Reviewerly/exHarmony huggingface dataset:
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations

Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient, often linear-time, inference of queries such as marginals and most probable explanations (MPE). However, marginal MAP, which is central to many decision-making problems, remains a hard query for PCs unless they satisfy highly restrictive structural constraints. In this paper, we develop a pruning algorithm that removes parts of the PC that are irrelevant to a marginal MAP query, shrinking the PC while maintaining the correct solution. This pruning technique is so effective that we are able to build a marginal MAP solver based solely on iteratively transforming the circuit -- no search is required. We empirically demonstrate the efficacy of our approach on real-world datasets.
Results of improved fractional/integer order PDE-based binarization  model

In this report, we present and compare the results of an improved fractional and integer order partial differential equation (PDE)-based binarization scheme. The improved model incorporates a diffusion term in addition to the edge and binary source terms from the previous formulation. Furthermore, logarithmic local contrast edge normalization and combined isotropic and anisotropic edge detection enables simultaneous bleed-through elimination with faded text restoration for degraded document images. Comparisons of results with state-of-the-art PDE methods show improved and superior results.
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities

Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the purpose of prediction (of ground-truth facts) to its use for prescriptive modeling. What is meant by this is the task of learning a model that stipulates appropriate decisions about the right course of action in real-world scenarios: Which medical therapy should be applied? Should this person be hired for the job? As argued in this article, prescriptive modeling comes with new technical conditions for learning and new demands regarding reliability, responsibility, and the ethics of decision making. Therefore, to support the data-driven design of decision-making agents that act in a rational but at the same time responsible manner, a rigorous methodological foundation of prescriptive ML is needed. The purpose of this short paper is to elaborate on specific characteristics of prescriptive ML and to highlight some key challenges it implies. Besides, drawing connections to other branches of contemporary AI research, the grounding of prescriptive ML in a (generalized) decision-theoretic framework is advocated.
Godot Reinforcement Learning Agents

We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows the design, creation and learning of agent behaviors in challenging 2D and 3D environments with various on-policy and off-policy Deep RL algorithms. We provide a standard Gym interface, with wrappers for learning in the Ray RLlib and Stable Baselines RL frameworks. This allows users access to over 20 state of the art on-policy, off-policy and multi-agent RL algorithms. The framework is a versatile tool that allows researchers and game designers the ability to create environments with discrete, continuous and mixed action spaces. The interface is relatively performant, with 12k interactions per second on a high end laptop computer, when parallized on 4 CPU cores. An overview video is available here: https://youtu.be/g1MlZSFqIj4
Robust Visual Tracking by Segmentation

Estimating the target extent poses a fundamental challenge in visual object tracking. Typically, trackers are box-centric and fully rely on a bounding box to define the target in the scene. In practice, objects often have complex shapes and are not aligned with the image axis. In these cases, bounding boxes do not provide an accurate description of the target and often contain a majority of background pixels. We propose a segmentation-centric tracking pipeline that not only produces a highly accurate segmentation mask, but also internally works with segmentation masks instead of bounding boxes. Thus, our tracker is able to better learn a target representation that clearly differentiates the target in the scene from background content. In order to achieve the necessary robustness for the challenging tracking scenario, we propose a separate instance localization component that is used to condition the segmentation decoder when producing the output mask. We infer a bounding box from the segmentation mask, validate our tracker on challenging tracking datasets and achieve the new state of the art on LaSOT with a success AUC score of 69.7%. Since most tracking datasets do not contain mask annotations, we cannot use them to evaluate predicted segmentation masks. Instead, we validate our segmentation quality on two popular video object segmentation datasets.
The universe as a nonlinear quantum simulation: Large $n$ limit of the central spin model

