Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle-To-Pedestrian Communication Feedback Module: A Study on Increasing Legibility, Public Acceptance and Trust

Vehicle pedestrian communication is extremely important when developing autonomy for an autonomous vehicle. Enabling bidirectional nonverbal communication between pedestrians and autonomous vehicles will lead to an improvement of pedestrians' safety in autonomous driving. If a pedestrian wants to communicate, the autonomous vehicle should provide feedback to the human about what it is about to do. The user study presented in this paper investigated several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and a human. The result of this study will guide the development of the feedback module in future studies, optimizing for public acceptance and trust in the autonomous vehicle's decision while being legible to the widest range of potential users. The results of this study show that participants prefer symbols over text, lights and road projection. Additionally, participants prefer the combination of symbols and text as interaction modes to be displayed if the autonomous vehicle is not driving. Further, the results show that the text interaction mode option"Safe to cross"should be used combined with the symbol interaction mode option that displays a symbol of a walking person. We plan to elaborate and focus on the selected interaction modes via Virtual Reality and in the real world in ongoing and future studies.
EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning

Recent state-of-the-art methods in semi-supervised learning (SSL) combine consistency regularization with confidence-based pseudo-labeling. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable. In this work, we present a new perspective of pseudo-labeling: instead of relying on model confidence, we instead measure whether an unlabeled sample is likely to be"in-distribution"; i.e., close to the current training data. To classify whether an unlabeled sample is"in-distribution"or"out-of-distribution", we adopt the energy score from out-of-distribution detection literature. As training progresses and more unlabeled samples become in-distribution and contribute to training, the combined labeled and pseudo-labeled data can better approximate the true distribution to improve the model. Experiments demonstrate that our energy-based pseudo-labeling method, albeit conceptually simple, significantly outperforms confidence-based methods on imbalanced SSL benchmarks, and achieves competitive performance on class-balanced data. For example, it produces a 4-6% absolute accuracy improvement on CIFAR10-LT when the imbalance ratio is higher than 50. When combined with state-of-the-art long-tailed SSL methods, further improvements are attained.
Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks

Data continuously emitted from industrial ecosystems such as social or commerce platforms are commonly represented as heterogeneous graphs (HG) composed of multiple node/edge types. State-of-the-art graph learning methods for HGs known as heterogeneous graph neural networks (HGNNs) are applied to learn deep context-informed node representations. However, many HG datasets from industrial applications suffer from label imbalance between node types. As there is no direct way to learn using labels rooted at different node types, HGNNs have been applied on only a few node types with abundant labels. We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG. KTN is derived from the theoretical relationship, which we introduce in this work, between distinct feature extractors for each node types given in a HGNN model. KTN improves performance of 6 different types of HGNN models by up to 960% for inference on zero-labeled node types and outperforms state-of-the-art transfer learning baselines by up to 73% across 18 different transfer learning tasks on HGs.
Universality and approximation bounds for echo state networks with random weights

We study the uniform approximation of echo state networks (ESNs) with randomly generated internal weights. These models, in which only the readout weights are optimized during training, have made empirical success in learning dynamical systems. Recent results showed that ESNs with ReLU activation are universal. In this article, we give an alternative construction and prove that the universality holds for general activation functions. Specifically, our main result shows that, under certain condition on the activation function, there exists a sampling procedure for the internal weights so that the ESN can approximate any continuous casual time-invariant operators with high probability. In particular, for ReLU activation, we give explicit construction for these sampling procedures. We also quantify the approximation error of the constructed ReLU ESNs for sufficiently regular operators.
Language Models are General-Purpose Interfaces

Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architecture, most pretrained models are typically still developed for specific tasks or modalities. In this work, we propose to use language models as a general-purpose interface to various foundation models. A collection of pretrained encoders perceive diverse modalities (such as vision, and language), and they dock with a language model that plays the role of a universal task layer. We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders. We subsume the advantages and capabilities from both causal and non-causal modeling, thereby combining the best of two worlds. Specifically, the proposed method not only inherits the capabilities of in-context learning and open-ended generation from causal language modeling, but also is conducive to finetuning because of the bidirectional encoders. More importantly, our approach seamlessly unlocks the combinations of the above capabilities, e.g., enabling in-context learning or instruction following with finetuned encoders. Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.
Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are also discussed.
FINE-NEEDLE ASPIRATION IN DIAGNOSIS OF METASTATIC ENDOMETRIOID ADENOCARCINOMA TO THE THYROID GLAND: A RARE ENTITY

Tumor metastasis to the thyroid gland is uncommon, most of these cases are discovered during an autopsy. But the number of cases seems to have increased in recent years may be related to more frequent use of fine-needle aspiration in any suspected case. In both clinical and autopsy series, renal cell, breast and lung carcinomas are the most frequent sources of metastases. They usually occur when there are metastases elsewhere, sometimes many years after the diagnosis of the original primary tumor and show poor prognosis in general. Clinical presentation with a palpable thyroid or functional disturbances in thyroid is uncommon. Metastasis of uterine cancer to the head and neck is extremely rare. Here we present a case of isolated metastasis of endometrioid adenocarcinoma to the thyroid gland diagnosed on FNAC in a 75-year-old woman presented with rapidly increasing swelling in thyroid region.
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling

Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information. Recently, machine learning (ML) methods have become increasingly popular and achieved promising results for price movement forecasting in both academia and industry. Most existing ML solutions formulate the forecasting problem as a classification (to predict the direction) or a regression (to predict the return) problem over the entire set of training data. However, due to the extremely low signal-to-noise ratio and stochastic nature of financial data, good trading opportunities are extremely scarce. As a result, without careful selection of potentially profitable samples, such ML methods are prone to capture the patterns of noises instead of real signals. To address this issue, we propose a novel price movement forecasting framework named LARA consisting of two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention automatically extracts the potentially profitable samples by attending to label information. Moreover, equipped with metric learning techniques, LA-Attention enjoys task-specific distance metrics and effectively distributes attention to potentially profitable samples. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies and experiments also demonstrate that LARA indeed captures more reliable trading opportunities.
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training data. To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i.e., minimizing the dependency between inputs (i.e., features) and outputs (i.e., labels) during training the classifier. However, such a minimization process conflicts with minimizing the supervised loss that aims to maximize the dependency between inputs and outputs, causing an explicit trade-off between model robustness against MI attacks and model utility on classification tasks. In this paper, we aim to minimize the dependency between the latent representations and the inputs while maximizing the dependency between latent representations and the outputs, named a bilateral dependency optimization (BiDO) strategy. In particular, we use the dependency constraints as a universally applicable regularizer in addition to commonly used losses for deep neural networks (e.g., cross-entropy), which can be instantiated with appropriate dependency criteria according to different tasks. To verify the efficacy of our strategy, we propose two implementations of BiDO, by using two different dependency measures: BiDO with constrained covariance (BiDO-COCO) and BiDO with Hilbert-Schmidt Independence Criterion (BiDO-HSIC). Experiments show that BiDO achieves the state-of-the-art defense performance for a variety of datasets, classifiers, and MI attacks while suffering a minor classification-accuracy drop compared to the well-trained classifier with no defense, which lights up a novel road to defend against MI attacks.
Hydrodynamic slender-body theory for local rotation at zero Reynolds number

