Miracle-dz/UniFAR-FTUs huggingface dataset:
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. Deformable odometry and SLAM pipelines, which tackle the most challenging scenario of exploratory trajectories, suffer from a lack of robustness and proper quantitative evaluation methodologies. To tackle this issue with a common benchmark, we introduce the Drunkard's Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality. We further present a novel deformable odometry method, dubbed the Drunkard's Odometry, which decomposes optical flow estimates into rigid-body camera motion and non-rigid scene deformations. In order to validate our data, our work contains an evaluation of several baselines as well as a novel tracking error metric which does not require ground truth data. Dataset and code: https://davidrecasens.github. io/TheDrunkard'sOdometry/
Case Studies: One Observation or Many? Justification or Discovery?

Critiques of case studies as an epistemic genre usually focus on the domain of justification and hinge on comparisons with statistics and laboratory experiments. In this domain, case studies can be defended by the notion of “infirming”: they use many different bits of evidence, each of which may independently “infirm” the account. Yet their efficacy may be more powerful in the domain of discovery, in which these same different bits of evidence must be fully integrated to create an explanatory account with internal validity.
Impact Dynamics of Droplet Containing Particle Suspensions on Deep Liquid Pool

Droplet impact on surfaces is ubiquitous in many natural and industrial processes. While the impact dynamics of droplets composed of simple fluids have been studied extensively, droplets containing particles are less explored, but are more application relevant. The non-Newtonian behavior of particle suspension introduces new physics affecting the impact dynamics. Here, we investigated the impact dynamics of droplets containing cornstarch particles on a deep water pool and systematically characterized the impact outcomes with various Weber number and particle volume fractions. Distinctive phenomena compared to Newtonian droplet impact have been observed. A regime map of the impact outcomes is unveiled and the transition boundaries are quantified with scaling analysis. Rheology of the suspension is found to play a pivotal role in giving rise to distinct impact outcomes. The results lay the foundation for further characterization of the dynamics of suspension droplet impacting on liquid surfaces and can be translated to other suspension fluids.
All-in-fiber dynamic orbital angular momentum mode sorting

The orbital angular momentum (OAM) spatial degree of freedom of light has been widely explored in many applications, including telecommunications, quantum information and light-based micro-manipulation. The ability to separate and distinguish between the different transverse spatial modes is called mode sorting or mode demultiplexing, and it is essential to recover the encoded information in such applications. An ideal $d$ mode sorter should be able to faithfully distinguish between the different $d$ spatial modes, with minimal losses, have $d$ outputs, and have fast response times. All previous mode sorters rely on bulk optical elements such as spatial light modulators, which cannot be quickly tuned and have additional losses if they are to be integrated with optical fiber systems. Here we propose and experimentally demonstrate, to the best of our knowledge, the first all-in-fiber method for OAM mode sorting with ultra-fast dynamic reconfigurability. Our scheme first decomposes the OAM mode in fiber-optical linearly polarized (LP) modes, and then interferometrically recombines them to determine the topological charge, thus correctly sorting the OAM mode. In addition, our setup can also be used to perform ultra-fast routing of the OAM modes. These results show a novel and fiber integrated form of optical spatial mode sorting that can be readily used for many new applications in classical and quantum information processing.
Optimal mixers restricted to subspaces and the stabilizer formalism

We present a novel formalism to both understand and construct mixers that preserve a given subspace. The method connects and utilizes the stabilizer formalism that is used in error correcting codes. This can be useful in the setting when the quantum approximate optimization algorithm (QAOA), a popular meta-heuristic for solving combinatorial optimization problems, is applied in the setting where the constraints of the problem lead to a feasible subspace that is large but easy to specify. The proposed method gives a systematic way to construct mixers that are resource efficient in the number of controlled not gates and can be understood as a generalization of the well-known X and XY mixers. The numerical examples provided show a dramatic reduction of CX gates when compared to previous results. Overall, we hope that this new perspective can lead to further insight into the development of quantum algorithms.
Spectral Batch Normalization: Normalization in the Frequency Domain

