Miracle-dz/UniFAR-FTUs huggingface dataset:
A probabilistic analysis of selected notions of iterated conditioning under coherence

It is well know that basic conditionals satisfy some desirable basic logical and probabilistic properties, such as the compound probability theorem, but checking the validity of these becomes trickier when we switch to compound and iterated conditionals. We consider de Finetti's notion of conditional as a three-valued object and as a conditional random quantity in the betting framework. We recall the notions of conjunction and disjunction among conditionals in selected trivalent logics. First, in the framework of specific three-valued logics we analyze the notions of iterated conditioning introduced by Cooper-Calabrese, de Finetti and Farrell, respectively. We show that the compound probability theorem and other basic properties are not preserved by these objects, by also computing some probability propagation rules. Then, for each trivalent logic we introduce an iterated conditional as a suitable random quantity which satisfies the compound prevision theorem and some of the desirable properties. We also check the validity of two generalized versions of Bayes' Rule for iterated conditionals. We study the p-validity of generalized versions of Modus Ponens and two-premise centering for iterated conditionals. Finally, we observe that all the basic properties are satisfied only by the iterated conditional mainly developed in recent papers by Gilio and Sanfilippo in the setting of conditional random quantities.
Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust

This paper presents the results of an extensive study investigating the opinions on Artificial Intelligence (AI) of a sample of 4,006 European citizens from eight distinct countries (France, Germany, Italy, Netherlands, Poland, Romania, Spain, and Sweden). The aim of the study is to gain a better understanding of people's views and perceptions within the European context, which is already marked by important policy actions and regulatory processes. To survey the perceptions of the citizens of Europe we design and validate a new questionnaire (PAICE) structured around three dimensions: people's awareness, attitude, and trust. We observe that while awareness is characterized by a low level of self-assessed competency, the attitude toward AI is very positive for more than half of the population. Reflecting upon the collected results, we highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust and the development of inclusive AI policies. The introduction of rules that ensure legal and ethical standards, along with the activity of high-level educational entities, and the promotion of AI literacy are identified as key factors in supporting a trustworthy AI ecosystem. We make some recommendations for AI governance focused on the European context and conclude with suggestions for future work.
Similarity solutions in elastohydrodynamic bouncing

We investigate theoretically and numerically the impact of an elastic sphere on a rigid wall in a viscous fluid. Our focus is on the dynamics of the contact, employing the soft lubrication model in which the sphere is separated from the wall by a thin liquid film. In the limit of large sphere inertia, the sphere bounces and the dynamics is close to the Hertz theory. Remarkably, the film thickness separating the sphere from the wall exhibits non-trivial self-similar properties that vary during the spreading and retraction phases. Leveraging these self-similar properties, we establish the energy budget and predict the coefficient of restitution for the sphere. The general framework derived here opens many perspectives to study the lubrication film in impact problems.
Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks

Spiking neural networks (SNNs) have tremendous potential for energy-efficient neuromorphic chips due to their binary and event-driven architecture. SNNs have been primarily used in classification tasks, but limited exploration on image generation tasks. To fill the gap, we propose a Spiking-Diffusion model, which is based on the vector quantized discrete diffusion model. First, we develop a vector quantized variational autoencoder with SNNs (VQ-SVAE) to learn a discrete latent space for images. In VQ-SVAE, image features are encoded using both the spike firing rate and postsynaptic potential, and an adaptive spike generator is designed to restore embedding features in the form of spike trains. Next, we perform absorbing state diffusion in the discrete latent space and construct a spiking diffusion image decoder (SDID) with SNNs to denoise the image. Our work is the first to build the diffusion model entirely from SNN layers. Experimental results on MNIST, FMNIST, KMNIST, Letters, and Cifar10 demonstrate that Spiking-Diffusion outperforms the existing SNN-based generation model. We achieve FIDs of 37.50, 91.98, 59.23, 67.41, and 120.5 on the above datasets respectively, with reductions of 58.60\%, 18.75\%, 64.51\%, 29.75\%, and 44.88\% in FIDs compared with the state-of-art work. Our code will be available at \url{https://github.com/Arktis2022/Spiking-Diffusion}.
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data