Slender objects are commonplace in microscale flow problems, from soft deformable sensors to biological filaments such as flagella and cilia. Whilst much research has focussed on the local translational motion of these slender bodies, relatively little attention has been given to local rotation, even though it can be the dominant component of motion. In this study, we explore a classically motivated ansatz for the Stokes flow around a rotating slender body via superposed rotlet singularities, which leads us to pose an alternative ansatz that accounts for both translation and rotation. Through an asymptotic analysis that is supported by numerical examples, we determine the suitability of these flow ansatzes for capturing the fluid velocity at the surface of a slender body, assuming local axisymmetry of the object but allowing the cross-sectional radius to vary with arclength. In addition to formally justifying the presented slender-body ansatzes, this analysis reveals a markedly simple relation between the local angular velocity and the torque exerted on the body, which we term resistive torque theory. Though reminiscent of classical resistive force theories, this local relation is found to be algebraically accurate in the slender-body aspect ratio, even when translation is present, and is valid and required whenever local rotation contributes to the surface velocity at leading asymptotic order.
A REVIEW OF TRAILING EDGE NOISE GENERATED BY AIRFOILS AT LOW TO MODERATE REYNOLDS NUMBER

This paper contains a detailed literature review of research findings regarding the cause of flow-induced noise created by airfoils operating at low to moderate Reynolds numbers. There are many important engineering applications that operate at these conditions. More investigation is required to understand why airfoils in this range of Reynolds numbers produce high levels of tonal noise. As discussed in this paper, there are still many uncertainties surrounding the nature of the source.
Characterizing the post-inflationary reheating history. Part II. Multiple interacting daughter fields

We characterize the post-inflationary dynamics of an inflaton ϕ coupled to multiple interacting daughter fields Xn (n = 1, … Nd ) through quadratic-quadratic interactions gn 2 ϕ 2 Xn 2. We assume a monomial inflaton potential V(ϕ) ∝ |ϕ| p (p ≥ 2) around the minimum. By simulating the system in 2+1-dimensional lattices, we study the post-inflationary evolution of the energy distribution and equation of state, from the end of inflation until a stationary regime is achieved. We show that in this scenario, the energy transferred to the daughter field sector can be larger than 50%, surpassing this way the upper bound found previously for single daughter field models. In particular, for p ≥ 4 the energy at very late times is equally distributed between all fields, and only 100/(Nd + 1) % of the energy remains in the inflaton. We also consider scenarios in which the daughter fields have scale-free interactions λnmXn 2 Xm 2, including the case of quartic daughter field self-interactions (for n = m). We show that these interactions trigger a resonance process during the non-linear regime, which in the single daughter field case already allows to deplete more than 50% of the energy from the inflaton for p ≥ 4.
Non-Newtonian Turbulent Jets at Low-Reynolds Number

We perform direct numerical simulations of planar jets of non-Newtonian fluids at low Reynolds number, in typical laminar conditions for a Newtonian fluid. We select three different non-Newtonian fluid models characterized by shear-thinning (Carreau), viscoelasticity (Oldroyd-B) and shear-thinning and viscoelasticity together (Giesekus), and perform a thorough analysis of the resulting flow statistics. We observe that, as the Weissenberg number is increased, the jet transitions from a laminar flow at low Weissenberg number, to a turbulent flow at high Weissenberg number. We show that the different non-Newtonian features and their combination give rise to rather different flowing regimes, originating from the competition of viscous, elastic and inertial effects. We observe that both viscoelasticity and shear-thinning can develop the instability and the consequent transition to a turbulent flowing regime; however, the purely viscoelastic Oldroyd-B fluid exhibits the onset of disordered fluid motions at a lower Weissenberg number than what observed for the purely shear-thinning Carreau fluid. When the two effects are both present, an intermediate condition is found, suggesting that, in this case, the shear-thinning feature is acting against the fluid elasticity. Despite the qualitative differences observed in the flowing regime, the bulk statistics, namely the centerline velocity and jet thickness, follow almost the same power-law scalings obtained for laminar and turbulent Newtonian planar jets.
Measuring Star Formation and Black Hole Accretion Rates in Tandem Using Mid-infrared Spectra of Local Infrared Luminous Galaxies

