Miracle-dz/UniFAR-FTUs huggingface dataset:
Achalasia Treatment. Robotic Approach or Laparoscopy?

Background: Nowadays the question persists whether to choose the endoscopic or surgical method as the first treatment of choice for achalasia. Another debate topic is about the differences between the outcomes of the two approaches of minimally invasive surgical treatment and their feasibility. Material and Methods: This retrospective observational study included 193 patients with achalasia treated between 2008 and 2021. The patients were divided into 2 groups (A and B): 152 with minimally invasive heller myotomy (HM), and 41 with pneumatic dilation (PD). Patients surgically treated were then subdivided into robotic group (RG) and laparoscopic group (LG). Results: The recurrence rate was significantly higher in PD group (Ã? 2 = 16.81, DF = 1, p 0.0001), with a success rate of 63,4%, comparing with 92,7% in HM group. No significant difference was obtained between the 2 groups concerning symptom relief on patients successfully treated. The success rate was comparable between the robotic and laparoscopic groups (p = 1). Significant difference was obtained in length of hospital stay between the 2 groups, with a mean of 4.78 +-1.59 days in the RG and, respectively, 5.52 +-2.1 days in the LG (t = 2.40, DF = 124.34, p = 0.0177). Postprocedural esophagitis rates were higher in patients with no fundoplication (6 out of 37 - 16.2%) and in patients treated with pneumatic dilation (4 out of 26 - 15.4%) than in patients with fundoplication (4 out of 46 - 8.5%). Conclusion: The present study indicates that surgery may be a better choice in fit patients for the treatment of achalasia. The procedure has a better success rate, even if the long-term outcomes are comparable in patients successfully treated. The success rate and long-term results were comparable between laparoscopy and robotic surgery.
The Structure of Turbulence in Pulsatile Flow over Urban Canopies

The transport of energy, mass, and momentum in the atmospheric boundary layer (ABL) is regulated by coherent structures. Although past studies have primarily focused on stationary ABL flows, the majority of real-world ABL flows are non-stationary, and a thorough examination of coherent structures under such conditions is lacking. To fill this gap, this study examines the topological changes in ABL turbulence induced by non-stationarity and their effects on momentum transport. Results from a large-eddy simulation of pulsatile open channel flow over an array of surface-mounted cuboids are examined with a focus on the inertial sublayer, and contrasted to those from a corresponding constant pressure gradient case. The analysis reveals that flow pulsation primarily affects the ejection-sweep pattern. Inspection of the instantaneous turbulence structures, two-point autocorrelations, and conditionally-averaged flow fields shows that such a pattern is primarily influenced by the phase-dependent shear rate. From a turbulence structure perspective, this influence is attributed to the changes in the geometry of hairpin vortices. An increase (decrease) in the shear rate intensifies (relaxes) the hairpin vortices, leading to an increase (decrease) in the frequency of ejections and an amplification (reduction) of their percentage contribution to the total momentum flux. Moreover, the size of the hairpin vortex packets changes according to the hairpin vortices comprising them, while the packet inclination remains unaltered during the pulsatile cycle. Findings underscore the important impact of non-stationarity on the structure of ABL turbulence and associated mechanisms supporting momentum transport.
Spectral three-dimensional sound directivity models for fixed wing aircraft

Acoustic measurements on aircraft in flight have been re-analysed in order to derive the coefficients of a mathematical model describing the spectral three-dimensional directivity pattern of the sound radiated by departing aircraft. On the basis of the fitted models the directivity characteristics of various aircraft are analysed and compared to the engine installation correction proposed by the revised Doc29 and SAE 5662.
Easy-to-implement multidimensional spline interpolation with application to ship design optimisation

ABSTRACT Among the different techniques for interpolation/approximation of sparse data, Spline interpolation represents one of the most popular alternatives. It is largely used for one-dimensional and two-dimensional problems, but the use in case of multi-dimensional datasets, where the space dimension is larger than 2, is not common. The necessity of a topology for the sample data, so that the proximal points are clearly identified, put some difficulties even in . Furthermore, the classical Spline algorithms are not straightforward to implement, so that for this reason probably they are not widely applied in ship design optimisation, and other interpolation techniques are much more popular. In this paper, some elements of Spline interpolation theory are presented in order to produce a simple and efficient multi-dimensional interpolation method, easy to implement.
Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players

Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called "Fused Spectatorship", where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences.
Exorcizing Ghosts from the Vacuum Spectra in String-inspired Nonlocal Tachyon Condensation

Tachyon condensation in quantum field theory (QFT) plays a central role in models of fundamental interactions and cosmology. Inspired by tower truncation in string field theory, ultraviolet completions were proposed with infinite-derivative form factors that preclude the appearance of pathological ghosts in the particle spectrum, contrary to other local higher-derivative QFT's. However, if the infinite-derivative QFT exhibits other vacua, each of them has its own spectrum, which is generally not ghost-free: an infinite tower of ghost-like resonances pops up above the nonlocal scale at tree-level, whose consistency is unclear. In this article, a new weakly nonlocal deformation of a generic local QFT is introduced via a Lorentz and gauge covariant star-product of fields, which is commutative but nonassociative in general. This framework realizes tachyon condensation without ghosts at the perturbative level, with applications for spontaneous symmetry breaking.
Nonlinear Analysis of the Space of Human Body Shapes and Its Application to Parametric Human Modeling System

In this thesis, we aim to achieve substantial knowledge on the space of human body shapes from a statistical analysis of digital anthropometry data. Exploring the unknown geometry of the shape space enables us to have an access to various arbitrary human body shapes, on which we are going to develop an accurate and anthropometrically reliable parametric human modeler for the computeraided design (CAD) systems. Although there have been a number of similar approaches in various computer graphics and geometry modeling literatures, most of them rely upon a linear regression model of the statistical distribution, which is inaccurate and may thus generate anthropometrically implausible results, instead of an accurate nonlinear model, which better describes precise statistical distribution. This is because, in most of computer graphics applications, visual fidelity and computational efficiency are considered to be the most important virtue in contrast to CAD applications where anthropometrical reliability counts much more instead. Therefore, in pursuing an accurate and reliable modeler for the CAD systems, we are going to establish a firm theoretical basis by developing mathematical tools for describing body shapes and for formulating their operations, so that the space of human body shapes can be analyzed in more accurate and mathematically rigorous manner. Especially, we will devote serious attention to the nonlinearity of the shape space, so that the original structure of it is not damaged or distorted by careless lin-
Robust Visual Question Answering: Datasets, Methods, and Future Challenges

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often tend to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Thirdly, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints.
Moments over the solution space of the travelling salesman problem

In this thesis we consider the statistical properties of the symmetric travelling salesman problem (TSP). Previous work on the statistical properties of the problem has been largely limited to the Euclidean case with vertex coordinates as random variables with known distribution embedded in Rd, and to the case of independent identically distributed random edge costs. Furthermore, this previous work did not extend to computing the moments, beyond the mean. In the work presented here we consider the more general case of problem instances specified as a set of edge costs and with no (known) embedding or coordinate system available. For an instance of the problem on n vertices with fixed edge costs we give constructive proofs that the population variance of tour costs over the solution space can be computed in O(n2), the third central moment can be computed in O(n4) and the fourth central moment can be computed in O(n6). These results provide direct methods to compute the moments about the origin and factorial moments of these orders with the corresponding computational complexity. In addition the results provide tractable methods to compute, among other statistics, the standard deviation of tour costs, the skewness of the probability distributions of tour costs over the solution space and kurtosis of this distribution. In the case of the stochastic TSP with edge costs defined as independently distributed random variables with (not necessarily identical) known mean and variance we provide a O(n4) algorithm to compute the variance of tour costs. Given a subgraph S of a tour in an n city TSP, we provide an O(n2) algorithm to compute the expected tour costs over the solution space of those tours containing S. This is useful in analysing and constructing algorithms such as Gutin’s greedy expectation heuristic. We demonstrate that the probability distribution of gains over the 2-opt landscape of an n city TSP can be computed in O(n4 log(n)). This result provides a tractable algorithm to compute, among other statistics the moments of gains over the landscape. The result also provides the 2-opt neutrality (the number of neighbouring solutions with identical cost) of a instance. The result has natural generalisation to the 3-opt landscape (at higher computational complexity). We relate the variance of tour costs over the solution space to that of the gains over the 2-opt landscape of a problem, providing an O(n2) method to compute the variance of gains over the landscape. We apply our method to compute the low order moments of the distribution of tour costs to several empirical studies of the solution space. Among other results we: confirm the known relationship between the standard deviation of tour costs and the optimal tour cost; we show a correlation between the skewness and the optimal tour cost; we demonstrate that the moments can be used to estimate the complete probability distribution of tour costs.
Bisimilarity in Fuzzy Description Logics Under the Zadeh Semantics

