Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks

Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learning environments. However, divergent and unstable behaviour have been long standing issues in DQNs. The unstable behaviour is often characterised by overestimation in the $Q$-values, commonly referred to as the overestimation bias. To address the overestimation bias and the divergent behaviour, a number of heuristic extensions have been proposed. Notably, multi-step updates have been shown to drastically reduce unstable behaviour while improving agent's training performance. However, agents are often highly sensitive to the selection of the multi-step update horizon ($n$), and our empirical experiments show that a poorly chosen static value for $n$ can in many cases lead to worse performance than single-step DQN. Inspired by the success of $n$-step DQN and the effects that multi-step updates have on overestimation bias, this paper proposes a new algorithm that we call `Elastic Step DQN' (ES-DQN). It dynamically varies the step size horizon in multi-step updates based on the similarity of states visited. Our empirical evaluation shows that ES-DQN out-performs $n$-step with fixed $n$ updates, Double DQN and Average DQN in several OpenAI Gym environments while at the same time alleviating the overestimation bias.
Measuring and Narrowing the Compositionality Gap in Language Models

We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often models can correctly answer all sub-problems but not generate the overall solution, a ratio we call the compositionality gap. We evaluate this ratio by asking multi-hop questions with answers that require composing multiple facts unlikely to have been observed together during pretraining. In the GPT-3 family of models, as model size increases we show that the single-hop question answering performance improves faster than the multi-hop performance does, therefore the compositionality gap does not decrease. This surprising result suggests that while more powerful models memorize and recall more factual knowledge, they show no corresponding improvement in their ability to perform this kind of compositional reasoning. We then demonstrate how elicitive prompting (such as chain of thought) narrows the compositionality gap by reasoning explicitly. We present a new method, self-ask, that further improves on chain of thought. In our method, the model explicitly asks itself (and answers) follow-up questions before answering the initial question. We finally show that self-ask's structured prompting lets us easily plug in a search engine to answer the follow-up questions, which additionally improves accuracy.
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding

Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging due to a high-dimensional space of multi-label assignment (tens of thousands of ICD codes) and the long-tail challenge: only a few codes (common diseases) are frequently assigned while most codes (rare diseases) are infrequently assigned. This study addresses the long-tail challenge by adapting a prompt-based fine-tuning technique with label semantics, which has been shown to be effective under few-shot setting. To further enhance the performance in medical domain, we propose a knowledge-enhanced longformer by injecting three domain-specific knowledge: hierarchy, synonym, and abbreviation with additional pretraining using contrastive learning. Experiments on MIMIC-III-full, a benchmark dataset of code assignment, show that our proposed method outperforms previous state-of-the-art method in 14.5% in marco F1 (from 10.3 to 11.8, P<0.001). To further test our model on few-shot setting, we created a new rare diseases coding dataset, MIMIC-III-rare50, on which our model improves marco F1 from 17.1 to 30.4 and micro F1 from 17.2 to 32.6 compared to previous method.
CORRELATIONS AT LARGE SCALES AND THE ONSET OF TURBULENCE IN THE FAST SOLAR WIND

We show that the scaling of structure functions of magnetic and velocity fields in a mostly highly Alfvénic fast solar wind stream depends strongly on the joint distribution of the dimensionless measures of cross helicity and residual energy. Already at very low frequencies, fluctuations that are both more balanced (cross helicity ∼0) and equipartitioned (residual energy ∼0) have steep structure functions reminiscent of “turbulent” scalings usually associated with the inertial range. Fluctuations that are magnetically dominated (residual energy ∼−1), and so have closely anti-aligned Elsasser-field vectors, or are imbalanced (cross helicity ∼1), and so have closely aligned magnetic and velocity vectors, have wide “1/f” ranges typical of fast solar wind. We conclude that the strength of nonlinear interactions of individual fluctuations within a stream, diagnosed by the degree of correlation in direction and magnitude of magnetic and velocity fluctuations, determines the extent of the 1/f region observed, and thus the onset scale for the turbulent cascade.
The Statistical Evaluation of Medical Tests for Classification and Prediction

1. Introduction 2. Measures of Accuracy for Binary Tests 3. Comparing Binary Tests and Regression Analysis 4. The Receiver Operating Characteristic Curve 5. Estimating the ROC Curve 6. Covariate Effects on Continuous and Ordinal Tests 7. Incomplete Data and Imperfect Reference Tests 8. Study Design and Hypothesis Testing 9. More Topics and Conclusions References/Bibliography Index
Pre-trained Adversarial Perturbations

