Miracle-dz/UniFAR-FTUs huggingface dataset:
On the implied volatility of European and Asian call options under the stochastic volatility Bachelier model

In this paper we study the short-time behavior of the at-the-money implied volatility for European and arithmetic Asian call options with fixed strike price. The asset price is assumed to follow the Bachelier model with a general stochastic volatility process. Using techniques of the Malliavin calculus such as the anticipating It\^o's formula we first compute the level of the implied volatility when the maturity converges to zero. Then, we find a short maturity asymptotic formula for the skew of the implied volatility that depends on the roughness of the volatility model. We apply our general results to the SABR and fractional Bergomi models, and provide some numerical simulations that confirm the accurateness of the asymptotic formula for the skew.
A Novel Dual Predictors Framework of PEE

In this paper, we propose a improved 2D-PEH based on double prediction-error. First,different from previous 2D-PEH, the proposed 2D-DPEH is established by selecting two distinct predictors with low correlation to calculate double prediction errors for each pixel. In addition, we adopt DP to optimize the selection of expansion bins, speeding up the running time and improving the quality of the embedded image. Finally,we combined the proposed method with C-PEE and original MHM, then designed comparative experiments with state of-the-art Pee-based methods in recent years to verify the superiority of the proposed algorithm and extend PEE into a more general case.
Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available.
Prognostic impact of Charlson’s Age-Comorbidity Index and other risk factors in patients with pancreatic cancer

Objectives: Few studies have evaluated the impact of risk factors and comorbidity on overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC). The aim was to i nvestigate the prognostic importance of Charlson’s age -comorbidity index (CACI) and other risk factors on prognosis in a clinical real-world cohort of PDAC patients. Methods: 1159 patients with PDAC who had received at least one cycle of adjuvant or palliative chemotherapy were included from the Danish BIOPAC study. We analyzed OS according to CACI, tobacco smoking, alcohol intake, performance status (PS), BMI, and diabetes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for OS using Cox proportional hazards regression. Results: At the end of follow-up 994 (86%) patients had died. The median OS was 298 days for all patients (range 3 – 3010) and shortest in patients with stage IV. No association with short OS was seen for CACI > 2, diabetes, alcohol abuse, tobacco smoking, hypertension, and high BMI. Multivariate analysis showed that stage (IV vs. I: HR=9.05, 95% CI 5.17-15.84), PS (2 vs. 0: HR=3.67, 2.92-4.61) and treatment with angiotensin-converting-enzyme inhibitors (yes vs. no: HR=1.31, 1.06-1.61) were independent negative prognostic factors. Conclusions: We found that CACI, diabetes, tobacco smoking, alcohol abuse, hypertension, and high BMI were not associated with OS in a real-world cohort of patients with PDAC treated with chemotherapy. Only stage and PS were prognostic parameters.
Coalescent processes emerging from large deviations

The classical model for the genealogies of a neutrally evolving population in a fixed environment is due to Kingman. Kingman's coalescent process, which produces a binary tree, universally emerges from many microscopic models in which the variance in the number of offspring is finite. It is understood that power-law offspring distributions with infinite variance can result in a very different type of coalescent structure with merging of more than two lineages. Here we investigate the regime where the variance of the offspring distribution is finite but comparable to the population size. This is achieved by studying a model in which the log offspring sizes have a stretched exponential form. Such offspring distributions are motivated by biology, where they emerge from a toy model of growth in a heterogenous environment, but also mathematics and statistical physics, where limit theorems and phase transitions for sums over random exponentials have received considerable attention due to their appearance in the partition function of Derrida's Random Energy Model (REM). We find that the limit coalescent is a $\beta$-coalescent -- a previously studied model emerging from evolutionary dynamics models with heavy-tailed offspring distributions. We also discuss the connection to previous results on the REM.
Bounded flows of dense gases

Numerical solutions of the Enskog equation obtained employing a Finite-Difference Lattice Boltzmann (FDLB) and a Direct Simulation Monte Carlo (DSMC)-like particle method (PM) are systematically compared to determine the range of applicability of the simplified Enskog collision operator implemented in the Lattice Boltzmann framework. Three types of bounded flows of dense gases - namely the Fourier, the Couette, and the Poiseuille flows - are investigated for a wide range of input parameters. For low to moderate reduced density, the proposed FDLB model exhibits commendable accuracy for all bounded flows tested in this study, with substantially lower computational cost than the PM method.
Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability

How do language models learn to make predictions during pre-training? To study this question, we extract learning curves from five autoregressive English language model pre-training runs, for 1M tokens in context. We observe that the language models generate short repetitive phrases before learning to generate longer and more coherent text. We quantify the final surprisal, within-run variability, age of acquisition, forgettability, and cross-run variability of learning curves for individual tokens in context. More frequent tokens reach lower final surprisals, exhibit less variability within and across pre-training runs, are learned earlier, and are less likely to be"forgotten"during pre-training. Higher n-gram probabilities further accentuate these effects. Independent of the target token, shorter and more frequent contexts correlate with marginally more stable and quickly acquired predictions. Effects of part-of-speech are also small, although nouns tend to be acquired later and less stably than verbs, adverbs, and adjectives. Our work contributes to a better understanding of language model pre-training dynamics and informs the deployment of stable language models in practice.
iBARLE: imBalance-Aware Room Layout Estimation

Room layout estimation predicts layouts from a single panorama. It requires datasets with large-scale and diverse room shapes to train the models. However, there are significant imbalances in real-world datasets including the dimensions of layout complexity, camera locations, and variation in scene appearance. These issues considerably influence the model training performance. In this work, we propose the imBalance-Aware Room Layout Estimation (iBARLE) framework to address these issues. iBARLE consists of (1) Appearance Variation Generation (AVG) module, which promotes visual appearance domain generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances generalizability w.r. t. room structure, and (3) a gradient-based layout objective function, which allows more effective accounting for occlusions in complex layouts. All modules are jointly trained and help each other to achieve the best performance. Experiments and ablation studies based on ZInD~\cite{cruz2021zillow} dataset illustrate that iBARLE has state-of-the-art performance compared with other layout estimation baselines.
Gauss-Newton oriented greedy algorithms for the reconstruction of operators in nonlinear dynamics

This paper is devoted to the development and convergence analysis of greedy reconstruction algorithms based on the strategy presented in [Y. Maday and J. Salomon, Joint Proceedings of the 48th IEEE Conference on Decision and Control and the 28th Chinese Control Conference, 2009, pp. 375--379]. These procedures allow the design of a sequence of control functions that ease the identification of unknown operators in nonlinear dynamical systems. The original strategy of greedy reconstruction algorithms is based on an offline/online decomposition of the reconstruction process and an ansatz for the unknown operator obtained by an a priori chosen set of linearly independent matrices. In the previous work [S. Buchwald, G. Ciaramella and J. Salomon, SIAM J. Control Optim. , 59(6), pp. 4511-4537], convergence results were obtained in the case of linear identification problems. We tackle here the more general case of nonlinear systems. More precisely, we introduce a new greedy algorithm based on the linearized system. Then, we show that the controls obtained with this new algorithm lead to the local convergence of the classical Gauss-Newton method applied to the online nonlinear identification problem. We then extend this result to the controls obtained on nonlinear systems where a local convergence result is also proved. The main convergence results are obtained for the reconstruction of drift operators in dynamical systems with linear and bilinear control structures.
Methods for assessment and techniques for management of striped legless lizard Delma impar populations in South-Eastern Australia

The Striped Legless Lizard Delma impar has been studied over a five year period at Iramoo Wildlife Reserve, some 18 kilometres west of Melbourne, Victoria. Thie site contains the largest documented population of D. impar in Australia. Pit-fall trapping with drift-fences was found to be a useful tool for conducting habitat manipulation experiments on the species. However, the use of artificial shelters, roof tiles in this case, is cheaper, less labour intensive and less disturbing and therefore provides an adequate survey tool. Surveys conducted in spring yielded a larger number of D. impar over a shorter survey period. An objective pictorial method was devised to identify individual D. impar specimens. This enabled the rate of recapture to be assessed and can be applied to both mature and immature animals. Attempts were made to adapt harmonic direction-finder technology for use on D. Impar. This technology has previously been used to monitor the movements of juvenile and small snake species. this attempt was not successful.
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques

Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because such artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this paper, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that our proposed context-aware features can achieve up to $6\%$ and $26\%$ more accurate results than other context- and non-context-aware solutions, respectively, depending on the specific task. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.
'Going home' in post-conflict Sierra Leone - Diamonds, agriculture and re-building rural livelihoods in the Eastern Province

Sierra Leone has recently emerged from a long period of political instability and civil war which has relegated the country to the bottom of the world human development league table. A process of reconstruction and rehabilitation is now underway, to restore livelihoods, repair damaged infrastructure and rebuild the economy The sustainable development of valuable mineral resources and the restoration qf rural production systems and food security are important priorities. This article examines the post-war reconstruction process and presents evidence from communities in the Eastern Province which were badly affected during the conflict. Although some progress is being made, the pace qf change is slow, and the threat of a return to instability is a common concern among all citizens and organisations, from national government to individuals and communities at the grassroots level.
Cerebrospinal fluid biomarker supported diagnosis of Creutzfeldt-Jakob disease and rapid dementias

Citation for published version: Stoeck, K, Sanchez-Juan, P, Gawinecka, J, Green, A, Ladogana, A, Pocchiari, M, Sanchez-Valle, R, Mitrova, E, Sklaviadis, T, Kulczycki, J, Slivarichova, D, Saiz, A, Calero, M, Knight, R, Aguzzi, A, Laplanche, J-L, Peoc'h, K, Schelzke, G, Karch, A, van Duijn, CM & Zerr, I 2012, 'Cerebrospinal fluid biomarker supported diagnosis of Creutzfeldt-Jakob disease and rapid dementias: a longitudinal multicentre study over 10 years' Brain, vol 135, no. 10, pp. 3051-3061. , 10.1093/brain/aws238
Natural language to SQL in low-code platforms

One of the developers' biggest challenges in low-code platforms is retrieving data from a database using SQL queries. Here, we propose a pipeline allowing developers to write natural language (NL) to retrieve data. In this study, we collect, label, and validate data covering the SQL queries most often performed by OutSystems users. We use that data to train a NL model that generates SQL. Alongside this, we describe the entire pipeline, which comprises a feedback loop that allows us to quickly collect production data and use it to retrain our SQL generation model. Using crowd-sourcing, we collect 26k NL and SQL pairs and obtain an additional 1k pairs from production data. Finally, we develop a UI that allows developers to input a NL query in a prompt and receive a user-friendly representation of the resulting SQL query. We use A/B testing to compare four different models in production and observe a 240% improvement in terms of adoption of the feature, 220% in terms of engagement rate, and a 90% decrease in failure rate when compared against the first model that we put into production, showcasing the effectiveness of our pipeline in continuously improving our feature.
Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models

Network data is prevalent in numerous big data applications including economics and health networks where it is of prime importance to understand the latent structure of network. In this paper, we model the network using the Degree-Corrected Mixed Membership (DCMM) model. In DCMM model, for each node $i$, there exists a membership vector $\boldsymbol{\pi}_ i = (\boldsymbol{\pi}_i(1), \boldsymbol{\pi}_i(2),\ldots, \boldsymbol{\pi}_i(K))$, where $\boldsymbol{\pi}_i(k)$ denotes the weight that node $i$ puts in community $k$. We derive novel finite-sample expansion for the $\boldsymbol{\pi}_i(k)$s which allows us to obtain asymptotic distributions and confidence interval of the membership mixing probabilities and other related population quantities. This fills an important gap on uncertainty quantification on the membership profile. We further develop a ranking scheme of the vertices based on the membership mixing probabilities on certain communities and perform relevant statistical inferences. A multiplier bootstrap method is proposed for ranking inference of individual member's profile with respect to a given community. The validity of our theoretical results is further demonstrated by via numerical experiments in both real and synthetic data examples.
Wall mode dynamics and transition to chaos in magnetoconvection with a vertical magnetic field