We investigate models of nonlinear qubit evolution based on mappings to an $n$-qubit central spin model (CSM) in the large $n$ limit, where mean field theory is exact. Extending a theorem of Erd\"os and Schlein, we establish that the CSM is rigorously dual to a nonlinear qubit when $n \rightarrow \infty$. The duality supports a type of nonlinear quantum computation in systems, such as a condensate, where a large number of ancilla couple symmetrically to a "central" qubit. It also enables a gate-model implementation of nonlinear quantum simulation with a rigorous error bound. Two variants of the model, with and without coupling between ancilla, map to effective models with different nonlinearity and symmetry. The duality discussed here might also be interesting from a quantum foundations perspective. There has long been interest in whether quantum mechanics might possess some type of small, unobserved nonlinearity. If not, what is the principle prohibiting it? The duality implies that there is not a sharp distinction between universes evolving according to linear and nonlinear quantum mechanics: A one-qubit "universe" prepared in a pure state $| \varphi \rangle $ at the time of the big bang and symmetrically coupled to ancilla prepared in the same state, would appear to evolve nonlinearly for any finite time $t>0$ as long as there are exponentially many ancilla $n \gg {\rm exp}(O(t))$.
Some Stylometric Remarks on Ovid's Heroides and the Epistula Sapphus

This article aims to contribute to two well-worn areas of debate in classical Latin philology, relating to Ovid's Heroides. The first is the question of the authenticity (and, to a lesser extent the correct position) of the letter placed fifteenth by almost every editor -- the so-called Epistula Sapphus (henceforth ES). The secondary question, although perhaps now less fervently debated, is the authenticity of the 'Double Heroides', placed by those who accept them as letters 16-21. I employ a variety of methods drawn from the domain of computational stylometry to consider the poetics and the lexico-grammatical features of these elegiac poems in the broader context of a corpus of 'shorter' (from 20 to 546 lines) elegiac works from five authors (266 poems in all) comprising more or less all of the non-fragmentary classical corpus. Based on a variety of techniques, every measure gives clear indication that the poetic style of the Heroides is Ovidian, but distinctive; they can be accurately isolated from Ovid more broadly. The Single and Double Heroides split into two clear groups, with the ES grouped consistently with the single letters. Furthermore, by comparing the style of the letters with the 'early' (although there are complications in this label) works of the Amores and the late works of the Ex Ponto, the evidence supports sequential composition -- meaning that the ES is correctly placed -- and, further, supports the growing consensus that the double letters were composed significantly later, in exile.
Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection

Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared to the conventional models and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection.
Omni-Seg: A Single Dynamic Network for Multi-label Renal Pathology Image Segmentation using Partially Labeled Data

Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer pathology (i.e., tumor segmentation and characterization). In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously, as a multi-label setting. The prior arts typically needed to train multiple segmentation networks in order to match the domain-specific knowledge for heterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximal tubular, distal tubular, peritubular capillaries, and arteries). In this paper, we propose a dynamic single segmentation network (Omni-Seg) that learns to segment multiple tissue types using partially labeled images (i.e., only one tissue type is labeled for each training image) for renal pathology. By learning from ~150,000 patch-wise pathological images from six tissue types, the proposed Omni-Seg network achieved superior segmentation accuracy and less resource consumption when compared to the previous the multiple-network and multi-head design. In the testing stage, the proposed method obtains "completely labeled" tissue segmentation results using only "partially labeled" training images. The source code is available at https://github.com/ddrrnn123/Omni-Seg
A study on some approximations on the average number of the LLL bases in higher dimensions

There is a result related to the average number of the $(\delta, \eta)$-LLL bases in dimension $n$ in theoretical sense but the formula seems to be complicated and computing in high dimension takes a long time. In practical sense, we suggest some approximations which can be computed by just storing some constants and computing relatively simple exponential functions.
Automatic inference of fault tree models via multi-objective evolutionary algorithms

Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems. Traditionally, fault tree (FT) models are built manually together with domain experts, considered a time-consuming process prone to human errors. With Industry 4.0, there is an increasing availability of inspection and monitoring data, making techniques that enable knowledge extraction from large data sets relevant. Thus, our goal with this work is to propose a data-driven approach to infer efficient FT structures that achieve a complete representation of the failure mechanisms contained in the failure data set without human intervention. Our algorithm, the FT-MOEA, based on multi-objective evolutionary algorithms, enables the simultaneous optimization of different relevant metrics such as the FT size, the error computed based on the failure data set and the Minimal Cut Sets. Our results show that, for six case studies from the literature, our approach successfully achieved automatic, efficient, and consistent inference of the associated FT models. We also present the results of a parametric analysis that tests our algorithm for different relevant conditions that influence its performance, as well as an overview of the data-driven methods used to automatically infer FT models.
Variational quantum circuits to prepare low energy symmetry states

We explore how to build quantum circuits that compute the lowest energy state corresponding to a given Hamiltonian within a Symmetry subspace by explicitly encoding it into the circuit. We create an explicit unitary and a variationally trained unitary that maps any vector output by ansatz A(~α) from a defined subspace to a vector in the symmetry space. The parameters are trained varitionally to minimize the energy thus keeping the output within the labelled symmetry value. The method was tested for a spin XXZ hamiltonian using rotation and reflection symmetry and H2 hamiltonian within S_z = 0 subspace using S^2 symmetry. We have found the variationally trained unitary surprisingly giving very good results with very low depth circuits and can thus be used to prepare symmetry states within near term quantum computers.
Neuron-based Pruning of Deep Neural Networks with Better Generalization  using Kronecker Factored Curvature Approximation

Existing methods of pruning deep neural networks focus on removing unnecessary parameters of the trained network and fine tuning the model afterwards to find a good solution that recovers the initial performance of the trained model. Unlike other works, our method pays special attention to the quality of the solution in the compressed model and inference computation time by pruning neurons. The proposed algorithm directs the parameters of the compressed model toward a flatter solution by exploring the spectral radius of Hessian which results in better generalization on unseen data. Moreover, the method does not work with a pre-trained network and performs training and pruning simultaneously. Our result shows that it improves the state-of-the-art results on neuron compression. The method is able to achieve very small networks with small accuracy degradation across different neural network models.
Balancing Fairness and Robustness via Partial Invariance

The Invariant Risk Minimization (IRM) framework aims to learn invariant features from a set of environments for solving the out-of-distribution (OOD) generalization problem. The underlying assumption is that the causal components of the data generating distributions remain constant across the environments or alternately, the data "overlaps" across environments to find meaningful invariant features. Consequently, when the "overlap" assumption does not hold, the set of truly invariant features may not be sufficient for optimal prediction performance. Such cases arise naturally in networked settings and hierarchical data-generating models, wherein the IRM performance becomes suboptimal. To mitigate this failure case, we argue for a partial invariance framework. The key idea is to introduce flexibility into the IRM framework by partitioning the environments based on hierarchical differences, while enforcing invariance locally within the partitions. We motivate this framework in classification settings with causal distribution shifts across environments. Our results show the capability of the partial invariant risk minimization to alleviate the trade-off between fairness and risk in certain settings.
On the Size and Width of the Decoder of a Boolean Threshold Autoencoder

In this paper, we study the size and width of autoencoders consisting of Boolean threshold functions, where an autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder which transforms the low-dimensional vector back to the original input vector exactly (or approximately). We focus on the decoder part, and show that $\Omega(\sqrt{Dn/d})$ and $O(\sqrt{Dn})$ nodes are required to transform $n$ vectors in $d$-dimensional binary space to $D$-dimensional binary space. We also show that the width can be reduced if we allow small errors, where the error is defined as the average of the Hamming distance between each vector input to the encoder part and the resulting vector output by the decoder.
SARS-CoV-2's closest relative, RaTG13, was generated from a bat  transcriptome not a fecal swab: implications for the origin of COVID-19

