Miracle-dz/UniFAR-FTUs huggingface dataset:
IMU Preintegrated Features for Efficient Deep Inertial Odometry

MEMS Inertial Measurement Units (IMUs) as ubiquitous proprioceptive motion measurement devices are available on various everyday gadgets and robotic platforms. Nevertheless, the direct inference of geometrical transformations or odometry based on these data alone is a challenging task. This is due to the hard-to-model imperfections and high noise characteristics of the sensor, which has motivated research in formulating the system as an end-to-end learning problem, where the motion patterns of the agent are exploited to facilitate better odometry estimates. However, this benefit comes at the cost of high computation and memory requirements, which makes deep inertial odometry unsuitable for low-power and edge applications. This paper attempts to address this conflict by proposing the IMU preintegrated features as a replacement for the raw IMU data in deep inertial odometry. Exploiting the manifold structure of the IMU motion model, these features provide a temporally compressed motion representation that preserves important geometrical information. We demonstrate the effectiveness and efficiency of this approach for the task of inertial odometry on two applications of pedestrian motion estimation and autonomous vehicles. We show a performance improvement compared to raw inputs while reducing the computational burdens. Additionally, we demonstrate the efficiency of this approach through an embedded implementation on a resource-constrained microcontroller.
Fourier Document Restoration for Robust Document Dewarping and Recognition

State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth. This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions and improve document recognition in a reliable and simpler manner. FDRNet focuses on high-frequency components in the Fourier space that capture most structural information but are largely free of degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. These features allow FDRNet to learn from a small amount of simply labeled training images, and the learned model can dewarp documents with complex geometric distortion and recognize the restored texts accurately. To facilitate document restoration research, we create a benchmark dataset consisting of over one thousand camera documents with different types of geometric and photometric distortion. Extensive experiments show that FDRNet outperforms the state-of-the-art by large margins on both dewarping and text recognition tasks. In addition, FDRNet requires a small amount of simply labeled training data and is easy to deploy. The proposed dataset is available at https://sg-vilab.github. io/event/warpdoc/.
Non-local heat equation with moving boundary and curve-crossing of delayed Brownian motion

In this paper we consider a non-local (in time) heat equation on a time-increasing parabolic set whose boundary is determined by a suitable curve. We provide a notion of solution for this equation and we study wellposedness under Dirichlet conditions outside the domain. A maximum principle is proved and used to derive uniqueness and continuity with respect to the initial datum of the solutions of the Dirichlet problem. Existence is proved by showing a stochastic representation based on the delayed Brownian motion killed on the boundary. Related properties on crossing probabilities of the delayed Brownian motion are obtained. The asymptotic behaviour of the mean square displacement of the process is determined, showing that the diffusive behaviour is anomalous.
Solid immersion microlens arrays-based light-field camera for 3D in vivo imaging

Light-field imaging facilitates the miniaturization of 3D cameras while it requires the extension of the depth-of-field (DoF) for practical applications such as endoscopy and intraoral scanning. Here we report a light-field camera (LFC) using solid immersion microlens arrays (siMLAs) for 3D biomedical imaging. The experimental results show that the focal length of MLAs is increased by 2.7 times and the transmittance is enhanced up to 6.9% by immersion in PDMS film. In particular, the f-number of siMLAs exceeds the limit of conventional MLAs fabricated by thermal reflow, resulting in a larger DoF. The LFC based on siMLAs has successfully acquired the depth map of a dental phantom as a hand-held scanner. This LFC suggests a new way for developing a compact in vivo 3D imaging system.
From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains

We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation. A key contribution is the combination of semi-automatic resource building for extraction of domain-dependent concern types (with 2-4 hours of human labor per domain) and an entirely automatic procedure for extraction of domain-independent moral dimensions and endorsement values. Prudent (automatic) selection of terms from propositional structures for lexical expansion (via semantic similarity) produces new moral dimension lexicons at three levels of granularity beyond a strong baseline lexicon. We develop a ground truth (GT) based on expert annotators and compare our concern detection output to GT, to yield 231% improvement in recall over baseline, with only a 10% loss in precision. F1 yields 66% improvement over baseline and 97.8% of human performance. Our lexically based approach yields large savings over approaches that employ costly human labor and model building. We provide to the community a newly expanded moral dimension/value lexicon, annotation guidelines, and GT.
Hybrid classifiers of pairwise Markov models

