Miracle-dz/UniFAR-FTUs huggingface dataset:
Objective measurement of retention of laparoscopic skills: a prospective cohort study

Introduction: There has been an overall growth of 462% in laparoscopic procedures performed by surgical residents between 2000 and 2018. Therefore, training courses in laparoscopic surgery are advocated in many postgraduate programs. While the immediate effect is determined in some cases, the retention of acquired skills is rarely investigated. The objective of this study was to objectively measure the retention of laparoscopic technical skills to offer a more personalized training program. Methods: First year general surgery residents performed two fundamental laparoscopic skills tasks (Post and Sleeve and the ZigZag loop) on the Lapron box trainer. Assessment was performed before, directly after, and 4 months after completing the basic laparoscopy course. Force, motion, and time were the measured variables. Results: A total of 29 participants were included from 12 Dutch training hospitals and 174 trials were analyzed. The 4 months assessment of the Post and Sleeve showed a significant improvement in force (P=0.004), motion (P≤0.001), and time (P≤0.001) compared to the baseline assessment. The same was true for the ZigZag loop: force (P≤0.001), motion (P=0.005), and time (P≤0.001). Compared to the 4 months assessment, skill deterioration was present for the Post and Sleeve in the mean force (P=0.046), max impulse (P=0.12), and time (P=0.002). For the ZigZag loop, skill decay was observed for force (P=0.021), motion (P=0.015), and time (P≤0.001) parameters. Conclusion: Acquired laparoscopic technical skills decreased 4 months after the basic laparoscopy course. Compared to baseline performance, participants showed significant improvement, however deterioration was observed compared to postcourse measurements. To preserve acquired laparoscopic skills, it is recommended to incorporate maintenance training, preferably with objective parameters, in training curricula.
A Survey on Applications of Cache-Aided NOMA

Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the future network requirements (5G and beyond systems), such as, low latency, massive connectivity, users’ fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of the cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize the recent related literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well.
Computational Language $ \beta $ based on Orthomodular Lattices with the Non-distributivity of Quantum Logic

It is argued that transformation processes (generation rules) showing evidence of a long evolutionary history in universal computing systems can be generalized. The explicit function class $ \Omega $ is defined as follows:"Operators whose eigenvectors (or eigenvalues) have an irrational number in their components constitute a class of functions with quasi-periodic structure, $ \Omega $, and the class $ \Omega $ shows evidence of a long evolutionary history. "In order to empirically prove this theorem by examining physical systems carrying out life activities or intellectual outputs of developed intelligence, the basic framework of the universal machine model C and the computational language $ \beta $ is presented as a model for general computational methods, which allow transformation processes (generation rules) with deep algorithmic complexity to be derived from generation results. C and $ \beta $ perform massively parallel computations on event-state systems consisting of exponential combinations of propositional elements expressed in terms of correlations between subsystems. The logical structure of the computational language relies on a non-distributivity in Hilbert spaces or orthogonal modular lattices, allowing for the manipulation and deduction of simultaneous propositions. In this logical local structure, the propositions implying certain consequences are not uniquely determined.
Variation of mixed Hodge structure and its applications

We treat generalizations of Koll\'ar's torsion-freeness, vanishing theorem, and so on, for projective morphisms between complex analytic spaces as an application of the theory of variations of mixed Hodge structure. The results will play a crucial role in the theory of minimal models for projective morphisms of complex analytic spaces. In this paper, we do not use Saito's theory of mixed Hodge modules.
Target Location in WGS-84 Coordinates Using Synthetic Aperture Radar Images