Regularization is a set of techniques that are used to improve the generalization ability of deep neural networks. In this paper, we introduce spectral batch normalization (SBN), a novel effective method to improve generalization by normalizing feature maps in the frequency (spectral) domain. The activations of residual networks without batch normalization (BN) tend to explode exponentially in the depth of the network at initialization. This leads to extremely large feature map norms even though the parameters are relatively small. These explosive dynamics can be very detrimental to learning. BN makes weight decay regularization on the scaling factors $\gamma,\beta$ approximately equivalent to an additive penalty on the norm of the feature maps, which prevents extremely large feature map norms to a certain degree. It was previously shown that preventing explosive growth at the final layer at initialization and during training in ResNets can recover a large part of Batch Normalization's generalization boost. However, we show experimentally that, despite the approximate additive penalty of BN, feature maps in deep neural networks (DNNs) tend to explode at the beginning of the training and that feature maps of DNNs contain large values during the whole training. This phenomenon also occurs in a weakened form in non-residual networks. Intuitively, it is not preferred to have large values in feature maps since they have too much influence on the prediction in contrast to other parts of the feature map. SBN addresses large feature maps by normalizing them in the frequency domain. In our experiments, we empirically show that SBN prevents exploding feature maps at initialization and large feature map values during the training. Moreover, the normalization of feature maps in the frequency domain leads to more uniform distributed frequency components. This discourages the DNNs to rely on single frequency components of feature maps. These, together with other effects (e.g. noise injection, scaling and shifting of the feature map) of SBN, have a regularizing effect on the training of residual and non-residual networks. We show experimentally that using SBN in addition to standard regularization methods improves the performance of DNNs by a relevant margin, e.g. ResNet50 on CIFAR-100 by 2.31%, on ImageNet by 0.71% (from 76.80% to 77.51%) and VGG19 on CIFAR-100 by 0.66%.
Relativistic Hydrostatic Structure Equations and Analytic Multilayer Stellar Model

The relativistic extension of the classic stellar structure equations is investigated. It is pointed out that the Tolman-Oppenheimer-Volkov (TOV) equation with the gradient equation for local gravitational mass can be made complete as a closed set of differential equations by adding that for the Tolman temperature with one equation of state, and the set is proposed as the relativistic hydrostatic structure equations. The exact forms of the relativistic Poisson equation and the steady-state heat conduction equation in the curved spacetime are derived. The application to an ideal gas of particles with the conserved particle number current leads to a strong prediction that the heat capacity ratio almost becomes one in any Newtonian convection zone such as the solar surface. The steady-state heat conduction equation is solved exactly in the system and thermodynamic observables exhibit the power law behavior, which implies the possibility for the system to be a new model of stellar corona and a flaw in the earlier one obtained by using the non-relativistic stellar structure equations. The mixture with another ideal gas yields multilayer structure to a stellar model, in which classic stellar structure equations are reproduced and analytic multilayer structure of luminous stars including the Sun is revealed in suitable approximation.
Improving Time and Memory Efficiency of Genetic Algorithms by Storing Populations as Minimum Spanning Trees of Patches

In many applications of evolutionary algorithms the computational cost of applying operators and storing populations is comparable to the cost of fitness evaluation. Furthermore, by knowing what exactly has changed in an individual by an operator, it is possible to recompute fitness value much more efficiently than from scratch. The associated time and memory improvements have been available for simple evolutionary algorithms, few specific genetic algorithms and in the context of gray-box optimization, but not for all algorithms, and the main reason is that it is difficult to achieve in algorithms using large arbitrarily structured populations. This paper makes a first step towards improving this situation. We show that storing the population as a minimum spanning tree, where vertices correspond to individuals but only contain meta-information about them, and edges store structural differences, or patches, between the individuals, is a viable alternative to the straightforward implementation. Our experiments suggest that significant, even asymptotic, improvements --- including execution of crossover operators! --- can be achieved in terms of both memory usage and computational costs.
SARC: Soft Actor Retrospective Critic

The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two. Various strategies have been introduced in literature to learn better gradient estimates to help achieve better convergence. Since gradient estimates depend upon the critic, we posit that improving the critic can provide a better gradient estimate for the actor at each time. Utilizing this, we propose Soft Actor Retrospective Critic (SARC), where we augment the SAC critic loss with another loss term - retrospective loss - leading to faster critic convergence and consequently, better policy gradient estimates for the actor. An existing implementation of SAC can be easily adapted to SARC with minimal modifications. Through extensive experimentation and analysis, we show that SARC provides consistent improvement over SAC on benchmark environments. We plan to open-source the code and all experiment data at: https://github.com/sukritiverma1996/SARC.
Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision

Large language models (LLMs) have demonstrated remarkable capabilities out of box for a wide range of applications, yet accuracy still remains a major growth area, especially in mission-critical domains such as biomedicine. An effective method to calibrate the conﬁdence level on LLM responses is essential to automatically detect errors and facilitate human-in-the-loop veriﬁcation. An important source of calibration signals stems from expert-stipulated programmatic super-vision, which is often available at low cost but has its own limitations such as noise and coverage. In this paper, we introduce a Pareto optimal self-supervision framework that can leverage available programmatic supervision to systematically calibrate LLM responses by producing a risk score for every response, without any additional manual efforts. This is accomplished by learning a harmonizer model to align LLM output with other available supervision sources, which would assign higher risk scores to more uncertain LLM responses and facilitate error correction. Experiments on standard relation extraction tasks in biomedical and general domains demonstrate the promise of this approach, with our proposed risk scores highly correlated with the real error rate of LLMs. For the most uncertain test instances, dynamic prompting based on our proposed risk scores results in signiﬁcant accuracy improvement for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA) weak supervision and GPT-4 results past SOTA supervised results on challenging evaluation datasets.
A Framework for Securing Health Information Using Blockchain in Cloud Hosted Cyber Physical Systems

Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, including the incorporation of mobile devices with the cloud computing technologies to facilitate medical data exchanges between patients and the healthcare professionals. This cutting-edge architecture enables cyber physical systems housed in the cloud to provide healthcare services with minimal operational costs, high flexibility, security, and EHR accessibility. If patient health information is stored in the hospital database, there will always be a risk of intrusion, i.e., unauthorized file access and information modification by attackers. To address this concern, we propose a decentralized EHR system based on Blockchain technology. To facilitate secure EHR exchange across various patients and medical providers, we develop a reliable access control method based on smart contracts. We incorporate Cryptocurrency, specifically Ethereum, in the suggested system to protect sensitive health information from potential attackers. In our suggested approach, both physicians and patients are required to be authenticated. Patients can register, and a block with a unique hash value will be generated. Once the patient discusses the disease with the physician, the physician can check the patient's condition and offer drugs. For experimental findings, we employ the public Block chain Ganache and solidity remix-based smart contracts to protect privacy. Ethers are used as the crypto currencies.
Computational convex analysis : from continuous deformation to finite convex integration

After introducing concepts from convex analysis, we study how to continuously transform one convex function into another. A natural choice is the arithmetic average, as it is pointwise continuous; however, this choice fails to average functions with different domains. On the contrary, the proximal average is not only continuous (in the epi-topology) but can actually average functions with disjoint domains. In fact, the proximal average not only inherits strict convexity (like the arithmetic average) but also inherits smoothness and differentiability (unlike the arithmetic average). Then we introduce a computational framework for computer-aided convex analysis. Motivated by the proximal average, we notice that the class of piecewise linear-quadratic (PLQ) functions is closed under (positive) scalar multiplication, addition, Fenchel conjugation, and Moreau envelope. As a result, the PLQ framework gives rise to linear-time and linear-space algorithms for convex PLQ functions. We extend this framework to nonconvex PLQ functions and present an explicit convex hull algorithm. Finally, we discuss a method to find primal-dual symmetric antiderivatives from cyclically monotone operators. As these antiderivatives depend on the minimal and maximal Rockafellar functions [5, Theorem 3.5, Corollary 3.10], it turns out that the minimal and maximal function in [12, p.132,p.136] are indeed the same functions. Algorithms used to compute these antiderivatives can be formulated as shortest path problems.
Evaluation of Environmental Conditions on Object Detection Using Oriented Bounding Boxes for AR Applications

The objective of augmented reality (AR) is to add digital content to natural images and videos to create an interactive experience between the user and the environment. Scene analysis and object recognition play a crucial role in AR, as they must be performed quickly and accurately. In this study, a new approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time. The approach is evaluated using two datasets: a real image dataset (DOTA dataset) commonly used for computer vision tasks, and a synthetic dataset that simulates different environmental, lighting, and acquisition conditions. The focus of the evaluation is on small objects, which are difficult to detect and recognise. The results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.
The $q_T$ spectrum for Higgs production via heavy quark annihilation at N$^3$LL$'$+aN$^3$LO

