Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Consistency in Text-based Financial Forecasting Models

Financial forecasting has been an important and active area of machine learning research, as even the most modest advantages in predictive accuracy can be parlayed into significant financial gains. Recent advances in natural language processing (NLP) bring the opportunity to leverage textual data, such as earnings reports of publicly traded companies, to predict the return rate for an asset. However, when dealing with such a sensitive task, the consistency of models – their invariance under meaning-preserving alternations in input – is a crucial property for building user trust. Despite this, current methods for financial forecasting do not take consistency into consideration. To address this issue, we propose FinTrust, an evaluation tool that assesses logical consistency in financial text. Using FinTrust, we show that the consistency of state-of-the-art NLP models for financial forecasting is poor. Our analysis of the performance degradation caused by meaning-preserving alternations suggests that current text-based methods are not suitable for robustly predicting market information.
Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models

Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating its importance in human interaction. In particular, generating laughter sequences presents a unique challenge due to the intricacy and nuances of this behaviour. This paper aims to bridge this gap by proposing a novel model capable of generating realistic laughter sequences, given a still portrait and an audio clip containing laughter. We highlight the failure cases of traditional facial animation methods and leverage recent advances in diffusion models to produce convincing laughter videos. We train our model on a diverse set of laughter datasets and introduce an evaluation metric specifically designed for laughter. When compared with previous speech-driven approaches, our model achieves state-of-the-art performance across all metrics, even when these are re-trained for laughter generation. Our code and project are publicly available
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Language models (LMs) are pretrained on diverse data sources—news, discussion forums, books, online encyclopedias. A significant portion of this data includes facts and opinions which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure media biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings which reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and media biases into misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness.
Text2Gender: A Deep Learning Architecture for Analysis of Blogger's Age and Gender

Deep learning techniques have gained a lot of traction in the field of NLP research. The aim of this paper is to predict the age and gender of an individual by inspecting their written text. We propose a supervised BERT-based classification technique in order to predict the age and gender of bloggers. The dataset used contains 681284 rows of data, with the information of the blogger's age, gender, and text of the blog written by them. We compare our algorithm to previous works in the same domain and achieve a better accuracy and F1 score. The accuracy reported for the prediction of age group was 84.2%, while the accuracy for the prediction of gender was 86.32%. This study relies on the raw capabilities of BERT to predict the classes of textual data efficiently. This paper shows promising capability in predicting the demographics of the author with high accuracy and can have wide applicability across multiple domains.
A Multilevel Low-Rank Newton Method with Super-linear Convergence Rate and its Application to Non-convex Problems

Second-order methods can address the shortcomings of first-order methods for the optimization of large-scale machine learning models. However, second-order methods have significantly higher computational costs associated with the computation of second-order information. Subspace methods that are based on randomization have addressed some of these computational costs as they compute search directions in lower dimensions. Even though super-linear convergence rates have been empirically observed, it has not been possible to rigorously show that these variants of second-order methods can indeed achieve such fast rates. Also, it is not clear whether subspace methods can be applied to non-convex cases. To address these shortcomings, we develop a link between multigrid optimization methods and low-rank Newton methods that enables us to prove the super-linear rates of stochastic low-rank Newton methods rigorously. Our method does not require any computations in the original model dimension. We further propose a truncated version of the method that is capable of solving high-dimensional non-convex problems. Preliminary numerical experiments show that our method has a better escape rate from saddle points compared to accelerated gradient descent and Adam and thus returns lower training errors.
Group velocity and energy propagation in a dielectric medium supporting magnetic current

The aspects of the group velocity and the electromagnetic energy transport for a dielectric medium endowed with magnetic conductivity are discussed. The dispersion relations and the correspondent optical effects are examined for isotropic and anisotropic magnetic conductivity tensors. The group and energy velocities are presented and carried out for all the particular cases addressed. Our results state that a dielectric supporting magnetic current breaks the usual equivalence between group velocity and energy velocity that holds in a non-absorbing medium, while establish the equivalence between them in an effective absorbing scenario, two unexpected behaviors.
On one-loop corrected dilaton action in string theory