Self-supervised pre-training has drawn increasing attention in recent years due to its superior performance on numerous downstream tasks after fine-tuning. However, it is well-known that deep learning models lack the robustness to adversarial examples, which can also invoke security issues to pre-trained models, despite being less explored. In this paper, we delve into the robustness of pre-trained models by introducing Pre-trained Adversarial Perturbations (PAPs), which are universal perturbations crafted for the pre-trained models to maintain the effectiveness when attacking fine-tuned ones without any knowledge of the downstream tasks. To this end, we propose a Low-Level Layer Lifting Attack (L4A) method to generate effective PAPs by lifting the neuron activations of low-level layers of the pre-trained models. Equipped with an enhanced noise augmentation strategy, L4A is effective at generating more transferable PAPs against fine-tuned models. Extensive experiments on typical pre-trained vision models and ten downstream tasks demonstrate that our method improves the attack success rate by a large margin compared with state-of-the-art methods.
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models

We explore the idea of compressing the prompts used to condition language models, and show that compressed prompts can retain a substantive amount of information about the original prompt. For severely compressed prompts, while fine-grained information is lost, abstract information and general sentiments can be retained with surprisingly few parameters, which can be useful in the context of decode-time algorithms for controllability and toxicity reduction. We explore contrastive conditioning to steer language model generation towards desirable text and away from undesirable text, and find that some complex prompts can be effectively compressed into a single token to guide generation. We also show that compressed prompts are largely compositional, and can be constructed such that they can be used to control independent aspects of generated text.
The Lifecycle of “Facts”: A Survey of Social Bias in Knowledge Graphs

Knowledge graphs are increasingly used in a plethora of downstream tasks or in the augmentation of statistical models to improve factuality. However, social biases are engraved in these representations and propagate downstream. We conducted a critical analysis of literature concerning biases at different steps of a knowledge graph lifecycle. We investigated factors introducing bias, as well as the biases that are rendered by knowledge graphs and their embedded versions afterward. Limitations of existing measurement and mitigation strategies are discussed and paths forward are proposed.
Understanding the Covariance Structure of Convolutional Filters

Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highly-structured operations like convolutions. Recent ViT-inspired convolutional networks such as ConvMixer and ConvNeXt use large-kernel depthwise convolutions whose learned filters have notable structure; this presents an opportunity to study their empirical covariances. In this work, we first observe that such learned filters have highly-structured covariance matrices, and moreover, we find that covariances calculated from small networks may be used to effectively initialize a variety of larger networks of different depths, widths, patch sizes, and kernel sizes, indicating a degree of model-independence to the covariance structure. Motivated by these findings, we then propose a learning-free multivariate initialization scheme for convolutional filters using a simple, closed-form construction of their covariance. Models using our initialization outperform those using traditional univariate initializations, and typically meet or exceed the performance of those initialized from the covariances of learned filters; in some cases, this improvement can be achieved without training the depthwise convolutional filters at all.
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems

Decision-based attacks construct adversarial examples against a machine learning (ML) model by making only hard-label queries. These attacks have mainly been applied directly to standalone neural networks. However, in practice, ML models are just one component of a larger learning system. We find that by adding a single preprocessor in front of a classifier, state-of-the-art query-based attacks are up to 7$\times$ less effective at attacking a prediction pipeline than at attacking the model alone. We explain this discrepancy by the fact that most preprocessors introduce some notion of invariance to the input space. Hence, attacks that are unaware of this invariance inevitably waste a large number of queries to re-discover or overcome it. We, therefore, develop techniques to (i) reverse-engineer the preprocessor and then (ii) use this extracted information to attack the end-to-end system. Our preprocessors extraction method requires only a few hundred queries, and our preprocessor-aware attacks recover the same efficacy as when attacking the model alone. The code can be found at https://github.com/google-research/preprocessor-aware-black-box-attack.
Adversarial Lagrangian Integrated Contrastive Embedding for Limited Size Datasets

