Miracle-dz/UniFAR-FTUs huggingface dataset:
Over-Fit: Noisy-Label Detection based on the Overfitted Model Property

Due to the increasing need to handle the noisy label problem in a massive dataset, learning with noisy labels has received much attention in recent years. As a promising approach, there have been recent studies to select clean training data by ﬁnding small-loss instances before a deep neural network overﬁts the noisy-label data. However, it is challenging to prevent overﬁtting. In this paper, we pro-pose a novel noisy-label detection algorithm by employing the property of overﬁtting on individual data points. To this end, we present two novel criteria that statistically measure how much each training sample abnormally affects the model and clean validation data. Using the criteria, our iterative algorithm removes noisy-label samples and retrains the model alternately until no further performance improvement is made. In experiments on multiple benchmark datasets, we demonstrate the validity of our algorithm and show that our algorithm outperforms the state-of-the-art methods when the exact noise rates are not given. Furthermore, we show that our method can not only be expanded to a real-world video dataset but also can be viewed as a regularization method to solve problems caused by overﬁtting.
Metrics for 3D Object Pointing and Manipulation in Virtual Reality: The Introduction and Validation of a Novel Approach in Measuring Human Performance

Assessing the performance of human movements during teleoperation and virtual reality (VR) is a challenging problem, particularly in 3D space, due to complex settings. Despite the presence of a multitude of measurements, a compelling standardized 3D metric is yet missing, aggravating interstudy comparability. Hence, evaluating human performance in virtual environments (VEs) is a long-standing research goal, and a performance metric that combines two or more measurements under one formulation remains largely unexplored, particularly in higher dimensions. The absence of such a metric is primarily attributed to the discrepancies between pointing and manipulation, the complex spatial variables in 3D, and the combination of translational and rotational movements.
Global Schauder theory for minimizers of the $H^s(\Omega)$ energy

We study the regularity of minimizers of the functional E(u) := [u]2Hs(Ω)+ ∫ Ω fu. This corresponds to understanding solutions for the regional fractional Laplacian in Ω ⊂ R . More precisely, we are interested on the global (up to the boundary) regularity of solutions, both in the case of free minimizers in H(Ω) (i.e., Neumann problem), or in the case of Dirichlet condition u ∈ H 0(Ω) when s > 1 2 . Our main result establishes the sharp regularity of solutions in both cases: u ∈ C(Ω) in the Neumann case, and u/δ ∈ C(Ω) in the Dirichlet case. Here, δ is the distance to ∂Ω, and α < αs, with αs ∈ (0, 1 − s) and 2s + αs > 1. We also show the optimality of our result: these estimates fail for α > αs, even when f and ∂Ω are C ∞.
Scalars are universal: Gauge-equivariant machine learning, structured like classical physics

There has been enormous progress in the last few years in designing conceivable (though not always practical) neural networks that respect the gauge symmetries—or coordinate freedom—of physical law. Some of these frameworks make use of irreducible representations, some make use of higher order tensor objects, and some apply symmetry-enforcing constraints. Diﬀerent physical laws obey diﬀerent combinations of fundamental symmetries, but a large fraction (possibly all) of classical physics is equivariant to translation, rotation, reﬂection (parity), boost (relativity), and permutations. Here we show that it is simple to parameterize universally approximating polynomial functions that are equiv-ariant under these symmetries, or under the Euclidean, Lorentz, and Poincar´e groups, at any dimensionality d . The key observation is that nonlinear O( d )-equivariant (and related-group-equivariant) functions can be expressed in terms of a lightweight collection of scalars—scalar products and scalar contractions of the scalar, vector, and tensor inputs. These results demonstrate theoretically that gauge-invariant deep learning models for classical physics with good scaling for large problems are feasible right now.
Determinantal Beam Search

Beam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does not reflect interactions between candidates. Empirically, this leads to sets often exhibiting high overlap, e.g., strings may differ by only a single word. Yet in use-cases that call for multiple solutions, a diverse or representative set is often desired. To address this issue, we propose a reformulation of beam search, which we call determinantal beam search. Determinantal beam search has a natural relationship to determinantal point processes (DPPs), models over sets that inherently encode intra-set interactions. By posing iterations in beam search as a series of subdeterminant maximization problems, we can turn the algorithm into a diverse subset selection process. In a case study, we use the string subsequence kernel to explicitly encourage n-gram coverage in text generated from a sequence model. We observe that our algorithm offers competitive performance against other diverse set generation strategies in the context of language generation, while providing a more general approach to optimizing for diversity.
Audio Attacks and Defenses against AED Systems - A Practical Study