This manuscript concerns string one-loop corrections to the Kahler potential in 4d N=1 vacua of string theories, and it largely consists of two parts. In the first part, we compute the string one-loop correction to the dilaton kinetic term in heterotic string theories, and we show that the dilaton kinetic term is not renormalized at one-loop. After reviewing the well-known result on the string one-loop correction to the Kahler potential in heterotic string theories, we explain how this result can be reconciled with the known result in the literature. In the second part, we study the dilaton dependence of the one-loop corrected Kahler potential in type II string theories. To do so, we compute the string one-loop correction to the kinetic action of the 4d-dilaton in type II string theories compactified on orientifolds of Calabi-Yau threefolds. We find that the string one-loop corrected 4d dilaton kinetic term is determined by the Witten index and the new supersymmetric index of the string worldsheet CFT.
Eulerian and Lagrangian stability in Zeitlin's model of hydrodynamics

The two-dimensional (2-D) Euler equations of a perfect fluid possess a beautiful geometric description: they are reduced geodesic equations on the infinite-dimensional Lie group of symplectomorphims with respect to a right-invariant Riemannian metric. This structure enables insights to Eulerian and Lagrangian stability via sectional curvature and Jacobi equations. The Zeitlin model is a finite-dimensional analog of the 2-D Euler equations; the only known discretization that preserves the rich geometric structure. Theoretical and numerical studies indicate that Zeitlin's model provides consistent long-time behaviour on large scales, but to which extent it truly reflects the Euler equations is mainly open. Towards progress, we give here two results. First, convergence of the sectional curvature in the Euler--Zeitlin equations on the Lie algebra $\mathfrak{su}(N)$ to that of the Euler equations on the sphere. Second, $L^2$-convergence of the corresponding Jacobi equations for Lagrangian and Eulerian stability. The results allow geometric conclusions about Zeitlin's model to be transferred to Euler's equations and vice versa, which might be central in the ultimate aim: to characterize the generic long-time behaviour in perfect 2-D fluids.
Optimal Decision Rules when Payoffs are Partially Identified

We derive optimal statistical decision rules for discrete choice problems when payoffs depend on a partially-identified parameter $\theta$ and the decision maker can use a point-identified parameter $P$ to deduce restrictions on $\theta$. Leading examples include optimal treatment choice under partial identification and optimal pricing with rich unobserved heterogeneity. Our optimal decision rules minimize the maximum risk or regret over the identified set of payoffs conditional on $P$ and use the data efficiently to learn about $P$. We discuss implementation of optimal decision rules via the bootstrap and Bayesian methods, in both parametric and semiparametric models. We provide detailed applications to treatment choice and optimal pricing. Using a limits of experiments framework, we show that our optimal decision rules can dominate seemingly natural alternatives. Our asymptotic approach is well suited for realistic empirical settings in which the derivation of finite-sample optimal rules is intractable.
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation

Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis is not universally guaranteed in numerous real-world applications, especially in medical image analysis. Medical image segmentation is typically formulated as a pixel-wise classification task in which each pixel is classified into a category. However, this formulation ignores the hard-to-classified pixels, e.g., some pixels near the boundary area, as they usually confuse DNNs. In this paper, we first explore that hard-to-classified pixels are associated with high uncertainty. Based on this, we propose a novel framework that utilizes uncertainty estimation to highlight hard-to-classified pixels for DNNs, thereby improving its generalization. We evaluate our method on two popular benchmarks: prostate and fundus datasets. The results of the experiment demonstrate that our method outperforms state-of-the-art methods.
Depth Dependence of μP Learning Rates in ReLU MLPs

In this short note we consider random fully connected ReLU networks of width $n$ and depth $L$ equipped with a mean-field weight initialization. Our purpose is to study the dependence on $n$ and $L$ of the maximal update ($\mu$P) learning rate, the largest learning rate for which the mean squared change in pre-activations after one step of gradient descent remains uniformly bounded at large $n,L$. As in prior work on $\mu$P of Yang et. al., we find that this maximal update learning rate is independent of $n$ for all but the first and last layer weights. However, we find that it has a non-trivial dependence of $L$, scaling like $L^{-3/2}. $
Topological Interpretability for Deep-Learning

With the increasing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly accelerating. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. And are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's prediction space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME interpretability method. This work demonstrates that we may gain insights into the decision mechanism of a DL model, which allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.
Estimating the Causal Effects of Natural Logic Features in Neural NLI Models

