Miracle-dz/UniFAR-FTUs huggingface dataset:
Procedural Generalization by Planning with Self-Supervised World Models

One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well understood because existing work has focused on model-free agents when benchmarking generalization. Here, we explicitly measure the generalization ability of model-based agents in comparison to their model-free counterparts. We focus our analysis on MuZero (Schrittwieser et al., 2020), a powerful model-based agent, and evaluate its performance on both procedural and task generalization. We identify three factors of procedural generalization -- planning, self-supervised representation learning, and procedural data diversity -- and show that by combining these techniques, we achieve state-of-the art generalization performance and data efficiency on Procgen (Cobbe et al., 2019). However, we find that these factors do not always provide the same benefits for the task generalization benchmarks in Meta-World (Yu et al., 2019), indicating that transfer remains a challenge and may require different approaches than procedural generalization. Overall, we suggest that building generalizable agents requires moving beyond the single-task, model-free paradigm and towards self-supervised model-based agents that are trained in rich, procedural, multi-task environments.
Designing Inherently Interpretable Machine Learning Models

Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings. In general, the inherently IML models should be adopted because of their transparency and explainability, while black-box models with model-agnostic explainability can be more difficult to defend under regulatory scrutiny. For assessing inherent interpretability of a machine learning model, we propose a qualitative template based on feature effects and model architecture constraints. It provides the design principles for high-performance IML model development, with examples given by reviewing our recent works on ExNN, GAMI-Net, SIMTree, and the Aletheia toolkit for local linear interpretability of deep ReLU networks. We further demonstrate how to design an interpretable ReLU DNN model with evaluation of conceptual soundness for a real case study of predicting credit default in home lending. We hope that this work will provide a practical guide of developing inherently IML models in high risk applications in banking industry, as well as other sectors.
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

Graph Neural Networks (GNNs) are a paradigm-shifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as flocking, multi-agent path planning and cooperative coverage. However, the policies derived through GNN-based learning schemes have not yet been deployed to the real-world on physical multi-robot systems. In this work, we present the design of a system that allows for fully decentralized execution of GNN-based policies. We create a framework based on ROS2 and elaborate its details in this paper. We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication. A video demonstration of this case-study can be found online11youtube.com/watch? v=COh-WLn4i04.
Supercurrent diode effect, spin torques, and robust zero-energy peak in planar half-metallic trilayers

Klaus Halterman,1, ∗ Mohammad Alidoust,2 Ross Smith,3 and Spencer Starr4 1Michelson Lab, Physics Division, Naval Air Warfare Center, China Lake, California 93555 2Department of Physics, Norwegian University of Science and Technology, N-7491 Trondheim, Norway 3Army Research Lab, Aberdeen Proving Ground, MD 21005 4General Dynamics Information Technology, Falls Church, VA 22042 (Dated: November 3, 2021)
MillenniumDB: A Persistent, Open-Source, Graph Database

In this systems paper, we present MillenniumDB: a novel graph database engine that is modular, persistent, and open source. MillenniumDB is based on a graph data model, which we call domain graphs, that provides a simple abstraction upon which a variety of popular graph models can be supported. The engine itself is founded on a combination of tried and tested techniques from relational data management, state-of-the-art algorithms for worst-case-optimal joins, as well as graph-specific algorithms for evaluating path queries. In this paper, we present the main design principles underlying MillenniumDB, describing the abstract graph model and query semantics supported, the concrete data model and query syntax implemented, as well as the storage, indexing, query planning and query evaluation techniques used. We evaluate MillenniumDB over real-world data and queries from the Wikidata knowledge graph, where we find that it outperforms other popular persistent graph database engines (including both enterprise and open source alternatives) that support similar query features.
MixFace: Improving Face Verification Focusing on Fine-grained Conditions

The performance of face recognition has become saturated for public benchmark datasets such as LFW, CFP-FP, and AgeDB, owing to the rapid advances in CNNs. However, the effects of faces with various fine-grained conditions on FR models have not been investigated because of the absence of such datasets. This paper analyzes their effects in terms of different conditions and loss functions using K-FACE, a recently introduced FR dataset with fine-grained conditions. We propose a novel loss function, MixFace, that combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness is demonstrated experimentally on various benchmark datasets.
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees

We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the parameters of a Bayesian posterior distribution. This viewpoint explicitly casts inference algorithms under the framework of numerical optimisation. We show that common approximations to Newton's method from the optimisation literature, namely Gauss-Newton and quasi-Newton methods (e.g., the BFGS algorithm), are still valid under this 'Bayes-Newton' framework. This leads to a suite of novel algorithms which are guaranteed to result in positive semi-definite (PSD) covariance matrices, unlike standard VI and EP. Our unifying viewpoint provides new insights into the connections between various inference schemes. All the presented methods apply to any model with a Gaussian prior and non-conjugate likelihood, which we demonstrate with (sparse) Gaussian processes and state space models.
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression

Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themselves normally require large storage space and thus contaminate the advantage brought by accurate data density estimation. To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch. We formalise this setting as that of One-Shot Online Adaptation (OSOA) of DGMs for lossless compression and propose a vanilla algorithm under this setting. Experimental results show that vanilla OSOA can save significant time versus training bespoke models and space versus using one model for all targets. With the same adaptation step number or adaptation time, it is shown vanilla OSOA can exhibit better space efficiency, e.g., $47\%$ less space, than fine-tuning the pretrained model and saving the fine-tuned model. Moreover, we showcase the potential of OSOA and motivate more sophisticated OSOA algorithms by showing further space or time efficiency with multiple updates per batch and early stopping.
Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching

Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated data. The distinguishing case of low performance of multilingual models in CS is the intra-sentence mixing of languages leading to switch points. We first benchmark two sequence labeling tasks -- POS and NER on 4 different language pairs with a suite of pretrained models to identify the problems and select the best performing model, char-BERT, among them (addressing (1)). We then propose a self training method to repurpose the existing pretrained models using a switch-point bias by leveraging unannotated data (addressing (2)). We finally demonstrate that our approach performs well on both tasks by reducing the gap between the switch point performance while retaining the overall performance on two distinct language pairs in both the tasks. Our code is available here: https://github.com/PC09/EMNLP2021-Switch-Point-biased-Self-Training.
Brain dynamics via Cumulative Auto-Regressive Self-Attention

Multivariate dynamical processes can often be intuitively described by a weighted connectivity graph between components representing each individual time-series. Even a simple representation of this graph as a Pearson correlation matrix may be informative and predictive as demonstrated in the brain imaging literature. However, there is a consensus expectation that powerful graph neural networks (GNNs) should perform better in similar settings. In this work, we present a model that is considerably shallow than deep GNNs, yet outperforms them in predictive accuracy in a brain imaging application. Our model learns the autoregressive structure of individual time series and estimates directed connectivity graphs between the learned representations via a self-attention mechanism in an end-to-end fashion. The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject. We demonstrate our results on a functional neuroimaging dataset classifying schizophrenia patients and controls.
Multi network InfoMax: A pre-training method involving graph convolutional networks

Discovering distinct features and their relations from data can help us uncover valuable knowledge crucial for various tasks, e.g., classification. In neuroimaging, these features could help to understand, classify, and possibly prevent brain disorders. Model introspection of highly performant overparameterized deep learning (DL) models could help find these features and relations. However, to achieve high-performance level DL models require numerous labeled training samples ($n$) rarely available in many fields. This paper presents a pre-training method involving graph convolutional/neural networks (GCNs/GNNs), based on maximizing mutual information between two high-level embeddings of an input sample. Many of the recently proposed pre-training methods pre-train one of many possible networks of an architecture. Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--. The learned high-level graph latent representations help increase performance for downstream graph classification tasks and bypass the need for a high number of labeled data samples. We apply our method to a neuroimaging dataset for classifying subjects into healthy control (HC) and schizophrenia (SZ) groups. Our experiments show that the pre-trained model significantly outperforms the non-pre-trained model and requires $50\%$ less data for similar performance.
$\ell$-Galois special subvarieties and the Mumford-Tate conjecture

We introduce $\ell$-Galois special subvarieties as an $\ell$-adic analog of the Hodge-theoretic notion of a special subvariety. The Mumford-Tate conjecture predicts that both notions are equivalent. We study some properties of these subvarieties and prove this equivalence for subvarieties satisfying a simple monodromy condition. As applications, we show that the $\ell$-Galois exceptional locus is a countable union of algebraic subvarieties and, if the derived group of the generic Mumford-Tate group of a family is simple, its part of positive period dimension coincides with the Hodge locus of positive period dimension. We use this to prove that for $n$ and $d$ sufficiently large, the absolute Mumford-Tate conjecture in degree $n$ holds on a dense open subset of the moduli space of smooth projective hypersurfaces of degree $d$ in $\mathbb{P}^{n+1}$, with the exception of hypersurfaces defined over number fields. Finally, we show that the Mumford-Tate conjecture for abelian varieties is equivalent to a conjecture about the local structure of $\ell$-Galois special subvarieties in $\mathcal{A}_g$.
Cybersecurity Anomaly Detection in Adversarial Environments