The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions. Current methodologies often rely on annotations derived from benchmark datasets to construct image-dialogue datasets for training purposes, akin to instruction tuning in LLMs. However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models. In an effort to mitigate these limitations, we propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models to yield a diverse and controllable dataset with varied image content. Additionally, datasets can be arbitrarily scaled. This not only provides greater flexibility compared to existing methodologies but also significantly enhances several model capabilities. Our research includes comprehensive experiments conducted on various datasets. The results emphasize substantial enhancements in more than ten commonly assessed capabilities. Additionally, our model achieves state-of-the-art results across multiple widely recognized multimodal benchmarks.
Beyond 2-D Mass-Radius Relationships: A Nonparametric and Probabilistic Framework for Characterizing Planetary Samples in Higher Dimensions

Fundamental to our understanding of planetary bulk compositions is the relationship between their masses and radii, two properties that are often not simultaneously known for most exoplanets. However, while many previous studies have modeled the two-dimensional relationship between planetary mass and radii, this approach largely ignores the dependencies on other properties that may have influenced the formation and evolution of the planets. In this work, we extend the existing nonparametric and probabilistic framework of \texttt{MRExo} to jointly model distributions beyond two dimensions. Our updated framework can now simultaneously model up to four observables, while also incorporating asymmetric measurement uncertainties and upper limits in the data. We showcase the potential of this multi-dimensional approach to three science cases: (i) a 4-dimensional joint fit to planetary mass, radius, insolation, and stellar mass, hinting of changes in planetary bulk density across insolation and stellar mass; (ii) a 3-dimensional fit to the California Kepler Survey sample showing how the planet radius valley evolves across different stellar masses; and (iii) a 2-dimensional fit to a sample of Class-II protoplanetary disks in Lupus while incorporating the upper-limits in dust mass measurements. In addition, we employ bootstrap and Monte-Carlo sampling to quantify the impact of the finite sample size as well as measurement uncertainties on the predicted quantities. We update our existing open-source user-friendly \texttt{MRExo} \texttt{Python} package with these changes, which allows users to apply this highly flexible framework to a variety of datasets beyond what we have shown here.
Improved Algorithms for Integer Complexity

The integer complexity $f(n)$ of a positive integer $n$ is defined as the minimum number of 1's needed to represent $n$, using additions, multiplications and parentheses. We present two simple and faster algorithms for computing the integer complexity: 1) A near-optimal $O(N\mathop{\mathrm{polylog}} N)$-time algorithm for computing the integer complexity of all $n\leq N$, improving the previous $O(N^{1.223})$ one [Cordwell et al., 2017]. 2) The first sublinear-time algorithm for computing the integer complexity of a single $n$, with running time $O(n^{0.6154})$. The previous algorithms for computing a single $f(n)$ require computing all $f(1),\dots,f(n)$.
Exposure Assessment of Pesticides and the Effect of Combinations of Pesticides on Parkinson's Disease

Due to the heavy and expanding agricultural use of neurotoxic pesticides suspected to affect dopaminergic neurons, it is imperative to closely examine the role of pesticides in the development of Parkinson's disease (PD). We recruited 357 incident PD cases and 752 population-based controls from 2000-2010 in the Central Valley of California and collected demographic, covariate, as well as residential and occupational address information. We utilized a geographic information system (GIS)-based exposure assessment tool to estimate historical ambient exposure to agricultural pesticides at residential and occupational addresses.Combined exposure to ziram, maneb, and paraquat at workplaces increased risk of PD three-fold and combined exposure to ziram and paraquat, excluding maneb exposure, was associated with an 80% increase in risk. Risk estimates for ambient workplace exposure were greater than exposures at residences and were especially high for younger onset PD patients and when exposed in both locations. We estimated a greater than two-fold increase in risk of developing PD for participants exposed to organophosphates, organochlorines, dithiocarbamates, and paraquat individually after adjusting for covariates. However after adjusting for other pesticides, only ambient exposure to organophosphates remained strongly associated, suggesting that pesticides from other classes have a high degree of co-exposure and may require combined exposure to affect PD risk. Longer duration of exposure and co-exposure to a large number of pesticides within the same year were both associated with strong increases in PD risk.Ambient exposure to each organophosphate separately increased the risk of developing PD. However, it is difficult to estimate the risk associated with an individual pesticide due to the likelihood that participants were exposed to combinations of these pesticides rather than any one single pesticide. Combinations of organophosphates with mitochondrial disrupting properties exhibited larger risk increases and exposure-response patterns were observed with exposure to an increasing number of these chemicals.Taken together, our results provide support that ambient co-exposure to pesticides contributes to the etiology of PD.
Agree to Disagree: Measuring Hidden Dissents in FOMC Meetings

