and the Bayesian central 95% confidence interval on vaccine effectiveness represented as log ratio of odds ratios for infection with and without vaccination. With these wide open priors, even when the population is homogeneous, the Bayesian 95% confidence interval typically has a width of nearly 4 nats (55-fold), implying too much uncertainty for the data collected to be of any use in monitoring effectiveness. There do exist some tight priors under which the data is useful: some lead to TNCC being more accurate while with others the crude estimate is more accurate. Thus using only data from those spontaneously choosing to be tested, we find that neither method is reliably better than the other, and indeed that the desired information is not present in this data. We conclude that effective monitoring of vaccine effectiveness and side-effects requires either strong information on the population's behaviour, or ongoing randomised controlled trials (RCTs), rather than just choosing whichever of TNCC and crude estimate gives the result we prefer to find.
Pareto optimal fronts of kinetic proofreading

Biological processes such as DNA replication, RNA transcription, and protein translation operate with remarkable speed and accuracy in selecting the right substrate from pools of chemically identical molecules. This result is obtained by non-equilibrium reactions that dissipate chemical energy. It is widely recognized that there must be a trade-off between speed, error, and dissipation characterizing these systems. In this paper, we quantify this trade-off using tools from mathematical optimization theory. We characterize the Pareto optimal front for a generalized version of Hopfield’s kinetic proofreading model, which is a paradigmatic example of biological error correction. We find that models with more proofreading steps are characterized by better trade-offs. Furthermore, we numerically study scaling relations between speed, accuracy, and dissipation on the Pareto front.
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?

Fact-checking is one of the effective solutions in fighting online misinformation. However, traditional fact-checking is a process requiring scarce expert human resources, and thus does not scale well on social media because of the continuous flow of new content to be checked. Methods based on crowdsourcing have been proposed to tackle this challenge, as they can scale with a smaller cost, but, while they have shown to be feasible, have always been studied in controlled environments. In this work, we study the first large-scale effort of crowdsourced fact-checking deployed in practice, started by Twitter with the Birdwatch program. Our analysis shows that crowdsourcing may be an effective fact-checking strategy in some settings, even comparable to results obtained by human experts, but does not lead to consistent, actionable results in others. We processed 11.9k tweets verified by the Birdwatch program and report empirical evidence of i) differences in how the crowd and experts select content to be fact-checked, ii) how the crowd and the experts retrieve different resources to fact-check, and iii) the edge the crowd shows in fact-checking scalability and efficiency as compared to expert checkers.
IAN: Iterated Adaptive Neighborhoods for Manifold Learning and Dimensionality Estimation

Abstract Invoking the manifold assumption in machine learning requires knowledge of the manifold's geometry and dimension, and theory dictates how many samples are required. However, in most applications, the data are limited, sampling may not be uniform, and the manifold's properties are unknown; this implies that neighborhoods must adapt to the local structure. We introduce an algorithm for inferring adaptive neighborhoods for data given by a similarity kernel. Starting with a locally conservative neighborhood (Gabriel) graph, we sparsify it iteratively according to a weighted counterpart. In each step, a linear program yields minimal neighborhoods globally, and a volumetric statistic reveals neighbor outliers likely to violate manifold geometry. We apply our adaptive neighborhoods to nonlinear dimensionality reduction, geodesic computation, and dimension estimation. A comparison against standard algorithms using, for example, k-nearest neighbors, demonstrates the usefulness of our approach.
Large totally symmetric sets

A totally symmetric set is a subset of a group such that every permutation of the subset can be realized by conjugation in the group. The (non-)existence of large totally symmetric sets obstruct homomorphisms, so bounds on the sizes of totally symmetric sets are of particular use. In this paper, we prove that if a group has a totally symmetric set of size $k$, it must have order at least $(k+1)! $. We also show that with three exceptions, $\{(1 \; i)\mid i = 2,\ldots,n\} \subset S_n$ is the only totally symmetric set making this bound sharp; it is thus the largest totally symmetric set relative to the size of the ambient group.
Pseudo-Labels Are All You Need

Automatically estimating the complexity of texts for readers has a variety of applications, such as recommending texts with an appropriate complexity level to language learners or supporting the evaluation of text simplification approaches. In this paper, we present our submission to the Text Complexity DE Challenge 2022, a regression task where the goal is to predict the complexity of a German sentence for German learners at level B. Our approach relies on more than 220,000 pseudolabels created from the German Wikipedia and other corpora to train Transformer-based models, and refrains from any feature engineering or any additional, labeled data. We find that the pseudo-label-based approach gives impressive results yet requires little to no adjustment to the specific task and therefore could be easily adapted to other domains and tasks.
New Velocity Measurements of NGC 5128 Globular Clusters Out to 130 kpc: Outer Halo Kinematics, Substructure, and Dynamics