Rigorous evaluation of the causal effects of semantic features on language model predictions can be hard to achieve for natural language reasoning problems. However, this is such a desirable form of analysis from both an interpretability and model evaluation perspective, that it is valuable to zone in on specific patterns of reasoning with enough structure and regularity to be able to identify and quantify systematic reasoning failures in widely-used models. In this vein, we pick a portion of the NLI task for which an explicit causal diagram can be systematically constructed: in particular, the case where across two sentences (the premise and hypothesis), two related words/terms occur in a shared context. In this work, we apply causal effect estimation strategies to measure the effect of context interventions (whose effect on the entailment label is mediated by the semantic monotonicity characteristic) and interventions on the inserted word-pair (whose effect on the entailment label is mediated by the relation between these words.). Following related work on causal analysis of NLP models in different settings, we adapt the methodology for the NLI task to construct comparative model profiles in terms of robustness to irrelevant changes and sensitivity to impactful changes.
Upper metric mean dimensions with potential of $\epsilon$-stable sets

It is well-known that for topological dynamical systems $\epsilon$-stable sets have a deep connection with the topological entropy of the systems. In the present paper, we investigate the relationships of upper metric mean dimension with potential, Bowen upper metric mean dimension with potential, packing upper metric mean dimension with potential between the blocks of $\epsilon$-stable sets, $\epsilon$-stable sets, the dispersion of preimages of $\epsilon$-stable sets and the whole phase space. As applications, we reveal that three types different of entropies, tail entropy, preimage neighborhood entropy and topological entropy in topological dynamical systems, have the same metric mean dimension; We also establish some new double variational principles for mean dimension and upper metric mean dimension in terms of $\epsilon$-stable sets.
Diffusion Models for Imperceptible and Transferable Adversarial Attack

Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space. Despite some achievements in transferability and attack success rate, the crafted adversarial examples are easily perceived by human eyes. Towards visual imperceptibility, some recent works explore unrestricted attacks without $L_p$-norm constraints, yet lacking transferability of attacking black-box models. In this work, we propose a novel imperceptible and transferable attack by leveraging both the generative and discriminative power of diffusion models. Specifically, instead of direct manipulation in pixel space, we craft perturbations in the latent space of diffusion models. Combined with well-designed content-preserving structures, we can generate human-insensitive perturbations embedded with semantic clues. For better transferability, we further"deceive"the diffusion model which can be viewed as an implicit recognition surrogate, by distracting its attention away from the target regions. To our knowledge, our proposed method, DiffAttack, is the first that introduces diffusion models into the adversarial attack field. Extensive experiments on various model structures, datasets, and defense methods have demonstrated the superiority of our attack over the existing attack methods.
Character Association and Path Coefficient Studies in Linseed

Linseed (Linum usitatissimum) is an important oil and fibre crop with multiple uses. Its seed is used in treatment of some provocative human and animal diseases. In India, the crop is mainly cultivated in the states like Rajasthan, West Bengal, Karnataka, Orissa and Bihar, Chhattisgarh Madhya Pradesh, Uttar Pradesh and Maharashtra is one of the important linseed growing states of India, which account 112.52 thousand hectare area and 34.20 thousand metric tonnes production. Linseed oil is mainly utilized in the
In Vitro Effects of Triamcinolone and Methylprednisolone on the Viability and Mechanics of Native Articular Cartilage