RaTG13 is the closest related coronavirus genome phylogenetically to SARS-CoV-2, consequently understanding its provenance is of key importance to understanding the origin of the COVID-19 pandemic. The RaTG13 NGS dataset is attributed to a fecal swab from the intermediate horseshoe bat Rhinolophus affinis. However, sequence analysis reveals that this is unlikely. Metagenomic analysis using Metaxa2 shows that only 10.3 % of small subunit (SSU) rRNA sequences in the dataset are bacterial, inconsistent with a fecal sample, which are typically dominated by bacterial sequences. In addition, the bacterial taxa present in the sample are inconsistent with fecal material. Assembly of mitochondrial SSU rRNA sequences in the dataset produces a contig 98.7 % identical to R.affinis mitochondrial SSU rRNA, indicating that the sample was generated from this or a closely related species. 87.5 % of the NGS reads map to the Rhinolophus ferrumequinum genome, the closest bat genome to R.affinis available. In the annotated genome assembly, 62.2 % of mapped reads map to protein coding genes. These results clearly demonstrate that the dataset represents a Rhinolophus sp. transcriptome, and not a fecal swab sample. Overall, the data show that the RaTG13 dataset was generated by the Wuhan Institute of Virology (WIV) from a transcriptome derived from Rhinolophus sp. tissue or cell line, indicating that RaTG13 was in live culture. This raises the question of whether the WIV was culturing additional unreported coronaviruses closely related to SARS-CoV-2 prior to the pandemic. The implications for the origin of the COVID-19 pandemic are discussed.
Compositional Generalization Requires Compositional Parsers

A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into unseen sequences. We present a systematic comparison of sequence-to-sequence models and models guided by compositional principles on the recent COGS corpus (Kim and Linzen, 2020). Though seq2seq models can perform well on lexical tasks, they perform with near-zero accuracy on structural generalization tasks that require novel syntactic structures; this holds true even when they are trained to predict syntax instead of semantics. In contrast, compositional models achieve near-perfect accuracy on structural generalization; we present new results confirming this from the AM parser (Groschwitz et al., 2021). Our findings show structural generalization is a key measure of compositional generalization and requires models that are aware of complex structure.
DeepGalaxy: Testing Neural Network Verifiers via Two-Dimensional Input Space Exploration

Deep neural networks (DNNs) are widely developed and applied in many areas, and the quality assurance of DNNs is critical. Neural network verification (NNV) aims to provide formal guarantees to DNN models. Similar to traditional software, neural network verifiers could also contain bugs, which would have a critical and serious impact, especially in safety-critical areas. However, little work exists on validating neural network verifiers. In this work, we propose DeepGalaxy, an automated approach based on differential testing to tackle this problem. Specifically, we (1) propose a line of mutation rules, including model level mutation and specification level mutation, to effectively explore the two-dimensional input space of neural network verifiers; and (2) propose heuristic strategies to select test cases. We leveraged our implementation of DeepGalaxy to test three state-of-the-art neural network verifies, Marabou, Eran, and Neurify. The experimental results support the efficiency and effectiveness of DeepGalaxy. Moreover, five unique unknown bugs were discovered
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report  Summarization

The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report. These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section. Prior research on radiology report summarization has focused on single-step end-to-end models -- which subsume the task of salient content acquisition. To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations. First, we design a two-step approach: extractive summarization followed by abstractive summarization. Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. Experiments on a publicly available radiology report dataset show our novel approach leads to a more precise summary compared to single-step and to two-step-with-single-extractive-process baselines with an overall improvement in F1 score Of 3-4%.
On the Universality of Graph Neural Networks on Large Random Graphs

We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" models known as c-GNNs, which directly enables a study of their approximation power on random graph models. In the absence of input node features however, just as GNNs are limited by the Weisfeiler-Lehman isomorphism test, c-GNNs will be severely limited on simple random graph models. For instance, they will fail to distinguish the communities of a well-separated Stochastic Block Model (SBM) with constant degree function. Thus, we consider recently proposed architectures that augment GNNs with unique node identifiers, referred to as Structural GNNs here (SGNNs). We study the convergence of SGNNs to their continuous counterpart (c-SGNNs) in the large random graph limit, under new conditions on the node identifiers. We then show that c-SGNNs are strictly more powerful than c-GNNs in the continuous limit, and prove their universality on several random graph models of interest, including most SBMs and a large class of random geometric graphs. Our results cover both permutation-invariant and permutation-equivariant architectures.