In this paper, we evaluate deep learning-enabled AED systems against evasion attacks based on adversarial examples. We test the robustness of multiple security critical AED tasks, implemented as CNNs classifiers, as well as existing third-party Nest devices, manufactured by Google, which run their own black-box deep learning models. Our adversarial examples use audio perturbations made of white and background noises. Such disturbances are easy to create, to perform and to reproduce, and can be accessible to a large number of potential attackers, even non-technically savvy ones. We show that an adversary can focus on audio adversarial inputs to cause AED systems to misclassify, achieving high success rates, even when we use small levels of a given type of noisy disturbance. For instance, on the case of the gunshot sound class, we achieve nearly 100% success rate when employing as little as 0.05 white noise level. Similarly to what has been previously done by works focusing on adversarial examples from the image domain as well as on the speech recognition domain. We then, seek to improve classifiers' robustness through countermeasures. We employ adversarial training and audio denoising. We show that these countermeasures, when applied to audio input, can be successful, either in isolation or in combination, generating relevant increases of nearly fifty percent in the performance of the classifiers when these are under attack.
Using Integrated Gradients to explain Linguistic Acceptability learnt by BERT

BERT [1] has been a breakthrough in language understanding by leveraging the multi-head self-attention mechanism [8] in its architecture. To the best of our knowledge this work is the first to leverage Layer Integrated Gradients Attribution Scores (LIGAS) to explain the Linguistic Acceptability criteria that are learnt by BERT on the Corpus of Linguistic Acceptability (CoLA) [9] benchmark dataset. Our experiments on 5 different categories of sentences lead to the following interesting findings: 1) LIGAS for Linguistically Acceptable (LA) sentences are significantly smaller in comparison to Linguistically Unacceptable (LUA) sentences, 2) There are specific subtrees of the Constituency Parse Tree (CPT) for LA and LUA sentences which contribute larger LIGAS, 3) Across the different categories of sentences we observed around 88% to 100% of the Correctly classified sentences had positive LIGAS, indicating a strong positive relationship to the prediction confidence of the model, and 4) Around 57% of the Misclassified sentences had positive LIGAS, which we believe can become correctly classified sentences if the LIGAS are parameterized in the loss function of the model.
RRULES: An improvement of the RULES rule-based classifier

RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IFTHEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more effective mechanism to detect irrelevant rules, at the same time that checks the stopping conditions more often. This results in a more compact rule set containing more general rules which prevent overfitting the training set and obtain a higher test accuracy. Moreover, the results show that RRULES outperforms the original algorithm by reducing the coverage rate up to a factor of 7 while running twice or three times faster consistently over several datasets.
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization

Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model-or DL-based.
Landscape Correspondence of Empirical and Population Risks in the Eigendecomposition Problem

Spectral methods include a family of algorithms related to the eigenvectors of certain data-generated matrices. In this work, we are interested in studying the geometric landscape of the eigendecomposition problem in various spectral methods. In particular, we first extend known results regarding the landscape at critical points to larger regions near the critical points in a special case of finding the leading eigenvector of a symmetric matrix. For a more general eigendecomposition problem, inspired by recent findings on the connection between the landscapes of empirical risk and population risk, we then build a novel connection between the landscape of an eigendecomposition problem that uses random measurements and the one that uses the true data matrix. We also apply our theory to a variety of low-rank matrix optimization problems and conduct a series of simulations to illustrate our theoretical findings.
Incorporating External POS Tagger for Punctuation Restoration

Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide informative tags, suggesting each input token's syntactic role, which has been shown to be beneficial for the punctuation restoration task. In this work, we incorporate an external POS tagger and fuse its predicted labels into the existing language model to provide syntactic information. Besides, we propose sequence boundary sampling (SBS) to learn punctuation positions more efficiently as a sequence tagging task. Experimental results show that our methods can consistently obtain performance gains and achieve a new state-of-the-art on the common IWSLT benchmark. Further ablation studies illustrate that both large pre-trained language models and the external POS tagger take essential parts to improve the model's performance.
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI

Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a tool for `fairwashing` by misleading users into trusting biased or incorrect models. In this paper, we created a framework for evaluating explainable AI tools with respect to their capabilities for detecting and addressing issues of bias and fairness as well as their capacity to communicate these results to their users clearly. We found that despite their capabilities in simplifying and explaining model behavior, many prominent XAI tools lack features that could be critical in detecting bias. Developers can use our framework to suggest modifications needed in their toolkits to reduce issues likes fairwashing.
Hierarchically Regularized Deep Forecasting

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy. The main challenge is to exploit the hierarchical correlations to simultaneously obtain good prediction accuracy for time series at different levels of the hierarchy. In this paper, we propose a new approach for hierarchical forecasting which consists of two components. First, decomposing the time series along a global set of basis time series and modeling hierarchical constraints using the coefficients of the basis decomposition. And second, using a linear autoregressive model with coefficients that vary with time. Unlike past methods, our approach is scalable (inference for a specific time series only needs access to its own history) while also modeling the hierarchical structure via (approximate) coherence constraints among the time series forecasts. We experiment on several public datasets and demonstrate significantly improved overall performance on forecasts at different levels of the hierarchy, compared to existing state-of-the-art hierarchical models.
Predicting Higher Education Throughput in South Africa Using a Tree-Based Ensemble Technique

We use gradient boosting machines and logistic regression to predict academic throughput at a South African university. The results highlight the significant influence of socio-economic factors and field of study as predictors of throughput. We further find that socio-economic factors become less of a predictor relative to the field of study as the time to completion increases. We provide recommendations on interventions to counteract the identified effects, which include academic, psychosocial and financial support.
Multistream Validnet: Improving 6D Object Pose Estimation by Automatic Multistream Validation