Based on a record of dissents on FOMC votes and transcripts of the meetings from 1976 to 2017, we develop a deep learning model based on self-attention mechanism to create a measure of disagreement for members in each meeting. While dissents are rare, we find that members often have reservations with the policy decision, and the level of disagreement is mostly driven by current or predicted macroeconomic data. Using our model to evaluate speeches made by members between meetings, we find that the informational content of speeches is low if we can only compare them to speeches made by the chair. Disagreement strongly correlates with data from the Summary of Economic Projections and a measure of monetary policy sub-optimality, suggesting that disagreement is driven by both members' different preferences and their different views about the future.
Parameter Estimation-Based States Reconstruction of Uncertain Linear Systems with Overparameterization and Unknown Additive Perturbations

The problem of state reconstruction is considered for uncertain linear time-invariant systems with overparameterization, arbitrary state-space matrices and unknown additive perturbation described by an exosystem. A novel adaptive observer is proposed to solve it, which, unlike known solutions, simultaneously: (i) reconstructs the physical state of the original system rather than the virtual state of its observer canonical form, (ii) ensures exponential convergence of the reconstruction error to zero when the condition of finite excitation is satisfied, (iii) is applicable to systems, in which mentioned perturbation is generated by an exosystem with fully uncertain constant parameters. The proposed solution uses a recently published parametrization of uncertain linear systems with unknown additive perturbations, the dynamic regressor extension and mixing procedure, as well as a method of physical states reconstruction developed by the authors. Detailed analysis for stability and convergence has been provided along with simulation results to validate the theoretical analysis.
Management and prevention of corneal graft rejection

The management of an episode of corneal graft rejection (CGR) is primarily by corticosteroids. Immunomodulators are useful for long-term immunosuppression and in dealing with cases of high-risk (HR) corneal grafts. The classical signs of CGR following penetrating keratoplasty (PKP) include rejection line, anterior chamber (AC) reaction, and graft edema. However, these signs may be absent or subtle in cases of endothelial keratoplasty (EK). Prevention of an episode of graft rejection is of utmost importance as it can reduce the need for donor cornea significantly. In our previous article (IJO_2866_22), we had discussed about the immunopathogenesis of CGR. In this review article, we aim to discuss the various clinical aspects and management of CGR.
Special K\"ahler geometry and holomorphic Lagrangian fibrations

Given a holomorphic Lagrangian fibration of a compact hyperkahler manifold, we use the differential geometry of the special Kahler metric that exists on the base away from the discriminant locus, and show that the pullback of the tangent bundle of the base to the total space of a family of minimal rational curves admits a parallel splitting. The splitting is nontrivial when the base is not half-dimensional projective space. Combining this with results of Voisin, Hwang and Bakker-Schnell, we deduce that the base must be projective space, a result first proved by Hwang.
Toward Extending Concentric Tube Robot Kinematics for Large Clearance and Impulse Curvature

Concentric Tube Robots (CTRs) have been proposed to operate within the unstructured environment for minimally invasive surgeries. In this letter, we consider the operation scenario where the tubes travel inside the channels with a large clearance or impulse curvature, such as aortas or industrial pipes. Accurate kinematic modeling of CTRs is required for the development of motion planning and control within these operation scenarios. To this end, we extended the conventional CTR kinematics model to a more general case with large tube-to-tube clearance and impulse centerline curvature. Numerical simulations and experimental validations are conducted to compare our model with respect to the conventional CTR kinematic model. In the physical experiments, our proposed model achieved a tip position error of 1.53 mm in the 2D planer case and 3.86 mm in 3D case, outperforming the state-of-the-art model by 71% and 61%, respectively.
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs

Large language models (LLMs) possess a wealth of knowledge encoded in their parameters. However, this knowledge may become outdated or unsuitable over time. As a result, there has been a growing interest in knowledge editing for LLMs and evaluating its effectiveness. Existing studies primarily focus on knowledge editing using factual triplets, which not only incur high costs for collection but also struggle to express complex facts. Furthermore, these studies are often limited in their evaluation perspectives. In this paper, we propose Eva-KELLM, a new benchmark for evaluating knowledge editing of LLMs. This benchmark includes an evaluation framework and a corresponding dataset. Under our framework, we first ask the LLM to perform knowledge editing using raw documents, which provides a more convenient and universal approach compared to using factual triplets. We then evaluate the updated LLM from multiple perspectives. In addition to assessing the effectiveness of knowledge editing and the retention of unrelated knowledge from conventional studies, we further test the LLM's ability in two aspects: 1) Reasoning with the altered knowledge, aiming for the LLM to genuinely learn the altered knowledge instead of simply memorizing it. 2) Cross-lingual knowledge transfer, where the LLM updated with raw documents in one language should be capable of handling queries from another language. To facilitate further research, we construct and release the corresponding dataset. Using this benchmark, we investigate the effectiveness of several commonly-used knowledge editing methods. Experimental results indicate that the current methods for knowledge editing using raw documents are not effective in yielding satisfactory results, particularly when it comes to reasoning with altered knowledge and cross-lingual knowledge transfer.
Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System