Abstract Quasistatic magnetoconvection of a fluid with low Prandtl number (${\textit {Pr}}=0.025$) with a vertical magnetic field is considered in a unit-aspect-ratio box with no-slip boundaries. At high relative magnetic field strengths, given by the Hartmann number ${\textit {Ha}}$, the onset of convection is known to result from a sidewall instability giving rise to the wall-mode regime. Here, we carry out three-dimensional direct numerical simulations of unprecedented length to map out the parameter space at ${\textit {Ha}} = 200, 500, 1000$, varying the Rayleigh number (${\textit {Ra}}$) over the range $6\times 10^5 \lesssim {\textit {Ra}} \lesssim 5\times 10^8$. We track the development of stable equilibria produced by this primary instability, identifying bifurcations leading to limit cycles and eventually to chaotic dynamics. At ${\textit {Ha}}=200$, the steady wall-mode solution undergoes a symmetry-breaking bifurcation producing a state that features a coexistence between wall modes and a large-scale roll in the centre of the domain, which persists to higher ${\textit {Ra}}$. However, under a stronger magnetic field at ${\textit {Ha}}=1000$, the steady wall-mode solution undergoes a Hopf bifurcation producing a limit cycle which further develops to solutions that shadow an orbit homoclinic to a saddle point. Upon a further increase in ${\textit {Ra}}$, the system undergoes a subsequent symmetry break producing a coexistence between wall modes and a large-scale roll, although the large-scale roll exists only for a small range of ${\textit {Ra}}$, and chaotic dynamics primarily arise from a mixture of chaotic wall-mode dynamics and arrays of cellular structures.
When hard negative sampling meets supervised contrastive learning

State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss. Many studies have shown that using cross-entropy can result in sub-optimal generalisation and stability. While the supervised contrastive loss addresses some limitations of cross-entropy loss by focusing on intra-class similarities and inter-class differences, it neglects the importance of hard negative mining. We propose that models will benefit from performance improvement by weighting negative samples based on their dissimilarity to positive counterparts. In this paper, we introduce a new supervised contrastive learning objective, SCHaNe, which incorporates hard negative sampling during the fine-tuning phase. Without requiring specialized architectures, additional data, or extra computational resources, experimental results indicate that SCHaNe outperforms the strong baseline BEiT-3 in Top-1 accuracy across various benchmarks, with significant gains of up to $3.32\%$ in few-shot learning settings and $3.41\%$ in full dataset fine-tuning. Importantly, our proposed objective sets a new state-of-the-art for base models on ImageNet-1k, achieving an 86.14\% accuracy. Furthermore, we demonstrate that the proposed objective yields better embeddings and explains the improved effectiveness observed in our experiments.
Large language models converge toward human-like concept organization

Large language models show human-like performance in knowledge extraction, reasoning and dialogue, but it remains controversial whether this performance is best explained by memorization and pattern matching, or whether it reflects human-like inferential semantics and world knowledge. Knowledge bases such as WikiData provide large-scale, high-quality representations of inferential semantics and world knowledge. We show that large language models learn to organize concepts in ways that are strikingly similar to how concepts are organized in such knowledge bases. Knowledge bases model collective, institutional knowledge, and large language models seem to induce such knowledge from raw text. We show that bigger and better models exhibit more human-like concept organization, across four families of language models and three knowledge graph embeddings.
Robust Long-Tailed Learning via Label-Aware Bounded CVaR

Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training. In such setting, the naive model tends to have poor performance on the minority classes. Previously, a variety of loss modifications have been proposed to address the long-tailed leaning problem, while these methods either treat the samples in the same class indiscriminatingly or lack a theoretical guarantee. In this paper, we propose two novel approaches based on CVaR (Conditional Value at Risk) to improve the performance of long-tailed learning with a solid theoretical ground. Specifically, we firstly introduce a Label-Aware Bounded CVaR (LAB-CVaR) loss to overcome the pessimistic result of the original CVaR, and further design the optimal weight bounds for LAB-CVaR theoretically. Based on LAB-CVaR, we additionally propose a LAB-CVaR with logit adjustment (LAB-CVaR-logit) loss to stabilize the optimization process, where we also offer the theoretical support. Extensive experiments on real-world datasets with long-tailed label distributions verify the superiority of our proposed methods.
Hyperbolic groups and local connectivity

The goal of this paper is to give an exposition of some results of Bestvina-Mess on local connectivity of the boundary of a one-ended word hyperbolic group. We also give elementary proofs that all hyperbolic groups are semistable at infinity and their boundaries are linearly connected in the one-ended case. Geoghegan first observed that semistability at infinity is a consequence of local connectivity using ideas from shape theory, and Bonk-Kleiner proved linear connectivity using analytical methods. The methods in this paper are closely based on the original ideas of Bestvina-Mess.