With the advent of GPS/INS integrated navigation for precision guided munitions, terminal accuracies of 15 meters SEP are attainable. The accuracy to which target location is known now can become a significant contributor to the overall error budget. Thus it has become necessary to locate targets in the WGS-84 datum with accuracies significantly less than the 15 meter GPS error. This paper investigates a method for obtaining high accuracy (< 5m) target location using synthetic aperture radar (SAR) imagery of the target. SAB range and range rate equations are solved for target coordinates in the geographic frame, avoiding use of measured angles and coordinate frame transformations that contribute error. Use of one SAR image to solve for target latitude and longitude, and use of two SAR images to solve for target latitude, longitude, and height are discussed. Line of sight velocity errors and their effect on total location error are discussed. Geometric dilution of precision calculations are adapted to the SAR target location problem. Using this information, SAR operators should be able to estimate bounds on the target location performance of their sensor based on navigator performance, SAR motion compensation performance, and radar resolution. This target location method was tested using data from the Sandia National Laboratories Twin Otter radar testbed. A top hat was placed at a known location and imaged, resulting in four data sets comprising over two hundred images. The image sets were processed, yielding target location accuracies ranging from less than 5 meters CEP to 26 meters CEP. Factors which cause this variance in performance are discussed.
Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines

Time series visualization plays a crucial role in identifying patterns and extracting insights across various domains. However, as datasets continue to grow in size, visualizing them effectively becomes challenging. Downsampling, which involves data aggregation or selection, is a well-established approach to overcome this challenge. This work focuses on data selection algorithms, which accomplish downsampling by selecting values from the original time series. Despite their widespread adoption in visualization platforms and time series databases, there is limited literature on the evaluation of these techniques. To address this, we propose an extensive metrics-based evaluation methodology. Our methodology analyzes visual representativeness by assessing how well a downsampled time series line chart visually approximates the original data. Moreover, our methodology includes a novel concept called"visual stability", which captures visual changes when updating (streaming) or interacting with the visualization (panning and zooming). We evaluated four data point selection algorithms across three open-source visualization toolkits using our proposed methodology, considering various figure-drawing properties. Following the analysis of our findings, we formulated a set of evidence-based guidelines for line chart visualization at scale with downsampling. To promote reproducibility and enable the qualitative evaluation of new advancements in time series data point selection, we have made our methodology and results openly accessible. The proposed evaluation methodology, along with the obtained insights from this study, establishes a foundation for future research in this domain.
A Generative Framework for Low-Cost Result Validation of Outsourced Machine Learning Tasks

The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as autonomous driving, integrity verification of the outsourced ML workload is more critical--a facet that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them unfit for real-time applications. We propose Fides, a novel framework for real-time validation of outsourced ML workloads. Fides features a novel and efficient distillation technique--Greedy Distillation Transfer Learning--that dynamically distills and fine-tunes a space and compute-efficient verification model for verifying the corresponding service model while running inside a trusted execution environment. Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack. Fides also offers a re-classification functionality that predicts the original class whenever an attack is identified. We devised a generative adversarial network framework for training the attack detection and re-classification models. The evaluation shows that Fides achieves an accuracy of up to 98% for attack detection and 94% for re-classification.
Analysis of Resonance in Jet Screech with Large-Eddy Simulations

Screech resonance is studied with experimentally validated large-eddy simulation data for a 4:1 rectangular under-expanded jet at three nozzle pressure ratios. The analysis uses spectral proper orthogonal decomposition (SPOD) and spatial cross correlation to characterize the oppositely-traveling waves in the jet at the screech fundamental frequency. The results support recent theoretical framing of screech as absolute instability, and further reveal the spatial separation of individual processes for screech generation. From the leading-order SPOD mode, direct evidence of the guided jet mode being the screech closure mechanism, not the external acoustic feedback, is observed. A match in the spatial wavenumber suggests the guided jet mode is generated via interactions between the Kelvin-Helmholtz wave and the shock cells. The energy of the oppositely-moving waves shows spatially global and non-periodic behavior of the coherent structures in the streamwise direction. The ratio of wave energy identifies regions where distinct processes in screech generation take place by comparing the rate of energy propagation in the downstream direction to that of the upstream direction. The distinct regions correspond to initial shear layer receptivity, sound emission, guided jet mode excitation and decay of coherence. The leading-order SPOD mode also enables the approximation of Lighthill's stress tensor and allows for accurate calculation of the far-field screech tone amplitude with the acoustic analogy formulation. The current findings provide insights on building a physics-based reduced order model for screech amplitude prediction in the future.
Effective Feature Extraction for Intrusion Detection System using Non-negative Matrix Factorization and Univariate analysis