Certain datasets contain a limited number of samples with highly various styles and complex structures. This study presents a novel adversarial Lagrangian integrated contrastive embedding (ALICE) method for small-sized datasets. First, the accuracy improvement and training convergence of the proposed pre-trained adversarial transfer are shown on various subsets of datasets with few samples. Second, a novel adversarial integrated contrastive model using various augmentation techniques is investigated. The proposed structure considers the input samples with different appearances and generates a superior representation with adversarial transfer contrastive training. Finally, multi-objective augmented Lagrangian multipliers encourage the low-rank and sparsity of the presented adversarial contrastive embedding to adaptively estimate the coefficients of the regularizers automatically to the optimum weights. The sparsity constraint suppresses less representative elements in the feature space. The low-rank constraint eliminates trivial and redundant components and enables superior generalization. The performance of the proposed model is verified by conducting ablation studies by using benchmark datasets for scenarios with small data samples.
A Milliarcsecond-accurate Position for Sagittarius A*

The absolute position of Sgr A*, the compact radio source at the center of the Milky Way, had been uncertain by several tens of milliarcseconds. Here we report improved astrometric measurements of the absolute position and proper motion of Sgr A*. Three epochs of phase-referencing observations were conducted with the Very Long Baseline Array for Sgr A* at 22 and 43 GHz in 2019 and 2020. Using extragalactic radio sources with submilliarcsecond-accurate positions as reference, we determined the absolute position of Sgr A* at a reference epoch 2020.0 to be at α(J2000) = 17h45m40. ˢ032863 ± 0. ˢ000016 and δ(J2000) = − 29°00′28. ″24260± 0. ″00047, with an updated proper motion −3.152 ± 0.011 and −5.586 ± 0.006 mas yr−1 in the easterly and northerly directions, respectively.
Effective Low-Energy Hamiltonians and Unconventional Landau-Level Spectrum of Monolayer C3N

We derive low-energy effective k·p Hamiltonians for monolayer C3N at the Γ and M points of the Brillouin zone, where the band edge in the conduction and valence band can be found. Our analysis of the electronic band symmetries helps to better understand several results of recent ab initio calculations for the optical properties of this material. We also calculate the Landau-level spectrum. We find that the Landau-level spectrum in the degenerate conduction bands at the Γ point acquires properties that are reminiscent of the corresponding results in bilayer graphene, but there are important differences as well. Moreover, because of the heavy effective mass, n-doped samples may host interesting electron–electron interaction effects.
Accurate Nonempirical Range-Separated Hybrid van der Waals Density Functional for Complex Molecular Problems, Solids, and Surfaces

Vivekanand Shukla§,1 Yang Jiao§,1 Jung-Hoon Lee, 3 Elsebeth Schröder, Jeffrey B. Neaton, 5, 6 and Per Hyldgaard ∗ Department of Microtechnology and Nanoscience – MC2, Chalmers University of Technology, SE-41296 Gothenburg, Sweden† Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720-7300, USA Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-7300, USA Department of Physics, University of California, Berkeley, California 94720-7300, USA Kavli Energy NanoScience Institute at Berkeley, Berkeley, California 94720-7300, USA (Dated: October 10, 2022)
Domain Adaptation for the Segmentation of Confidential Medical Images

Convolutional neural networks (CNNs) have led to significant improvements in the semantic segmentation of images. When source and target datasets come from different modalities, CNN performance suffers due to domain shift. In such cases data annotation in the target domain becomes necessary to maintain model performance. To circumvent the re-annotation process, unsupervised domain adaptation (UDA) is proposed to adapt a model to new modalities using solely unlabeled target data. Common UDA algorithms require access to source domain data during adaptation, which may not be feasible in medical imaging due to data sharing restrictions. In this work, we develop an algorithm for UDA where the source domain data is inaccessible during target adaptation. Our approach is based on encoding the source domain information into an internal distribution that is used to guide adaptation in the absence of source samples. We demonstrate the effectiveness of our algorithm by comparing it to state-of-the-art medical image semantic segmentation approaches on two medical image semantic segmentation datasets.
Field localization in submicron-sized clusters and its eﬀect on the ionization dynamics

Submicron-sized clusters are attractive targets in intense laser-cluster interaction experiments. The internal ﬁeld in the Mie scattering regime is inhomogeneous and size-dependent. Here we investigate the ﬁeld localization and its eﬀect on ionization in submicron-sized clusters. The diﬀractive focusing reduces the ionization threshold of these clusters and leads to an ionization hotspot at the rear of the clusters. At a higher intensity, a skin layer of plasma at the front surface shields the ﬁeld, stopping further rear side ionization. Interestingly, an unionized core with a plasma shell at a suitable condition may excite hybridization of plasmons, allowing transiently enhanced ﬁeld beyond the skin depth. Control and optimization of the ﬁeld localization and the resulting ionization may have practical importance for relevant laser-cluster experiments.
DROID: Driver-Centric Risk Object Identification

Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition. In this work, we approach the problem from the perspective of subjective risk. We operationalize subjective risk assessment by predicting driver behavior changes and identifying the cause of changes. To this end, we introduce a new task called driver-centric risk object identification (DROID), which uses egocentric video to identify object(s) influencing a driver's behavior, given only the driver's response as the supervision signal. We formulate the task as a cause-effect problem and present a novel two-stage DROID framework, taking inspiration from models of situation awareness and causal inference. A subset of data constructed from the Honda Research Institute Driving Dataset (HDD) is used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even compared with strong baseline models using this dataset. Additionally, we conduct extensive ablative studies to justify our design choices. Moreover, we demonstrate the applicability of DROID for risk assessment.
Application of optical coherence tomography for imaging and assessment of early dental caries lesions

The ability of an en-face optical coherence tomography (OCT) system to detect and quantitatively monitor the demineralization of teeth to produce enamel caries was determined and validated with an established method of quantifying demineralization, transverse microradiography (TMR). Factors that might affect the use of OCT to detect and quantify caries were also examined. We used an OCT system which can collect A-scans, B-scans (longitudinal images) and C-scans (en-face images). Development of dental caries was quantitatively monitored every 24 h for 3 days. Caries lesions were shown as volumes of reduced reflectivity in both the transversal and longitudinal images. It was observed that the reflectivity of tooth tissue decreased with increasing demineralization time. A linear correlation was observed between the mineral loss in enamel measured by TMR and the percentage reflectivity loss measured by OCT. Neither the presence of saliva, plague, Dacron gauze, plaque/Dacron gauze, nor lesion staining nor the level of ambient lighting significantly affected OCT detection and analysis of an incipient caries. We concluded that OCT could detect early enamel caries and quantitatively monitor the demineralization of the tissue. The reflectivity loss in tooth tissue during demineralization, measured by OCT, could be related to the amount of mineral lost during the demineralization.
The incomplete Analytic Hierarchy Process and Bradley-Terry model: (in)consistency and information retrieval

Several methods of preference modeling, ranking, voting and multi-criteria decision making include pairwise comparisons. It is usually simpler to compare two objects at a time, furthermore, some relations (e.g., the outcome of sports matches) are naturally known for pairs. This paper investigates and compares pairwise comparison models and the stochastic Bradley-Terry model. It is proved that they provide the same priority vectors for consistent (complete or incomplete) comparisons. For incomplete comparisons, all filling in levels are considered. Recent results identified the optimal subsets and sequences of multiplicative/additive/reciprocal pairwise comparisons for small sizes of items (up to n = 6). Simulations of this paper show that the same subsets and sequences are optimal in case of the Bradley-Terry and the Thurstone models as well. This, somehow surprising, coincidence suggests the existence of a more general result. Further models of information and preference theory are subject to future investigation in order to identify optimal subsets of input data.
How Large Language Models are Transforming Machine-Paraphrase Plagiarism

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work.However, the role of large autoregressive models in generating machine-paraphrased plagiarism and their detection is still incipient in the literature.This work explores T5 and GPT3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia.We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples.Our results suggest that large language models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves 66% F1-score in detecting paraphrases.We make our code, data, and findings publicly available to facilitate the development of detection solutions.
AlphaFold Distillation for Protein Design

Inverse protein folding, the process of designing sequences that fold into a specific 3D structure, is crucial in bio-engineering and drug discovery. Traditional methods rely on experimentally resolved structures, but these cover only a small fraction of protein sequences. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. However, these models are too slow for integration into the optimization loop of inverse folding models during training. To address this, we propose using knowledge distillation on folding model confidence metrics, such as pTM or pLDDT scores, to create a faster and end-to-end differentiable distilled model. This model can then be used as a structure consistency regularizer in training the inverse folding model. Our technique is versatile and can be applied to other design tasks, such as sequence-based protein infilling. Experimental results show that our method outperforms non-regularized baselines, yielding up to 3% improvement in sequence recovery and up to 45% improvement in protein diversity while maintaining structural consistency in generated sequences. Code is available at https://github.com/IBM/AFDistill
On cellular rational approximations to $\zeta(5)$

We analyse a certain family of cellular integrals, which are period integrals on the moduli space M 0 , 8 of curves of genus zero with eight marked points, which give rise to simultaneous rational approximations to ζ (3) and ζ (5). By exploiting the action of a large symmetry group on these integrals, we construct inﬁnitely many rational approximations p/q to ζ (5) satisfying