The article studies segmentation problem (also known as classification problem) with pairwise Markov models (PMMs). A PMM is a process where the observation process and underlying state sequence form a two-dimensional Markov chain, it is a natural generalization of a hidden Markov model. To demonstrate the richness of the class of PMMs, we examine closer a few examples of rather different types of PMMs: a model for two related Markov chains, a model that allows to model an inhomogeneous Markov chain as a homogeneous one and a semi-Markov model. The segmentation problem assumes that one of the marginal processes is observed and the other one is not, the problem is to estimate the unobserved state path given the observations. The standard state path estimators often used are the so-called Viterbi path (a sequence with maximum state path probability given the observations) or the pointwise maximum a posteriori (PMAP) path (a sequence that maximizes the conditional state probability for given observations pointwise). Both these estimators have their limitations, therefore we derive formulas for calculating the so-called hybrid path estimators which interpolate between the PMAP and Viterbi path. We apply the introduced algorithms to the studied models in order to demonstrate the properties of different segmentation methods, and to illustrate large variation in behaviour of different segmentation methods in different PMMs. The studied examples show that a segmentation method should always be chosen with care by taking into account the particular model of interest.
On the Importance of Data Size in Probing Fine-tuned Models

Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. However, these studies often neglect the role of the size of the dataset on which the model is fine-tuned. In this paper, we highlight the importance of this factor and its undeniable role in probing performance. We show that the extent of encoded linguistic knowledge depends on the number of fine-tuning samples. The analysis also reveals that larger training data mainly affects higher layers, and that the extent of this change is a factor of the number of iterations updating the model during fine-tuning rather than the diversity of the training samples. Finally, we show through a set of experiments that fine-tuning data size affects the recoverability of the changes made to the model’s linguistic knowledge.
Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random

In recommender systems, the feedback data received is always missing not at random (MNAR), which poses challenges for accurate rating prediction. To address this issue, many recent studies have been conducted on the doubly robust (DR) method and its variants to reduce bias. However, theoretical analysis shows that the DR method has a relatively large variance, while that of the error imputation-based (EIB) method is smaller. In this paper, we propose DR-TMLE that effectively captures the merits of both EIB and DR, by leveraging the targeted maximum likelihood estimation (TMLE) technique. DR-TMLE ﬁrst obtains an initial EIB estimator and then updates the error imputation model along with the bias-reduced direction. Furthermore, we propose a novel RCT-free collaborative targeted learning algorithm for DR-TMLE, called DR-TMLE-TL , which updates the propensity model adaptively to reduce the bias of imputed errors. Both theoretical analysis and experiments demonstrate the advantages of the proposed methods compared with existing debiasing methods.
Challenges and Strategies in Cross-Cultural NLP

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.
Initial Conditions for Star Formation: A Physical Description of the Filamentary ISM

The interstellar medium contains ﬁlamentary structure over a wide range of scales. Understanding the role of this structure, both as a conduit of gas across the scales and a diagnostic tool of local physics, is a major focus of star formation studies. We review recent progress in studying ﬁlamentary structure in the ISM, interpreting its properties in terms of physical processes, and exploring formation and evolution scenarios. We include structures from galactic-scale ﬁlaments to tenth-of-a-parsec scale ﬁlaments, comprising both molecular and atomic structures, from both observational and theoretical perspectives. In addition to the literature overview, we assemble a large amount of catalogue data from different surveys and provide the most comprehensive census of ﬁlamentary structures to date. Our census consists of 22803 ﬁlamentary structures, facilitating a holistic perspective and new insights. We use our census to conduct a meta-analysis, leading to a description of ﬁlament properties over four orders of magnitudes in length and eight in mass. Our analysis emphasises the hierarchical and dynamical nature of ﬁlamentary structures. Filaments do not live in isolation, nor they generally resemble static structures close to equilibrium. We propose that accretion during ﬁlament formation and evolution sets some of the key scaling properties of ﬁlaments. This highlights the role of accretion during ﬁlament formation and evolution and also in setting the initial conditions for star formation. Overall, the study of ﬁlamentary structures during the past decade has been observationally driven. While great progress has been made on measuring the basic properties of ﬁlaments, our understanding of their formation and evolution is clearly lacking. In this context, we identify a number of directions and questions we consider most pressing for the ﬁeld.
Exact approaches for the Connected Vertex Cover problem