An Intrusion detection system (IDS) is essential for avoiding malicious activity. Mostly, IDS will be improved by machine learning approaches, but the model efficiency is degrading because of more headers (or features) present in the packet (each record). The proposed model extracts practical features using Non-negative matrix factorization and chi-square analysis. The more number of features increases the exponential time and risk of overfitting the model. Using both techniques, the proposed model makes a hierarchical approach that will reduce the features quadratic error and noise. The proposed model is implemented on three publicly available datasets, which gives significant improvement. According to recent research, the proposed model has improved performance by 4.66% and 0.39% with respective NSL-KDD and CICD 2017.
Multi-scale Protocol for Mechanistic Reaction Studies Using Semi-local Fitted Potential Energy Surfaces

In this work, we propose a multi-scale protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged-Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are ﬁtted with a machine-learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive Potential Energy Surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (S N 2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here CASSCF and CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction-coordinates and barriers. Furthermore, we ﬁnd a qualitative agreement in vibrational frequencies and reaction rate coeﬃcients. The key aspect of the method’s
Exponential stabilization of mobile robots with nonholonomic constraints

The authors present an exponentially stable controller for a two degree of freedom robot with nonholonomic constraints. They propose a piecewise smooth controller to make the origin exponentially stable for any initial condition in the state space. The main difference with respect to other approaches can be summarized as follow. The proposed scheme does not seek to render the discontinuous surface invariant, as opposed to the principles of sliding control, but rather to make this surface non-attractive. Infinite switching in the control law and the undesirable chattering phenomenon, can thus be avoided. Furthermore, this control law yields exponential stability so that the convergence can be chosen arbitrarily fast. <<ETX>>
Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction

This paper demonstrates the learning of the underlying device physics by mapping device structure images to their corresponding Current-Voltage (IV) characteristics using a novel framework based on variational autoencoders (VAE). Since VAE is used, domain expertise is not required and the framework can be quickly deployed on any new device and measurement. This is expected to be useful in the compact modeling of novel devices when only device cross-sectional images and electrical characteristics are available (e.g. novel emerging memory). Technology Computer-Aided Design (TCAD) generated and hand-drawn Metal-Oxide-Semiconductor (MOS) device images and noisy drain-current-gate-voltage curves (IDVG) are used for the demonstration. The framework is formed by stacking two VAEs (one for image manifold learning and one for IDVG manifold learning) which communicate with each other through the latent variables. Five independent variables with different strengths are used. It is shown that it can perform inverse design (generate a design structure for a given IDVG) and forward prediction (predict IDVG for a given structure image, which can be used for compact modeling if the image is treated as device parameters) successfully. Since manifold learning is used, the machine is shown to be robust against noise in the inputs (i.e. using hand-drawn images and noisy IDVG curves) and not confused by weak and irrelevant independent variables.
Solution-phase single-particle spectroscopy for probing multi-polaronic dynamics in quantum emitters at femtosecond resolution

The development of many optical quantum technologies depends on the availability of solid-state single quantum emitters with near-perfect optical coherence. However, a standing issue that limits systematic improvement is the significant sample heterogeneity and lack of mechanistic understanding of microscopic energy flow at the single emitter level and ultrafast timescales. Here we develop solution-phase single-particle pump-probe spectroscopy with photon correlation detection that captures sample-averaged dynamics in single molecules and/or defect states with unprecedented clarity at femtosecond resolution. We apply this technique to single quantum emitters in two-dimensional hexagonal boron nitride, which suffers from significant heterogeneity and low quantum efficiency. From millisecond to nanosecond timescales, the translation diffusion, metastable-state-related bunching shoulders, rotational dynamics, and antibunching features are disentangled by their distinct photon-correlation timescales, which collectively quantify the normalized two-photon emission quantum yield. Leveraging its femtosecond resolution, spectral selectivity and ultralow noise (two orders of magnitude improvement over solid-state methods), we visualize electron-phonon coupling in the time domain at the single defect level, and discover the acceleration of polaronic formation driven by multi-electron excitation. Corroborated with results from a theoretical polaron model, we show how this translates to sample-averaged photon fidelity characterization of cascaded emission efficiency and optical decoherence time. Our work provides a framework for ultrafast spectroscopy in single emitters, molecules, or defects prone to photoluminescence intermittency and heterogeneity, opening new avenues of extreme-scale characterization and synthetic improvements for quantum information applications.
A short note on the paper 'Are Randomized Caches Really Random?'