The proliferation of interconnected battlefield information-sharing devices, known as the Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the IoBT operating environment is the practice of adversarial machine learning, which attempts to circumvent machine learning models. This work examines the feasibility of cost-effective unsupervised learning and graph-based methods for anomaly detection in the network intrusion detection system setting, and also leverages an ensemble approach to supervised learning of the anomaly detection problem. We incorporate a realistic adversarial training mechanism when training supervised models to enable strong classification performance in adversarial environments. The results indicate that the unsupervised and graph-based methods were outperformed in detecting anomalies (malicious activity) by the supervised stacking ensemble method with two levels. This model consists of three different classifiers in the first level, followed by either a Naive Bayes or Decision Tree classifier for the second level. The model maintains an F1-score above 0.97 for malicious samples across all tested level two classifiers. Notably, Naive Bayes is the fastest level two classifier averaging 1.12 seconds while Decision Tree maintains the highest AUC score of 0.98.
Prothrombotic state in patients with stable COPD: an observational study

Background COPD patients have an increased risk of cardiovascular disease and venous thromboembolism. Methods This study aimed to investigate whether patients with stable COPD have a prothrombotic state compared to COPD-free smokers. We conducted an observational study comparing levels of: D-dimers, INR, aPTT, coagulation factors; fibrinogen, FII, FV, FVII, FVIII, FIX, FX and coagulation inhibitors; protein S, proteins C and antithrombin between stable COPD patients and control subjects. Results A total of 103 COPD patients and 42 controls with similar age, sex, current smoking status, comorbidity burden and cardiovascular risk met the inclusion criteria. Compared to controls, COPD patients had higher levels of D-dimers (median (interquartile range): 360 (230–600) ng·mL−1 versus 240 (180–400) ng·mL−1, p=0.001), fibrinogen (mean±sd: 399±82 mg·dL−1 versus 346±65 mg·dL−1, p<0.001), FII (122±22% versus 109±19%, p=0.004), FV (131±25% versus 121±19%, p=0.015), FVIII (143±32% versus 122±20%, p<0.001) and FX (111 (94–134)% versus 98 (88–107)%, p=0.002), and lower levels of protein S (95 (85–105)% versus 116 (98–121)%, p<0.001) and antithrombin (94.4±11.5% versus 102.3±13.2%, p=0.001). In the COPD group, patients with more severe airflow limitation and frequent exacerbations had significantly higher levels of FII, FV and FX, whereas patients with higher COPD assessment test score had significantly higher levels of FX and lower levels of protein S. Conclusion Patients with stable COPD exhibited increased levels of key coagulation factors and decreased levels of coagulation inhibitors, namely protein S and antithrombin, compared to COPD-free smokers. Among COPD patients, increased levels of FII, FV and FX and decreased levels of protein S were found in patients with more severe disease. Patients with stable COPD exhibit increased levels of key coagulation factors and decreased levels of coagulation inhibitors, namely protein S and antithrombin, compared to COPD-free smokers, indicating a prothrombotic state in stable COPD https://bit.ly/2VmR1PP
Marizomib alone or in combination with bevacizumab in patients with recurrent glioblastoma: Phase I/II clinical trial data