We present the results of a stacking analysis performed on Spitzer/Infrared Spectrograph high-resolution mid-infrared (mid-IR) spectra of luminous infrared galaxies (LIRGs) in the Great Observatories All-Sky LIRG Survey. By binning in relation to mid-IR active galactic nucleus (AGN) fraction and stacking spectra, we detect bright emission lines [Ne ii] and [Ne iii], which trace star formation, and fainter emission lines [Ne v] and [O iv], which trace AGN activity, throughout the sample. We find that the [Ne ii] luminosity is fairly constant across all AGN fraction bins, while the [O iv] and [Ne v] luminosities increase by over an order of magnitude. Our measured average line ratios, [Ne v]/[Ne ii] and [O iv]/[Ne ii], at low AGN fraction are similar to H II galaxies, while the line ratios at high AGN fraction are similar to LINERs and Seyferts. We decompose the [O iv] luminosity into star formation and AGN components by fitting the [O iv] luminosity as a function of the [Ne ii] luminosity and the mid-IR AGN fraction. The [O iv] luminosity in LIRGs is dominated by star formation for mid-IR AGN fractions ≲0.3. With the corrected [O iv] luminosity, we calculate black hole accretion rates (BHARs) ranging from 10−5 M ⊙ yr−1 at low AGN fractions to 0.2 M ⊙ yr−1 at the highest AGN fractions. We find that using the [O iv] luminosity, without correcting for star formation, can lead to overestimation of the BHAR by up to a factor of 30 in starburst-dominated LIRGs. Finally, we show that the BHAR/star formation rate ratio increases by more than three orders of magnitude as a function of mid-IR AGN fraction in LIRGs.
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization

Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practices. Yet, data minimisation is one of the core data protection principles enshrined in the European Union’s General Data Protection Regulation (’GDPR’) and requires that only personal data that is adequate, relevant and limited to what is necessary is processed. However, the principle has seen limited adoption due to the lack of technical interpretation. In this work, we build on literature in machine learning and law to propose FIDO, a Framework for Inhibiting Data Overcollection. FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance. Concretely, FIDO provides a data collection stopping criterion by iteratively updating an estimate of the performance curve, or the relationship between dataset size and performance, as data is acquired. FIDO estimates the performance curve via a piecewise power law technique that models distinct phases of an algorithm’s performance throughout data collection separately. Empirical experiments show that the framework produces accurate performance curves and data collection stopping criteria across datasets and feature acquisition algorithms. We further demonstrate that many other families of curves systematically overestimate the return on additional data. Results and analysis from our investigation offer deeper insights into the relevant considerations when designing a data minimization framework, including the impacts of active feature acquisition on individual users and the feasability of user-specific data minimization. We conclude with practical recommendations for the implementation of data minimization.
On the cosmological constant of flat flrw spacetime

We consider an exponentiallyexpanding, ﬂat, Friedmann-Lemaˆıtre-Robertson-Walker (FLRW) Universe ﬁlled with a free Schroedinger ﬁeld. The probability ﬂuid of the latter is used to mimic the cosmological ﬂuid (baryonic plus dark matter), thus pro-viding the matter density and pressure terms in the corresponding Friedmann-Lemaˆıtre equations. We ﬁrst obtain the eigenfunctions of the Laplacian operator on ﬂat FLRW space. A quantum operator qualifying as a cosmological constant is deﬁned to act on the Schroedinger ﬁeld. We then compute the matrix representing the cosmological constant in the basis of Laplacian eigenfunctions. For an estimate of the orders of magnitude involved it sufﬁces to determine the expectation values of this operator. The expectation value that best ﬁts the experimentally measured value of the cosmological constant allows us to identify the quantum state of the Schroedinger ﬁeld that best represents the matter contents (baryonic and dark) of the current Universe. Finally, the operator inverse (modulo dimensional factors) to the one representing the cosmological constant provides a measure of the gravitational Boltzmann entropy of the Universe. We