In this paper, we analyse the results and claims presented in the paper \emph{`Are Randomized Caches Truly Random? Formal Analysis of Randomized Partitioned Caches'}, presented at HPCA conference 2023. In addition, we also analyse the applicability of `Bucket and Ball' analytical model presented in MIRAGE (Usenix Security 2021) for its security estimation. We put forth the fallacies in the original bucket and ball model and discuss its implications. Finally, we demonstrate a cache occupancy attack on MIRAGE with just $10\%$ of total cache capacity and extend the framework to establish a covert channel and a template-based fingerprinting attack.
Petermann factors and phase rigidities near exceptional points

The Petermann factor and the phase rigidity are convenient measures for various aspects of open quantum and wave systems, such as the sensitivity of energy eigenvalues to perturbations or the magnitude of quantum excess noise in lasers. We discuss the behavior of these two important quantities near non-Hermitian degeneracies, so-called exceptional points. For small generic perturbations, we derive analytically explicit formulas which reveal a relation to the spectral response strength of the exceptional point. These formulas shed light on the possibilities for enhanced sensing in passive systems. The predictions of the general theory are successfully compared to analytical solutions of a toy model. Moreover, it is demonstrated that the connection between the Petermann factor and the spectral response strength provides the basis for an efficient numerical scheme to calculate the latter. Our theory is also important in the presence of the unavoidable imperfections in the fabrication of exceptional points as it allows to determine of what is left of the sensitivity for such imperfect exceptional points studied in experiments.
BioSequence2Vec: Efficient Embedding Generation For Biological Sequences

Representation learning is an important step in the machine learning pipeline. Given the current biological sequencing data volume, learning an explicit representation is prohibitive due to the dimensionality of the resulting feature vectors. Kernel-based methods, e.g., SVM, are a proven efficient and useful alternative for several machine learning (ML) tasks such as sequence classification. Three challenges with kernel methods are (i) the computation time, (ii) the memory usage (storing an $n\times n$ matrix), and (iii) the usage of kernel matrices limited to kernel-based ML methods (difficult to generalize on non-kernel classifiers). While (i) can be solved using approximate methods, challenge (ii) remains for typical kernel methods. Similarly, although non-kernel-based ML methods can be applied to kernel matrices by extracting principal components (kernel PCA), it may result in information loss, while being computationally expensive. In this paper, we propose a general-purpose representation learning approach that embodies kernel methods' qualities while avoiding computation, memory, and generalizability challenges. This involves computing a low-dimensional embedding of each sequence, using random projections of its $k$-mer frequency vectors, significantly reducing the computation needed to compute the dot product and the memory needed to store the resulting representation. Our proposed fast and alignment-free embedding method can be used as input to any distance (e.g., $k$ nearest neighbors) and non-distance (e.g., decision tree) based ML method for classification and clustering tasks. Using different forms of biological sequences as input, we perform a variety of real-world classification tasks, such as SARS-CoV-2 lineage and gene family classification, outperforming several state-of-the-art embedding and kernel methods in predictive performance.
Information Recovery-Driven Deep Incomplete Multi-view Clustering Network

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multiview data. To date, existing IMC methods usually bypass unavailable views according to prior missing information, which is considered a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this article, we propose an information-recovery-driven-deep IMC network, termed as RecFormer. Concretely, a two-stage autoencoder network with self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote representation learning and further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.
Linewidth broadening of SCH quantum-well lasers enhanced by carrier fluctuation in optical guiding layers

A theoretical analysis of the spectral linewidth in separate-confinement heterostructure (SCH) quantum-well (QW) semiconductor lasers is given by taking into account the dynamics of the injected carriers in the optical guiding and barrier layers. The analytical result indicates that the linewidth tends to be abruptly broadened due to the dynamics of the carriers overflowing into the optical guiding layers, typically in a single-quantum-well structure. In addition, a superior effect on the linewidth reduction is predicted by adoption of a potential controlled (or modulation-doped) structure. >