Fuzzy description logics (DLs) are extensions of DLs for dealing with imprecise and vague concepts. They found the logical basis for fuzzy ontologies, which are useful for practical applications. Bisimilarity is a natural notion of equivalence between individuals in DLs. In this paper, for the first time, we introduce the notion of bisimilarity in fuzzy DLs under the Zadeh semantics. It is defined using our notion of <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-cut simulation between fuzzy interpretations. The considered logics are fuzzy DLs that extend the fuzzy version of the DL <inline-formula><tex-math notation="LaTeX">$\mathcal {ALC}_{\bf reg}$</tex-math></inline-formula> (a variant of propositional dynamic logic) with features among inverse roles, the universal role, qualified number restrictions, nominals, and local reflexivity of a role. We provide results on preservation of information by the mentioned simulations, conditional invariance of ABoxes and TBoxes by bisimilarity between witnessed interpretations, as well as the Hennessy–Milner property for fuzzy DLs under the Zadeh semantics.
The Schmidt rank for the commuting operator framework

In quantum information theory, the Schmidt rank is a fundamental measure for the entanglement dimension of a pure bipartite state. Its natural definition uses the Schmidt decomposition of vectors on bipartite Hilbert spaces, which does not exist (or at least is not canonically given) if the observable algebras of the local systems are allowed to be general C*-algebras. In this work, we generalize the Schmidt rank to the commuting operator framework where the joint system is not necessarily described by the minimal tensor product but by a general bipartite algebra. We give algebraic and operational definitions for the Schmidt rank and show their equivalence. We analyze bipartite states and compute the Schmidt rank in several examples: The vacuum in quantum field theory, Araki-Woods-Powers states, as well as ground states and translation invariant states on spin chains which are viewed as bipartite systems for the left and right half chains. We conclude with a list of open problems for the commuting operator framework.
Statement-based Memory for Neural Source Code Summarization

Source code summarization is the task of writing natural language descriptions of source code behavior. Code summarization underpins software documentation for programmers. Short descriptions of code help programmers understand the program quickly without having to read the code itself. Lately, neural source code summarization has emerged as the frontier of research into automated code summarization techniques. By far the most popular targets for summarization are program subroutines. The idea, in a nutshell, is to train an encoder-decoder neural architecture using large sets of examples of subroutines extracted from code repositories. The encoder represents the code and the decoder represents the summary. However, most current approaches attempt to treat the subroutine as a single unit. For example, by taking the entire subroutine as input to a Transformer or RNN-based encoder. But code behavior tends to depend on the flow from statement to statement. Normally dynamic analysis may shed light on this flow, but dynamic analysis on hundreds of thousands of examples in large datasets is not practical. In this paper, we present a statement-based memory encoder that learns the important elements of flow during training, leading to a statement-based subroutine representation without the need for dynamic analysis. We implement our encoder for code summarization and demonstrate a significant improvement over the state-of-the-art.
Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity

Traditional computer vision models often require extensive manual effort for data acquisition and validation, especially when detecting subtle behavioral nuances or events within safety, security, and health analytics. The difﬁculty in distinguishing routine behaviors from potential risks in real-world applications, like differentiating routine shopping from potential shoplifting, further complicates the process. Moreover, these models may demonstrate high false positive rates and imprecise event detection when exposed to real-world scenarios that differ signiﬁcantly from the conditions of the training data. To overcome these hurdles, we present Ethosight, a novel zero-shot computer vision algorithm. Ethosight eradicates the need for pre-existing symbolic knowledge, initiating from a clean slate based on user requirements and semantic knowledge of interest. Using localized label afﬁnity calculations and a reasoning-guided iterative learning loop, Ethosight infers scene details and iteratively reﬁnes the label set. Reasoning mechanisms can be derived from large language models like GPT4, symbolic reasoners like OpenNARS(Wang 2013)(Wang 2006), or hybrid systems. Ethosight further capitalizes on the capabilities of a pre-trained multi-modal model, ImageBind, generating accurate semantic knowledge of images within a few cycles. It successfully captures both explicit and nuanced elements efﬁ-ciently. We also introduce the implementation of Korzybski’s ”time-binding” concept in machines(Korzybski 1921)(Ko-rzybski 1994), which allows for generational learning and knowledge sharing across deployments. Our evaluations demonstrate Ethosight’s efﬁcacy across 40 complex use cases, spanning domains such as health, safety, and security. It has exhibited an exceptional ability to discern new areas of interest, consistently generating high-afﬁnity scores within the top ﬁve labels from a set of a thousand. Tests conducted across diverse environments attest to Ethosight’s robust performance. Detailed results and case studies within the main body of this paper and an appendix underscore a promising trajectory towards enhancing the adaptability and resilience of computer vision models in detecting and extracting subtle and nuanced behaviors.
GPU-accelerated Parallel Solutions to the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is an important combinatorial optimization problem with applications in many areas including logistics and manufacturing. QAP is known to be NP-hard, a computationally challenging problem, which requires the use of sophisticated heuristics in finding acceptable solutions for most real-world data sets. In this paper, we present GPU-accelerated implementations of a 2opt and a tabu search algorithm for solving the QAP. For both algorithms, we extract parallelism at multiple levels and implement novel code optimization techniques that fully utilize the GPU hardware. On a series of experiments on the well-known QAPLIB data sets, our solutions, on average run an order-of-magnitude faster than previous implementations and deliver up to a factor of 63 speedup on specific instances. The quality of the solutions produced by our implementations of 2opt and tabu is within 1.03% and 0.15% of the best known values. The experimental results also provide key insight into the performance characteristics of accelerated QAP solvers. In particular, the results reveal that both algorithmic choice and the shape of the input data sets are key factors in finding efficient implementations.
Deep Directly-Trained Spiking Neural Networks for Object Detection

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown great success in achieving high performance on classification tasks with very few time steps. However, how to design a directly-trained SNN for the regression task of object detection still remains a challenging problem. To address this problem, we propose EMS-YOLO, a novel directly-trained SNN framework for object detection, which is the first trial to train a deep SNN with surrogate gradients for object detection rather than ANN-SNN conversion strategies. Specifically, we design a full-spike residual block, EMS-ResNet, which can effectively extend the depth of the directly-trained SNN with low power consumption. Furthermore, we theoretically analyze and prove the EMS-ResNet could avoid gradient vanishing or exploding. The results demonstrate that our approach outperforms the state-of-the-art ANN-SNN conversion methods (at least 500 time steps) in extremely fewer time steps (only 4 time steps). It is shown that our model could achieve comparable performance to the ANN with the same architecture while consuming 5.83× less energy on the frame-based COCO Dataset and the event-based Gen1 Dataset. Our code is available in https://github.com/BICLab/EMS-YOLO.
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks. In this work, we explore the potential of task-agnostic, unconditional diffusion models for several time series applications. We propose TSDiff, an unconditionally-trained diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. We demonstrate the effectiveness of our method on three different time series tasks: forecasting, refinement, and synthetic data generation. First, we show that TSDiff is competitive with several task-specific conditional forecasting methods (predict). Second, we leverage the learned implicit probability density of TSDiff to iteratively refine the predictions of base forecasters with reduced computational overhead over reverse diffusion (refine). Notably, the generative performance of the model remains intact -- downstream forecasters trained on synthetic samples from TSDiff outperform forecasters that are trained on samples from other state-of-the-art generative time series models, occasionally even outperforming models trained on real data (synthesize).
Further Remarks on the Sampled-Data Feedback Stabilization Problem

The paper deals with the problem of the sampled data feedback stabilization for autonomous nonlinear systems. The corresponding results extend those obtained in earlier works by the same authors. The sufficient conditions we establish are based on the existence of discontinuous control Lyapunov functions and the corresponding results are applicable to a class of nonlinear affine in the control systems.