We study the transverse momentum ($q_T$) spectrum of the Higgs boson produced via the annihilation of heavy quarks ($s,c,b$) in proton-proton collisions. Using soft-collinear effective theory (SCET) and working in the five-flavour scheme, we provide predictions at three-loop order in resummed perturbation theory (N$^3$LL$'$). We match the resummed calculation to full fixed-order results at next-to-next-to-leading order (NNLO), and introduce a decorrelation method to enable a consistent matching to an approximate N$^3$LO (aN$^3$LO) result. Since the $b$-quark initiated process exhibits large nonsingular corrections, it requires special care in the matching procedure and estimation of associated theoretical uncertainties, which we discuss in detail. Our results constitute the most accurate predictions to date for these processes in the small $q_T$ region and could be used to improve the determination of Higgs Yukawa couplings from the shape of the measured Higgs $q_T$ spectrum.
Self-interacting dark matter implied by nano-Hertz gravitational waves

The self-interacting dark matter (SIDM) paradigm offers a potential solution to small-scale structure problems faced by the collision-less cold dark matter. This framework incorporates self-interactions among dark matter particles, typically mediated by a particle with a MeV-scale mass. Recent evidences of nano-Hertz gravitational waves from pulsar timing arrays (PTAs) such as NANOGrav, CPTA, EPTA, and PPTA suggest the occurrence of a first-order phase transition (FOPT) at a MeV-scale temperature. Considering the close proximity between these two scales, we propose that the mediator mass in the SIDM model originates from the spontaneous breaking of a $U(1)'$ symmetry, which is driven by the FOPT indicated by PTA data. Consequently, the alignment of these two scales is believed to be deeply connected by the same underlying physics. By extensively exploring the parameter space, remarkably, we find that the parameter space favored by SIDM just provides an explanation for the PTA data.
Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach

Numerical data imputation algorithms replace missing values by estimates to leverage incomplete data sets. Current imputation methods seek to minimize the error between the unobserved ground truth and the imputed values. But this strategy can create artifacts leading to poor imputation in the presence of multimodal or complex distributions. To tackle this problem, we introduce the $k$NN$\times$KDE algorithm: a data imputation method combining nearest neighbor estimation ($k$NN) and density estimation with Gaussian kernels (KDE). We compare our method with previous data imputation methods using artificial and real-world data with different data missing scenarios and various data missing rates, and show that our method can cope with complex original data structure, yields lower data imputation errors, and provides probabilistic estimates with higher likelihood than current methods. We release the code in open-source for the community: https://github.com/DeltaFloflo/knnxkde
Towards a Self-Replicating Turing Machine

We provide partial implementations of von Neumann's universal constructor and universal copier, starting out with three types of simple building blocks using minimal assumptions. Using the same principles, we also construct Turing machines. Combining both, we arrive at a proposal for a self-replicating Turing machine. Our construction allows for mutations if desired, and we give a simple description language.
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?

This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets.
AoA-Based Position and Orientation Estimation Using Lens MIMO in Cooperative Vehicle-to-Vehicle Systems

Positioning accuracy is a critical requirement for vehicle-to-everything (V2X) use cases. Therefore, this paper derives the theoretical limits of estimation for the position and orientation of vehicles in a cooperative vehicle-to-vehicle (V2V) scenario, using a lens-based multiple-input multiple-output (lens-MIMO) system. Following this, we analyze the Cramér-Rao lower bounds (CRLBs) of the position and orientation estimation and explore a received signal model of a lens-MIMO for the particular angle of arrival (AoA) estimation with a V2V geometric model. Further, we propose a lower complexity AoA estimation technique exploiting the unique characteristics of the lens-MIMO for a single target vehicle; as a result, its estimation scheme is effectively extended by the successive interference cancellation (SIC) method for multiple target vehicles. Given these AoAs, we investigate the lens-MIMO estimation capability for the positions and orientations of vehicles. Subsequently, we prove that the lens-MIMO outperforms a conventional uniform linear array (ULA) in a certain configuration of a lens’s structure. Finally, we confirm that the proposed localization algorithm is superior to ULA’s CRLB as the resolution of the lens increases in spite of the lower complexity.