Miracle-dz/UniFAR-FTUs huggingface dataset:
Unified Statistical Channel Modeling and performance analysis of Vertical Underwater Wireless Optical Communication Links considering Turbulence-Induced Fading

The reliability of a vertical underwater wireless optical communication (UWOC) network is seriously impacted by turbulence-induced fading due to fluctuations in the water temperature and salinity, which vary with depth. To better assess the vertical UWOC system performances, an accurate probability distribution function (PDF) model that can describe this fading is indispensable. In view of the limitations of theoretical and experimental studies, this paper is the first to establish a more accurate modeling scheme for wave optics simulation (WOS) by fully considering the constraints of sampling conditions on multi-phase screen parameters. On this basis, we complete the modeling of light propagation in a vertical oceanic turbulence channel and subsequently propose a unified statistical model named mixture Weibull-generalized Gamma (WGG) distribution model to characterize turbulence-induced fading in vertical links. Interestingly, the WGG model is shown to provide a perfect fit with the acquired data under all considered channel conditions. We further show that the application of the WGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the average bit-error rate (BER). The presented results give valuable insight into the practical aspects of development of UWOC networks.
$A_{5}$ symmetry and deviation from Golden Ratio mixing with charged lepton flavor violation

A neutrino mass model that can satisfy the exact golden ratio mixing is constructed using $A_{5}$ discrete symmetry group. The deviation from the golden ratio mixing is studied by considering the contribution from the charged lepton sector in a linear seesaw framework. A definite pattern of charged lepton mass matrix predicted by the model controls the leptonic mixing angles. By taking the observed $\theta_{13}$ as the input value, we can obtain the values of all the mixing angles and Dirac CP-violating phase within the current experimental bounds. The model predicts that only the normal neutrino mass ordering is consistent with the current oscillation data. We also study the charged lepton flavor violation (cLFV) process such as $\mu \rightarrow e +\gamma$ and $\tau \rightarrow e +\gamma$ and neutrinoless double beta decay parameter $m_{\beta \beta}$. The present neutrino mass model can explain the current and future sensitivity of the cLFV processes and the present sensitivity of neutrinoless double beta decay parameter when the masses of quasi-Dirac neutrinos are in the TeV range.
Trajectory Generation and Tracking Based on Energy Minimization for a Four-Link Brachiation Robot

Aiming to mimic the brachiation locomotion of primates, we establish a brachiation robot model capable of swinging between different bars. The robot's design is based on a four-link underactuated structure. We propose an offline trajectory generator with optimization for minimizing energy consumption, which is implemented by direct collocation method to generate joint-space trajectories. We also propose a linear Model Predictive Control (MPC) algorithm as the feedback controller. The proposed MPC concurrently tracks both trajectories in joint space and Cartesian space. In simulation experiments, we analyzed the influence of lower-to-upper arm length ratio and swing time on the motion performance. The simulation results also demonstrate that the robot has satisfied ability in trajectory tracking, obstacle avoidance and robustness.
Randomized low-rank approximations beyond Gaussian random matrices

This paper expands the analysis of randomized low-rank approximation beyond the Gaussian distribution to four classes of random matrices: (1) independent sub-Gaussian entries, (2) independent sub-Gaussian columns, (3) independent bounded columns, and (4) independent columns with bounded second moment. Using a novel interpretation of the low-rank approximation error involving sample covariance matrices, we provide insight into the requirements of a \textit{good random matrix} for the purpose of randomized low-rank approximation. Although our bounds involve unspecified absolute constants (a consequence of the underlying non-asymptotic theory of random matrices), they allow for qualitative comparisons across distributions. The analysis offers some details on the minimal number of samples (the number of columns $\ell$ of the random matrix $\boldsymbol\Omega$) and the error in the resulting low-rank approximation. We illustrate our analysis in the context of the randomized subspace iteration method as a representative algorithm for low-rank approximation, however, all the results are broadly applicable to other low-rank approximation techniques. We conclude our discussion with numerical examples using both synthetic and real-world test matrices.
High-dimensional broadband non-Abelian holonomy in silicon nitride photonics

Non-Abelian geometry phase has attracted significant attention for the robust holonomic unitary behavior exhibited, which arises from the degenerate subspace evolving along a trajectory in Hilbert space. It has been regarded as a promising approach for implementing topologically protected quantum computation and logic manipulation. However, due to the challenges associated with high-dimensional parameters manipulation, this matrix-valued geometry phase has not been realized on silicon integrated photonic platform, which is CMOS compatible and regarded as the most promising flatform for next-generation functional devices. Here, we demonstrate the first non-Abelian holonomic high-dimensional unitary matrices on multilayer silicon nitride integrated platform. By leveraging the advantage of integrated platform and geometry phase, ultracompact footprint, highest order (up to six) and broadband operation (larger than 100nm) non-Abelian holonomy unitary matrices are experimentally realized. Our work paves the way for versatile non-Abelian optical computing devices in integrated photonics.
The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces

Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact that that one approximates the gradient as opposed to using the full Jacobian matrix. One research direction, related to this, has been on the application to infinite-dimensional problems, where one may naturally have a Hilbert space framework. However, there has been limited work done on considering this in a more general setup, such as where the natural framework is that of a Banach space. This article aims to address this by the introduction of a novel stochastic method, the stochastic steepest descent method (SSD). The SSD will follow the spirit of stochastic gradient descent, which utilizes Riesz representation to identify gradients and derivatives. Our choice for using such a method is that it naturally allows one to adopt a Banach space setting, for which recent applications have exploited the benefit of this, such as in PDE-constrained shape optimization. We provide a convergence theory related to this under mild assumptions. Furthermore, we demonstrate the performance of this method on a couple of numerical applications, namely a $p$-Laplacian and an optimal control problem. Our assumptions are verified in these applications.
Nonlinear Permuted Granger Causality

Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience. The original definition addresses the notion of causality in time series by establishing functional dependence conditional on a specified model. Adaptation of Granger causality to nonlinear data remains challenging, and many methods apply in-sample tests that do not incorporate out-of-sample predictability, leading to concerns of model overfitting. To allow for out-of-sample comparison, a measure of functional connectivity is explicitly defined using permutations of the covariate set. Artificial neural networks serve as featurizers of the data to approximate any arbitrary, nonlinear relationship, and consistent estimation of the variance for each permutation is shown under certain conditions on the featurization process and the model residuals. Performance of the permutation method is compared to penalized variable selection, naive replacement, and omission techniques via simulation, and it is applied to neuronal responses of acoustic stimuli in the auditory cortex of anesthetized rats. Targeted use of the Granger causal framework, when prior knowledge of the causal mechanisms in a dataset are limited, can help to reveal potential predictive relationships between sets of variables that warrant further study.
Thinking Like an Expert: Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals

Reasoning ability is one of the most crucial capabilities of a foundation model, signifying its capacity to address complex reasoning tasks. Chain-of-Thought (CoT) technique is widely regarded as one of the effective methods for enhancing the reasoning ability of foundation models and has garnered significant attention. However, the reasoning process of CoT is linear, step-by-step, similar to personal logical reasoning, suitable for solving general and slightly complicated problems. On the contrary, the thinking pattern of an expert owns two prominent characteristics that cannot be handled appropriately in CoT, i.e., high-order multi-hop reasoning and multimodal comparative judgement. Therefore, the core motivation of this paper is transcending CoT to construct a reasoning paradigm that can think like an expert. The hyperedge of a hypergraph could connect various vertices, making it naturally suitable for modelling high-order relationships. Inspired by this, this paper innovatively proposes a multimodal Hypergraph-of-Thought (HoT) reasoning paradigm, which enables the foundation models to possess the expert-level ability of high-order multi-hop reasoning and multimodal comparative judgement. Specifically, a textual hypergraph-of-thought is constructed utilizing triple as the primary thought to model higher-order relationships, and a hyperedge-of-thought is generated through multi-hop walking paths to achieve multi-hop inference. Furthermore, we devise a visual hypergraph-of-thought to interact with the textual hypergraph-of-thought via Cross-modal Co-Attention Graph Learning for multimodal comparative verification. Experimentations on the ScienceQA benchmark demonstrate the proposed HoT-based T5 outperforms CoT-based GPT3.5 and chatGPT, which is on par with CoT-based GPT4 with a lower model size.
Long distance electron-electron scattering detected with point contacts

We measure electron transport through point contacts in an electron gas in AlGaAs/GaAs heterostructures and graphene for a range of temperatures, magnetic fields and electron densities. We find a magnetoconductance peak around B = 0. With increasing temperature, the width of the peak increases monotonically, while its amplitude first increases and then decreases. For GaAs point contacts the peak is particularly sharp at relatively low temperatures $T\approx$1.5 K: the curve rounds on a scale of few tens of $\mu$T hinting at length scales of several millimeters for the corresponding scattering processes. We propose a model based on the transition between different transport regimes with increasing temperature: from ballistic transport to few electron-electron scatterings to hydrodynamic superballistic flow to hydrodynamic Poiseuille-like flow. The model is in qualitative and, in many cases, quantitative agreement with the experimental observations.
Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures

Free text comments (FTC) in patient-reported outcome measures (PROMs) data are typically analysed using manual methods, such as content analysis, which is labour-intensive and time-consuming. Machine learning analysis methods are largely unsupervised, necessitating post-analysis interpretation. Weakly supervised text classification (WSTC) can be a valuable method of analysis to classify domain-specific text data in which there is limited labelled data. In this paper, we apply five WSTC techniques to FTC in PROMs data to identify health-related quality of life (HRQoL) themes reported by colorectal cancer patients. The WSTC methods label all the themes mentioned in the FTC. The results showed moderate performance on the PROMs data, mainly due to the precision of the models, and variation between themes. Evaluation of the classification performance illustrated the potential and limitations of keyword based WSTC to label PROMs FTC when labelled data is limited.
Reinforcement Logic Rule Learning for Temporal Point Processes

We propose a framework that can incrementally expand the explanatory temporal logic rule set to explain the occurrence of temporal events. Leveraging the temporal point process modeling and learning framework, the rule content and weights will be gradually optimized until the likelihood of the observational event sequences is optimal. The proposed algorithm alternates between a master problem, where the current rule set weights are updated, and a subproblem, where a new rule is searched and included to best increase the likelihood. The formulated master problem is convex and relatively easy to solve using continuous optimization, whereas the subproblem requires searching the huge combinatorial rule predicate and relationship space. To tackle this challenge, we propose a neural search policy to learn to generate the new rule content as a sequence of actions. The policy parameters will be trained end-to-end using the reinforcement learning framework, where the reward signals can be efficiently queried by evaluating the subproblem objective. The trained policy can be used to generate new rules in a controllable way. We evaluate our methods on both synthetic and real healthcare datasets, obtaining promising results.
NEMA NU 2-2018 performance evaluation of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT

A NEMA performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32-cm extended field-of-view. This study marks the introduction of the first-ever commercially available clinical digital bismuth germanate technology. Testing was performed in accordance with the NEMA NU2-2018 standard. A comparison was made with the performance of two other commercial GE scanners with extended fields-of-view. A digital lutetium yttrium orthosilicate system (Discovery MI - 6 ring) and a non-digital bismuth germanate system (Discovery IQ). For the Omni assessment, the tangential, radial, and axial spatial resolutions at 1 cm radial offset were measured as 3.76 mm, 3.73 mm, and 4.25 mm FWHM. The total system sensitivity to a line source at the center was 44.36 cps/kBq. The peak NECR was 501 kcps at 17.8 kBq/mL. The scatter fraction at NECR peak was 35.48%, and the maximum count-rate error at and below NEC peak was 5.5%. Sphere contrast recovery coefficients were from 52% (10 mm) to 93% (37 mm). The system does not use time of flight; thus, no assessment of timing resolution was made. The PET-CT co-registration accuracy was 2.4 mm. The performance of the Omni Legend surpassed that of the Discovery MI on all NEMA tests, except for assessments of background variability (image noise). Time of flight is associated with inherent improvements in signal-to-noise ratio. In lieu of time of flight capabilities, the Omni provides software corrections in the form of a pre-trained neural network (trained on non-ToF to ToF). With such corrections, average performance is competitive when compared to ToF systems. Further validation is required to optimize clinical imaging protocols and hyperparameters associated with such software corrections and to examine the effect of non-linear corrections with varying target size, particularly for real world, clinical scans.
Minimum Hilbert-Schmidt distance and the Closest Separable state to arbitrary $2 \times 2$ and $2 \times 3$ states

In this article we provide a three step algorithm to obtain the Closest Separable State to the given state in Hilbert space dimensions $2\times 2$ and $2\times 3$, or in the higher dimensional Hilbert spaces, 'Closest Positive Partial Transpose (PPT) state' for the chosen bipartition. In the process, a tight lower bound to the minimum Hilbert-Schmidt distance is brought forth together with the relation between the minimum Hilbert-Schmidt distance and Negativity. This also leads us to discuss the validity of the said distance from the set of separable quantum states as an entanglement measure. Any Entanglement measure defined as the minimum of a distance measure to the set of separable states needs to follow certain widely accepted rules. Most significantly, contractiveness of the distance (also, CP non-expansive property) under LOCC maps. While the Hilbert-Schmidt distance does not have this property, it is still an open question if the measure constructed using it is non-increasing under LOCC operations. While we outline some of the difficulties in such a proof, we also provide numerical evidence that brings one step closer to closing the question.
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification

Classifying fine-grained lesions is challenging due to minor and subtle differences in medical images. This is because learning features of fine-grained lesions with highly minor differences is very difficult in training deep neural networks. Therefore, in this paper, we introduce Fine-Grained Self-Supervised Learning(FG-SSL) method for classifying subtle lesions in medical images. The proposed method progressively learns the model through hierarchical block such that the cross-correlation between the fine-grained Jigsaw puzzle and regularized original images is close to the identity matrix. We also apply hierarchical block for progressive fine-grained learning, which extracts different information in each step, to supervised learning for discovering subtle differences. Our method does not require an asymmetric model, nor does a negative sampling strategy, and is not sensitive to batch size. We evaluate the proposed fine-grained self-supervised learning method on comprehensive experiments using various medical image recognition datasets. In our experiments, the proposed method performs favorably compared to existing state-of-the-art approaches on the widely-used ISIC2018, APTOS2019, and ISIC2017 datasets.
Environmental variables determining the occurrence of the red-listed Carbonicola anthracophila and C. myrmecina in boreal forests

The global biodiversity loss is mainly due to human activities such as an intensification of forestry. Boreal forest ecosystems in Fennoscandia are characterized by disturbances such as forest fires, storms and floods. However, industrial forestry practices suppress forest fires and change the landscape, leading to a loss of habitats and associated species. Particularly lichen species with slow adaptation abilities and a strong substrate specificity face an extinction debt in boreal forests since their substrates are long-lived but no longer created. In this study the species-substrate relationship of two red-listed forest-fire dependent lichen species Carbon-icola anthracophila and C. myrmecina is investigated as well as the environmental variables determining their occurrence. Three dead wood types (snags, stumps, logs) of Picea abies as well as “ordinary” and “resin-rich” Pinus sylvestris were sampled. 24 forest stands of four different forest types (8-19 years old, 20-64 years old, >65 years old, nature reserves and vol-untary set-asides) were inventoried in boreal forests in Halsingland, Sweden. To record the frequency of potential Carbonicola harbouring substrates a dead wood inventory was con-ducted using a 200 x 10 m sample plot in which all wooden objects were measured. Lichen occurrence probability was modelled based on the explanatory variables using a generalized linear mixed model with a logit link function. The number of suitable dead wood objects in a stand was modelled based on stand categories using a zero-inflated Poisson regression. Sub-strate characteristics were the most important variables in species occurrence and therefore confirm the strong substrate specificity of Carbonicola anthracophila and C. myrmecina to-wards resin rich, hard, charred, dead pine wood without bark. Both lichen species were found within all stand categories, which demonstrates that stand level characteristics such as forest age or management type are not per se important for lichen occurrence. However, under the current management regime, new dead wood of the quality demanded by the species will not be formed and the current occurrence is due to historical factors. Resin rich pine dead wood was found more often in nature reserves and voluntary set asides than in middle-aged stands. Due to the high substrate specificity of Carbonicola effective conservation measures should aim at (i) the preservation of suitable substrates currently existing in managed forests and (ii) the creation of suitable Carbonicola harbouring substrates in the future with a focus on pro-tected areas. This could be achieved through an incorporation of a dead wood inventory in the national forest inventory in order to identify resin rich, fire-scarred pine dead wood, as well as harvesting and planting techniques which take old, fire-scarred pine dead wood into consider-ation and prescribed burning of protected forest stands with a fire frequency similar to those in undisturbed boreal forest ecosystems.
A Survey of Algorithmic Methods for Competency Self-Assessments in Human-Autonomy Teaming

Humans working with autonomous artificially intelligent systems may not be experts in the inner workings of their machine teammates, but need to understand when to employ, trust, and rely on the system. A critical challenge is to develop machine agents with the capacity to understand their own capabilities and limitations, and the ability to communicate this information to human partners. Self-assessment is an emerging field that tackles this challenge through the development of algorithms that enable autonomous agents to understand and communicate their competency. These methods can engender appropriate trust and align human expectations with autonomous assistant abilities. However, current research in self-assessment is dispersed across many fields, including artificial intelligence, robotics, and human factors. This survey connects work from these disparate areas and reviews state-of-the-art methods for algorithmic self-assessments that enable autonomous agents to estimate, understand, and communicate valuable information pertaining to their competency, with focus on methods that can improve interactions within human-machine teams. To better understand the landscape of self-assessment approaches, we present a framework for categorizing work in self-assessment based on underlying algorithm type: test-based, learning-based, or knowledge-based. We synthesize common features across these approaches and discuss relevant future directions for research in this emerging space.