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Siléane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.
Graph Contrastive Learning Automated

Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance. Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data. That significantly limits the more general applicability of GraphCL. Aiming to fill in this crucial gap, this paper proposes a unified bi-level optimization framework to automatically, adaptively and dynamically select data augmentations when performing GraphCL on specific graph data. The general framework, dubbed JOint Augmentation Optimization (JOAO), is instantiated as min-max optimization. The selections of augmentations made by JOAO are shown to be in general aligned with previous"best practices"observed from handcrafted tuning: yet now being automated, more flexible and versatile. Moreover, we propose a new augmentation-aware projection head mechanism, which will route output features through different projection heads corresponding to different augmentations chosen at each training step. Extensive experiments demonstrate that JOAO performs on par with or sometimes better than the state-of-the-art competitors including GraphCL, on multiple graph datasets of various scales and types, yet without resorting to any laborious dataset-specific tuning on augmentation selection. We release the code at https://github.com/Shen-Lab/GraphCL_Automated.
Approaches to Enhance Natural Killer Cell-Based Immunotherapy for Pediatric Solid Tumors

Simple Summary Children with metastatic solid tumors typically have a very poor prognosis, especially when the cancer returns or is resistant to upfront treatment. There is hope that the cells in the immune system can be programmed to help patients recognize and eliminate their cancer. Natural killer (NK) cells are an important component of the immune system designed to eliminate virally infected cells and tumors. In the last 20 years, advances in cell manufacturing have allowed investigators to grow NK cells in the laboratory to high numbers and engineer the cells to become highly potent. While some clinical trials using NK cells in children with solid tumors have shown promise, we still have much to learn on how to use NK cells effectively. This review will discuss our current understanding of NK cell biology and its relevance to solid tumors that commonly affect children. Abstract Treatment of metastatic pediatric solid tumors remain a significant challenge, particularly in relapsed and refractory settings. Standard treatment has included surgical resection, radiation, chemotherapy, and, in the case of neuroblastoma, immunotherapy. Despite such intensive therapy, cancer recurrence is common, and most tumors become refractory to prior therapy, leaving patients with few conventional treatment options. Natural killer (NK) cells are non-major histocompatibility complex (MHC)-restricted lymphocytes that boast several complex killing mechanisms but at an added advantage of not causing graft-versus-host disease, making use of allogeneic NK cells a potential therapeutic option. On top of their killing capacity, NK cells also produce several cytokines and growth factors that act as key regulators of the adaptive immune system, positioning themselves as ideal effector cells for stimulating heavily pretreated immune systems. Despite this promise, clinical efficacy of adoptive NK cell therapy to date has been inconsistent, prompting a detailed understanding of the biological pathways within NK cells that can be leveraged to develop “next generation” NK cell therapies. Here, we review advances in current approaches to optimizing the NK cell antitumor response including combination with other immunotherapies, cytokines, checkpoint inhibition, and engineering NK cells with chimeric antigen receptors (CARs) for the treatment of pediatric solid tumors.
Potential Uses of Musaceae Wastes: Case of Application in the Development of Bio-Based Composites

The Musaceae family has significant potential as a source of lignocellulosic fibres and starch from the plant’s bunches and pseudostems. These materials, which have traditionally been considered waste, can be used to produce fully bio-based composites to replace petroleum-derived synthetic plastics in some sectors such as packaging, the automotive industry, and implants. The fibres extracted from Musaceae have mechanical, thermal, and physicochemical properties that allow them to compete with other natural fibres such as sisal, henequen, fique, and jute, among others, which are currently used in the preparation of bio-based composites. Despite the potential use of Musaceae residues, there are currently not many records related to bio-based composites’ developments using starches, flours, and lignocellulosic fibres from banana and plantain pseudostems. In this sense, the present study focusses on the description of the Musaceae components and the review of experimental reports where both lignocellulosic fibre from banana pseudostem and flour and starch are used with different biodegradable and non-biodegradable matrices, specifying the types of surface modification, the processing techniques used, and the applications achieved.
African Herbal Medicines: Adverse Effects and Cytotoxic Potentials with Different Therapeutic Applications

The African continent is naturally endowed with various plant species with nutritional and medicinal benefits. About 80% of the people in developing countries rely on folk medicines to treat different diseases because of indigenous knowledge, availability, and cost-effectiveness. Extensive research studies have been conducted on the medicinal uses of African plants, however, the therapeutic potentials of some of these plants has remained unexploited. Over the years, several studies have revealed that some of these African floras are promising candidates for the development of novel drugs. Despite the plethora of studies on medicinal plant research in Africa, there is still little scientific data supporting the folkloric claims of these plants. Besides, safety in the use of folk medicines has been a major public health concern over the year. Therefore, it has become mandatory that relevant authority should take measures in safeguarding the populace on the use of herbal mixtures. Thus, the present review extracted relevant information from different scientific databases and highlighted some problems associated with folk medicines, adverse effects on reproductive systems, issue about safety due to the toxicity of some plants and their toxicity effects with potential therapeutic benefits are discussed.