Abstract Introduction The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules. Materials and Methods Ultrasound images were collected prospectively from patients who received fine needle aspiration biopsy for thyroid nodules from January 2016 to June 2021. One hundred thirty-nine patients with malignant thyroid nodules were enrolled, while 235 patients with benign nodules served as controls. Images were fed to Swin-T and ResNeSt50 models to classify the thyroid nodules. Results Patients with malignant nodules were younger and more likely male compared to those with benign nodules. The average sensitivity and specificity of Swin-T were 82.46% and 84.29%, respectively. The average sensitivity and specificity of ResNeSt50 were 72.51% and 77.14%, respectively. Receiver operating characteristics analysis revealed that the area under the curve of Swin-T was higher (AUC=0.91) than that of ResNeSt50 (AUC=0.82). The McNemar test evaluating the performance of these models showed that Swin-T had significantly better performance than ResNeSt50. Swin-T classifier can be a useful tool in helping shared decision-making between physicians and patients with thyroid nodules, particularly in those with high-risk characteristics of sonographic patterns.
An investigation of depositional mechanisms of pyroclastic density currents using anisotropy of magnetic susceptibility (AMS) and a detailed stratigraphic study of the La Caleta Formation, Tenerife

The 221 ka La Caleta Formation of the Bandas del Sur Group, Tenerife, records a phonolitic explosive caldera-forming eruption that devastated an area of >533 km in SE Tenerife. It produced a <38m succession consisting of a Plinian fall deposit overlain by ignimbrites and co-ignimbrite ash layers. Six members have been defined on the basis of 45 logs, granulometry and geochemistry, and were emplaced during five eruptive phases. Phase 1 comprised of a fluctuating Plinian eruption, with dispersal axes towards the SE that deposited an extensive, laminated ash to thin-bedded pumice fallout layer (Member 1). In Phase 2, the main Plinian phase, the eruption column reached c. 36 km high and dispersed to the SE as the eruption waxed, producing a < 3 m thick Plinian pumice fall layer, with an upper lithic-rich layer recording increased vent erosion. Phase 3 was the onset of column collapse, with generation of a rapid succession of short-lived pyroclastic density currents that deposited a series of extensive ignimbrites and fine ash layers (Members 3-5). The first density current widely bypassed the island, and its passage is recorded by c. 1 m erosion of the underlying Plinian deposit, which is draped by its associated co-ignimbrite ashfall layer. The eruption climaxed during Phases 4 and 5, with a sustained pyroclastic density current that deposited> 25 m thick ignimbrite (Member 6) containing lithic breccias that probably mark the onset of caldera collapse (Phase 5). The eruption unit is compositionally zoned, and has a more complex internallithostratigraphy than previously documented, sharing numerous similarities with the older Poris Formation.
AI said, She said - How Users Perceive Consumer Scoring in Practice

As digitization continues, consumers are increasingly exposed to AI scoring decisions. However, currently lacking is a thorough understanding of how users’ misjudgments of an AI-supported system lead to it being rejected. Therefore, investigations are needed into the appropriation of such socio-technical systems in practice and how users describe their experience with algorithm-based scoring. To address this issue, we evaluated 1,003 user reviews of an app on car insurance that calculates premiums based on the consumers’ individual driving behavior. We find evidence that users develop their own folk theories to explain the algorithms with the help of situation-related experiences and that insufficient explanations lead to power asymmetries between consumers, the system, and the company. In particular, as a result of the different needs of the stakeholders, we uncover a fundamental conflict between computational risk assessment and the perceived agency to influence the score.
Increasing sustainable user behavior in digital products through transparent sustainability information

Sustainable user behavior and purchasing preferences are crucial in combating the climate crisis and encourage businesses to develop eco-efficient and sustainable products. This study examines the impact of transparency in sustainability information for digital products on users’ behavior, conducting an online study with 80 participants. Participants were asked to decide for a website from a search result list - in the experimental condition the result list additionally contained information on the energy type the website was hosted (fossil or renewable energy). The results reveal that the inclusion of sustainability information in search results significantly influences users, leading to an increased preference for search results hosted on renewable energy platforms.