Background: The chondrotoxic effects of methylprednisolone acetate (MP) and triamcinolone acetonide (TA) have been well described. However, the mechanical effects of these commonly used steroids on native cartilage are largely unknown. Purpose: To investigate the in vitro effects of a single 1-hour MP or TA exposure on the viability, mechanics, and biochemical content of native articular cartilage explants. Study Design: Controlled laboratory study. Methods: Articular cartilage explants (n = 6 per group) were harvested from the femoral condyles of bovine stifles. Explants were exposed to chondrogenic medium containing a clinical dose of MP or TA for 1 hour, followed by fresh medium wash and exchange. Explants in the control group underwent the same treatment with chondrogenic medium alone. At 24 hours after treatment, samples were assessed for viability (live/dead), mechanical properties (creep indentation and Instron tensile testing), biochemical (collagen and glycosaminoglycan) content, and pyridinoline crosslinking via mass spectrometry. Results: Mean cell viability was significantly decreased in native explants exposed to MP (35.5%) compared with the control (49.8%; P < .001) and TA (45.7%; P = .01) specimens. Significant decreases were seen in the mechanical properties of steroid-treated native explants when compared with controls, with decreases in aggregate modulus (646.3 vs 312.8 kPa [MP] and 257.0 kPa [TA]; P < .001), shear modulus (370.1 vs 191.2 kPa [MP] and 157.4 kPa [TA]; P < .001), and ultimate tensile strength (9.650 vs 5.648 MPa [MP; P = .021] and 6.065 MPa [TA; P = .0403]). No significant differences in collagen and glycosaminoglycan content were found in the steroid-treated groups. Pyridinoline crosslinking was significantly decreased in explants exposed to TA compared with controls (P = .027). Conclusion: Exposure of MP to articular cartilage explants was chondrotoxic, and exposure of articular cartilage explants to MP or TA resulted in significant decreases in mechanical properties of articular cartilage explants compared with controls. Clinicians should be judicious regarding use of intra-articular steroids, particularly in patients with intact healthy articular cartilage.
Impact of COVID-19 on health disparities between migrant and non-migrant households: the case of Dearborn, Michigan

ABSTRACT Objectives: Studies on immigration have shown that cultural changes can positively or negatively affect psychological, behavioral, and physical outcomes when different cultures settle into a new host community or country. The majority of research done in the United States and North America has focused on these changes for larger immigrant and minority groups such as Hispanics/Latinos and Asians. However, in the United States, there is a sub-group of immigrants that is largely understudied, resulting in misunderstood data on mental and physical health: Arab Americans. This study assesses mental health disparities between immigrant and non-immigrant populations, before and after COVID-19 restrictions, in Dearborn, MI, a city that has one of the largest concentrations of Arab immigrants in the nation. Design: Using an online survey instrument, this study assessed mental health disparities before and since COVID-19 restrictions, and stressors induced since the pandemic for immigrants and non-immigrants in Dearborn, MI. Results: Through inferential statistics and logistic regressions, results indicate that immigrants are less likely to have healthcare coverage, have lower annual incomes, lower educational attainment, and experience continuously higher mental health issues before the pandemic than non-immigrants faced during the pandemic. Conclusion: This study reinforces that the Arab-American immigrant population is a disadvantaged sub-group and faces considerable stress and mental health concerns as an acculturating population. This stress gets exacerbated when a worldwide event such as the COVID-19 pandemic strikes. Oversight of this population’s health issues results in the inability to receive appropriate social services and healthcare that is vital to address this community’s concerns.
Evolution of systemic therapy for advanced HCC patients: Did we make progress in 2022?

Great strides have been made in the systemic treatment of advanced HCC in the last 6 years. Approved tyrosine kinase inhibitors (TKIs) have been expanded to include lenvatinib (LEN), regorafenib, and cabozantinib (CABO). Meanwhile, immune checkpoint inhibitors (ICI) including nivolumab (NIVO) ± ipilimumab, pembrolizumab (PEMBRO), and atezolizumab (+ bevacizumab) (ATEZO+BEV) have revolutionized advanced HCC treatment. The past year saw the publication of important clinical trials, all of which evaluated ICIs but differed in ICI partners. They can be broadly categorized into single agents ICI, dual-ICIs, and ICI + antiangiogenic agent studies (Table 1).
Seeing, saying and acting: The psychological semantics of spatial prepositions

Our use of spatial prepositions carries an implicit understanding of the functional relationships both between objects themselves and human interaction with those objects. This is the thesis rigorously explicated in 'Saying, Seeing and Acting'. It aims to account not only for our theoretical comprehension of spatial relations but our ability to intercede with efficacy in the world of spatially related objects. Only the phenomenon of functionality can adequately account for what even the simplest of everyday experiences show to be the technically problematic, but still meaningful status of expressions of spatial location in contentious cases. The terms of the debate are established and contextualised in Part One. In the Second Section, systematic experimental evidence is drawn upon to demonstrate specific covariances between spatial world and spatial language. The authors go on to give an original account of the functional and geometric constraints on which comprehension and human action among spatially related objects is based. Part Three looks at the interaction of these constraints to create a truly dynamic functional geometric framework for the meaningful use of spatial prepositions. Fascinating to anyone whose work touches on psycholinguistics, this book represents a thorough and incisive contribution to debates in the cognitive psychology of language.