Abstract Background This phase I/II trial in patients with recurrent glioblastoma (GBM) evaluates the safety and preliminary efficacy of marizomib, an irreversible pan-proteasome inhibitor that crosses the blood–brain barrier. Methods Part A assessed the safety and efficacy of marizomib monotherapy. In Part B, escalating doses of marizomib (0.5–0.8 mg/m2) in combination with bevacizumab were evaluated. Part C explored intra-patient dose escalation of marizomib (0.8–1.0 mg/m2) for the combination. Results In Part A, 30 patients received marizomib monotherapy. The most common AEs were fatigue (66.7%), headache (46.7%), hallucination (43.3%), and insomnia (43.3%). One patient (3.3%) achieved a partial response. In Part B, the recommended phase II dose of marizomib was 0.8 mg/m2 when combined with bevacizumab 10 mg/kg. In Part C, dose escalation to 1.0 mg/m2 was not tolerated. Pooled analysis of 67 patients treated with marizomib ≤0.8 mg/m2 and bevacizumab showed a nonoverlapping safety profile consistent with the known safety profile of each agent: the most common grade ≥3 AEs were hypertension (16.4%), confusion (13.4%), headache (10.4%), and fatigue (10.4%). The overall response rate was 34.3%, including 2 patients with complete response. Six-month progression-free survival was 29.8%; median overall survival was 9.1 months. Conclusions The safety profile of marizomib as monotherapy and in combination with bevacizumab was consistent with previous observations that marizomib crosses the blood–brain barrier. Preliminary efficacy did not demonstrate a meaningful benefit of the addition of marizomib to bevacizumab for the treatment of recurrent GBM.
Role of cancer stem cells in head-and-neck squamous cell carcinoma – A systematic review

Targeting cancer stem cell (CSC) subpopulation within the tumor remains an obstacle for specific therapy in head-and-neck squamous cell carcinoma (HNSCC). Few studies in the literature describe a panel of stem cell makers, however a distinct panel has not been put forth. This systematic review aims to enhance the knowledge of additional markers to accurately relate their expression to tumorigenesis, metastasis, and therapy resistance. Databases, including PubMed, Google Scholar, Ebsco, and Science Direct, were searched from 2010 to 2017 using various combinations of the following keywords: “Stem cell markers in HNSCC” and “chemoresistance and radioresistence in HNSCC. ” Original experimental studies (both in vitro and in vivo) published in English considering stem cell markers in HNSCC, were considered and included. We excluded articles on tumors other than HNSCC, reviews, editorial letters, book chapters, opinions, and abstracts from the analyses. Forty-two articles were included, in which 13 types of stem cell markers were identified. The most commonly expressed CSC markers were CD44, aldehyde dehydrogenase, and CD133, which were responsible for tumorigenesis, self-renewal, and therapy resistance, whereas NANOG, SOX-2, and OCT-4 were involved in metastasis and invasion. Identification of an accurate panel of CSC markers is the need of the hour as nonspecificity of the current markers poses a problem. Further studies with a large sample size would help validate the role of these CSC markers in HNSCC. These CSC proteins can be developed as therapeutic targets for HNSCC therapy, making future treatment modality more specific and effective.
Optical investigations of microwave induced synthesis of zinc oxide thin-film

Abstract In this article, ZnO thin-film deposition on a glass substrate was done using microwave induced oxygen plasma based CVD system. The prepared thin-films were tested in terms of crystallinity and optical properties by varying the microwave power. The effect of power variation on the morphology and size of final products was carefully investigated. The crystal structure, chemical composition and morphology of the final products were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-Vis spectroscopy, Raman spectroscopy and photoluminescence (PL). This technique confirmed the presence of hexagonal ZnO nanocrystals in all the thin-films. The minimum crystallite grain size as obtained from the XRD measurements was ~9.7 nm and the average diameter was ~18 nm.
Autopsy findings of suicidal hanging in Mugla, Turkey

Introduction: External and internal autopsy findings of suicidal hanging were evaluated retrospectively in Muğla, Turkey. The relations between macroscopic autopsy findings and age, gender, nature of the ligature, position of the knot, and the ligature marks were assessed. The aim of this study was to describe autopsy findings of the hangings and discuss the regional differences. Method: The reports of 175 hanging autopsies between 2013 to 2018 were evaluated retrospectively. Macroscopic external and internal findings were evaluated and compared statistically. Results: Among the 2534 autopsies, the cause of death was suicide by hanging in 175 cases (6.9%). The male-to-female ratio was 3:1, 76%(n = 133) of the cases were male, 24% (n = 42) were female, the mean age was 44.9(SD = 19.3) years. Suicide by hanging was most prevalent in the 25–44 age group and among men. Complete hanging was more common than incomplete hanging, and softer materials were more commonly used in incomplete hangings. Females utilized soft materials more than males. Males used atypical hanging more than females. Bilateral hemorrhage in the neck muscles was more common with typical hanging. There was a significant relationship between tongue protrusion and hemorrhage at the clavicular head of sternocleidomastoid muscle. Conclusion: In this study, which includes a large series, some regional and gender differences were observed. Careful and detailed macroscopic examination was important to diagnose antemortem vitality findings.