We present new radial velocity measurements from the Magellan and the Anglo-Australian Telescopes for 175 previously known and 121 newly confirmed globular clusters (GCs) around NGC 5128, the nearest accessible massive early-type galaxy at D = 3.8 Mpc. Remarkably, 28 of these newly confirmed GCs are at projected radii >50′ (≳54 kpc), extending to ∼130 kpc, in the outer halo where few GCs had been confirmed in previous work. We identify several subsets of GCs that spatially trace halo substructures that are visible in red giant branch star maps of the galaxy. In some cases, these subsets of GCs are kinematically cold, and may be directly associated with and originate from these specific stellar substructures. From a combined kinematic sample of 645 GCs, we see evidence for coherent rotation at all radii, with a higher rotation amplitude for the metal-rich GC subpopulation. Using the tracer mass estimator, we measure a total enclosed mass of 2.5 ± 0.3 × 1012 M ⊙ within ∼120 kpc, an estimate that will be sharpened with forthcoming dynamical modeling. The combined power of stellar mapping and GC kinematics makes NGC 5128 an ongoing keystone for understanding galaxy assembly at mass scales inaccessible in the Local Group.
Understanding Volatility Spillover Relationship Among G7 Nations And India During Covid-19 (preprint)

Purpose: In the context of a COVID pandemic in 2020-21, this paper attempts to capture the interconnectedness and volatility transmission dynamics. The nature of change in volatility spillover effects and time-varying conditional correlation among the G7 countries and India is investigated. Methodology: To assess the volatility spillover effects, the bivariate BEKK and t- DCC (1,1) GARCH (1,1) models have been used. Our research shows how the dynamics of volatility spillover between India and the G7 countries shift before and during COVID-19. Findings: The findings reveal that the extent of volatility spillover has altered during COVID compared to the pre-COVID environment. During this pandemic, a sharp increase in conditional correlation indicates an increase in systematic risk between countries. Originality: The study contributes to a better understanding of the dynamics of volatility spillover between G7 countries and India. Asset managers and foreign corporations can use the changing spillover dynamics to improve investment decisions and implement effective hedging measures to protect their interests. Furthermore, this research will assist financial regulators in assessing market risk in the future owing to crises like as COVID-19.
A limit on the polarization of the cosmic microwave background radiation

We describe polarization observations of the CMBR with the Cosmic Background Imager, a 13-element interferometer which operates in the 26-36 GHz band and is located on the Llano de Chajnantor in northern Chile. The array consists of 90 cm Cassegrain antennas mounted on a steerable platform which can be rotated about the optical axis to facilitate polarization observations. The CBI employs single-mode circularly polarized receivers and it samples multipoles from ell~400 to ell~4250. The instrumental polarization of the CBI was calibrated with 3C279, a bright polarized point source which was monitored with the VLA. Observations of two deep fields during the 2000 season yielded three limits (95% c.l.) for C_ell^EE under the assumption that C_ell^BB=0: 7.0 muK (ell=603); 12.8 muK (ell=1144); and 25.1 muK (ell=2048). The low-ell limit approaches the levels of fluctuations predicted by standard models. This thesis also entailed the design and implementation of several major components of the CBI signal chain including the downconverter, the noise calibration system, and the low noise HEMT amplifiers. We discuss the design and performance of these critical systems.
Scaling Blockchains with Error Correction Codes: A Survey on Coded Blockchains

A fundamental issue in blockchain systems is their scalability in terms of data storage, computation, communication, and security. To resolve this issue, a promising research direction is coding theory, which is widely used for distributed storage, recovery from erasures or channel errors and/or to reduce communication cost. To this end, this paper provides the first comprehensive survey of approaches that employ coding theory to scale blockchain systems. It shows how the use of coded symbols or shards allow participants to only store a fraction of the total blockchain, protect against malicious nodes or erasures, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, coded symbols help reduce communication cost when disseminating blocks, which help bootstrap new nodes and speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication and computation cost.
UniCausal: Unified Benchmark and Model for Causal Text Mining

Current causal text mining datasets vary in objectives, data coverage, and annotation schemes. These inconsistent efforts prevented modeling capabilities and fair comparisons of model performance. Few datasets include cause-effect span annotations, which are needed for end-to-end causal extraction. Therefore, we proposed UniCausal, a uniﬁed benchmark for causal text mining across three tasks: Causal Sequence Classiﬁcation, Cause-Effect Span Detection and Causal Pair Classi-ﬁcation. We consolidated and aligned annotations of six high quality human-annotated corpus, resulting in a total of 58,720, 12,144 and 69,165 examples for each task respectively. Since the deﬁnition of causality can be subjective, our framework was designed to allow researchers to work on some or all datasets and tasks. As an initial benchmark, we adapted BERT pre-trained models to our task and generated baseline scores. We achieved 70.10% Binary F1 score for Sequence Classiﬁcation, 52.42% Macro F1 score for Span Detection, and 84.68% Binary F1 score for Pair Classi-ﬁcation.
Long-distance transmon coupler with CZ gate fidelity above $99.8\%$