Given a graph $G$, the Connected Vertex Cover problem (CVC) asks to find a minimum cardinality vertex cover of $G$ that induces a connected subgraph. In this paper we describe some approaches to solve the CVC problem exactly. First, we give compact mixed-integer extended formulations for CVC: these are the first formulations proposed for this problem, and can be easily adapted to variations of the problem such as Tree Cover. Second, we describe a simple branch and bound algorithm for the CVC problem. Finally, we implement our algorithm and compare its performance against our best formulation: contrary to what usually happens for the classical Vertex Cover problem, our formulation outperforms the branch and bound algorithm.
Cost Function Learning in Memorized Social Networks With Cognitive Behavioral Asymmetry

This article investigates the cost function learning in social information networks, wherein human memory and cognitive bias are explicitly taken into account. We first propose a model for social information-diffusion dynamics, with a focus on the systematic modeling of asymmetric cognitive bias represented by confirmation bias and novelty bias. Building on the dynamics model, we then propose the M3IRL—a memorized model and maximum-entropy-based inverse reinforcement learning—for learning cost functions. Compared with the existing model-free IRLs, the characteristics of M3IRL are significantly different here: no dependence on the Markov decision process principle, the need for only a single finite-time trajectory sample, and bounded decision variables. Finally, the effectiveness of the proposed social information-diffusion model and the M3IRL algorithm is validated by the online social media data.
Scalar-mediated dark matter model at colliders and gravitational wave detectors -- A White paper for Snowmass 2021

The weakly interacting massive particles (WIMPs) have been the most popular particle dark matter (DM) candidate for the last several decades, and it is well known that WIMP can be probed via the direct, indirect and collider experiments. However, the direct and indirect signals are highly suppressed in some scalar-mediated DM models, e.g. the lepton portal model with a Majorana DM candidate. As a result, collider searches are considered as the only hope to probe such models. In this white paper, we propose that the gravitational wave (GW) astronomy also serves as a powerful tool to probe such scalar mediated WIMP models via the potential first-order phase transition GW signals. An example for the lepton portal dark matter is provided, showing the complementarity between collider and GW probes.
A Facility for Low-Radioactivity Underground Argon

The DarkSide-50 experiment demonstrated the ability to extract and purify argon from deep underground sources and showed that the concentration of $^{39}$Ar in that argon was greatly reduced from the level found in argon derived from the atmosphere. That discovery broadened the physics reach of argon-based detector and created a demand for low-radioactivity underground argon (UAr) in high-energy physics, nuclear physics, and in environmental and allied sciences. The Global Argon Dark Matter Collaboration (GADMC) is preparing to produce UAr for DarkSide-20k, but a general UAr supply for the community does not exist. With the proper resources, those plants could be operated as a facility to supply UAr for most of the experiments after the DarkSide 20k production. However, if the current source becomes unavailable, or UAr masses greater than what is available from the current source is needed, then a new source must be found. To find a new source will require understanding the production of the radioactive argon isotopes underground in a gas field, and the ability to measure $^{37}$Ar, $^{39}$Ar, and $^{42}$Ar to ultra-low levels. The operation of a facility creates a need for ancillary systems to monitor for $^{37}$Ar, $^{39}$Ar, or $^{42}$Ar infiltration either directly or indirectly, which can also be used to vet the $^{37}$Ar, $^{39}$Ar, and $^{42}$Ar levels in a new UAr source, but requires the ability to separate UAr from the matrix well gas. Finding methods to work with industry to find gas streams enriched in UAr, or to commercialize a UAr facility, are highly desirable.
Verification of colorable hypergraph states with stabilizer test

Many-body quantum states, as a matter of fact, are extremely essential to solve certain mathematical problems or simulate quantum systems in measurement-based quantum computation. However, how to verify large-scale quantum states, such as hypergraph states, is an exceedingly hard task for many-body quantum systems. Here, we propose a novel fault-tolerant solution for the verification of colorable hypergraph states by using the stabilizer test. Furthermore, our protocol is dramatically facilitated by making only Pauli-X and Pauli-Z measurements. For geometric structure hypergraph states, the computational complexity of our protocol is polynomial. As to appliance, it will be also applied to blind quantum computing based on the no-signaling principle.
Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning

Detecting and evaluating surface coating defects is important for marine vessel maintenance. Currently, the assessment is carried out manually by qualified inspectors using international standards and their own experience. Automating the processes is highly challenging because of the high level of variation in vessel type, paint surface, coatings, lighting condition, weather condition, paint colors, areas of the vessel, and time in service. We present a novel deep learning-based pipeline to detect and evaluate the percentage of corrosion, fouling, and delamination on the vessel surface from normal photographs. We propose a multi-stage image processing framework, including ship section segmentation, defect segmentation, and defect classification, to automatically recognize different types of defects and measure the coverage percentage on the ship surface. Experimental results demonstrate that our proposed pipeline can objectively perform a similar assessment as a qualified inspector.
Enriching Unsupervised User Embedding via Medical Concepts

Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. However, existing unsupervised approaches of user embeddings from clinical notes do not explicitly incorporate medical concepts. In this study, we propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora, MIMIC-III and Diabetes. We evaluate user embeddings on both extrinsic and intrinsic tasks, including phenotype classification, in-hospital mortality prediction, patient retrieval, and patient relatedness. Experiments on the two clinical corpora show our approach exceeds unsupervised baselines, and incorporating medical concepts can significantly improve the baseline performance.
EdgeMatrix: A Resources Redefined Edge-Cloud System for Prioritized Services

The edge-cloud system has the potential to com-bine the advantages of heterogeneous devices and truly realize ubiquitous computing. However, for service providers to guar-antee the Service-Level-Agreement (SLA) priorities, the complex networked environment brings inherent challenges such as multi-resource heterogeneity, resource competition, and networked sys-tem dynamics. In this paper, we design a framework for the edge-cloud system, namely EdgeMatrix, to maximize the throughput while guaranteeing various SLA priorities. First, EdgeMatrix introduces Networked Multi-agent Actor-Critic (NMAC) algorithm to redefines physical resources as logically isolated resource combinations, i.e., resource cells. Then, we use a clustering algorithm to group the cells with similar characteristics into various sets, i.e., resource channels, for different channels can offer different SLA guarantees. Besides, we design a multi-task mechanism to solve the problem of joint service orchestration and request dispatch (JSORD) among edge-cloud clusters, significantly reducing the runtime than traditional methods. To ensure stability, EdgeMatrix adopts a two-time-scale framework, i.e., coordinating resources and services at the large time scale and dispatching requests at the small time scale. The real trace-based experimental results verify that EdgeMatrix can improve system throughput in complex networked environments, reduce SLA violations, and significantly reduce the runtime than traditional methods.
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation

Extracting blood vessels from retinal fundus images plays a decisive role in diagnosing the progression in pertinent diseases. In medical image analysis, vessel extraction is a semantic binary segmentation problem, where blood vasculature needs to be extracted from the background. Here, we present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. Despite a relatively less number of trainable network parameters, the scaled version of U-net architecture provides better performance compare to other methods in the domain. We validated the proposed method on retinal fundus images from the DRIVE database. A significant improvement as compared to the other algorithms in the domain, in terms of the area under ROC curve (> 0.9762) and classification accuracy (> 95.47%) are evident from the results. Furthermore, the proposed method is resistant to the central vessel reflex while sensitive to detect blood vessels in the presence of background items viz. exudates, optic disc, and fovea.
Interaction Potential for NaCs for Ultracold Scattering and Spectroscopy

We obtain the interaction potential for NaCs by fitting to experiments on ultracold scattering and spectroscopy in optical tweezers. The central region of the potential has been accurately determined from Fourier transform spectroscopy at higher temperatures, so we focus on adjusting the long-range and short-range parts. We use coupled-channel calculations of binding energies and wave functions to understand the nature of the molecular states observed in ultracold spectroscopy and of the state that causes the Feshbach resonance used to create ultracold NaCs molecules. We elucidate the relationships between the experimental quantities and features of the interaction potential. We establish the combinations of experimental quantities that determine particular features of the potential. We find that the long-range dispersion coefficient C6 must be increased by about 0.9% to 3256(1)Eha06 to fit the experimental results. We use coupled-channel calculations on the final potential to predict bound-state energies and resonance positions.