Tunable coupling of superconducting qubits has been widely studied due to its importance for isolated gate operations in scalable quantum processor architectures. Here, we demonstrate a tunable qubit-qubit coupler based on a floating transmon device which allows us to place qubits at least 2 mm apart from each other while maintaining over 50 MHz coupling between the coupler and the qubits. In the introduced tunable-coupler design, both the qubit-qubit and the qubit-coupler couplings are mediated by two waveguides instead of relying on direct capacitive couplings between the components, reducing the impact of the qubit-qubit distance on the couplings. This leaves space for each qubit to have an individual readout resonator and a Purcell filter needed for fast high-fidelity readout. In addition, the large qubit-qubit distance reduces unwanted non-nearest neighbor coupling and allows multiple control lines to cross over the structure with minimal crosstalk. Using the proposed flexible and scalable architecture, we demonstrate a controlled-$Z$ gate with $(99.81 \pm 0.02)\%$ fidelity.
Revealing nanoscale disorder in W/CoFeB/MgO ultra-thin films using domain wall motion

Disorder in ultra-thin magnetic films can significantly hinder domain wall motion. One of the main issues on the path towards efficient domain wall based devices remains the characterization of the pinning landscape at the nanoscale. In this paper, we study domain wall motion in W/CoFeB/MgO thin films with perpendicular magnetic anisotropy crystallized by annealing at 400$^{\circ}$C and a process based on He$^{+}$ irradiation combined with elevated temperatures. Magnetic properties are similar for the whole series of samples, while the magnetic domain wall mobility is critically improved in the irradiated samples. By using an analytical model to extract nanoscale pinning parameters, we reveal important variations in the disorder of the crystallized samples. This work offers a unique opportunity to selectively analyze the effects of disorder on the domain wall dynamics, without the contribution of changes in the magnetic properties. Our results highlight the importance of evaluating the nanoscale pinning parameters of the material when designing devices based on domain wall motion, which in return can be a powerful tool to probe the disorder in ultra-thin magnetic films.
IRMA: Iterative Repair for graph MAtching

The alignment of two similar graphs from different domains is a well-studied problem. In many practical usages, there is no reliable information or labels over the vertices or edges, leaving structural similarity as the only information available to match such a graph. In such cases, one often assumes a small amount of already aligned vertices -- called a seed. Current state-of-the-art scalable seeded alignment algorithms are based on percolation. Namely, aligned vertices are used to align their neighbors and gradually percolate in parallel in both graphs. However, percolation-based graph alignment algorithms are still limited in scale-free degree distributions. We here propose `IRMA' -- Iterative Repair for graph MAtching to show that the accuracy of percolation-based algorithms can be improved in real-world graphs with a limited additional computational cost, and with lower run time when used in a parallel version. IRMA starts by creating a primary alignment using an existing percolation algorithm, then it iteratively repairs the mistakes in the previous alignment steps. We prove that IRMA improves on single-iteration algorithms. We then numerically show that it is significantly better than all state-of-the-art seeded graph alignment algorithms on the graphs that they tested. In scale-free networks, many vertices have a very low degree. Such vertices have a high probability of erroneous alignments. We show that combining iterations with high recall but low precision in the alignment leads in the long run to higher recall and precision for the entire alignment.
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models

The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion models as state-of-the-art generative models and structured state space models as internal model architecture, which are particularly suited to capture long-term dependencies in time series data. We demonstrate that SSSD matches or even exceeds state-of-the-art probabilistic imputation and forecasting performance on a broad range of data sets and different missingness scenarios, including the challenging blackout-missing scenarios, where prior approaches failed to provide meaningful results.
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing

This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product’s demand curve and the proﬁt-maximizing price, while being aware of market environment shifts to avoid high opportunity costs from offering sub-optimal prices. The proposed ACIDP extends information-directed sampling (IDS) algorithms from statistical machine learning to include microeconomic choice theory, with a novel pricing strategy auditing procedure to escape sub-optimal pricing after market environment shift. The proposed ACIDP outperforms competing bandit algorithms including Upper Conﬁdence Bound (UCB) and Thompson sampling (TS) in a se-ries of market environment shifts.
Interpolation techniques for climate variables

This paper examines statistical approaches for interpolating climatic data over large regions, providing a brief introduction to interpolation techniques for climate variables of use in agricultural research, as well as general recommendations for future research to assess interpolation techniques. Three approaches—1) inverse distance weighted averaging (IDWA), 2) thin plate smoothing splines and 3) co-kriging—were evaluated for a 20,000 km2 square area covering the state of Jalisco, Mexico. Taking into account valued error prediction, data assumptions, and computational simplicity, we recommend use of thin-plate smoothing splines for interpolating climate variables.