Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?

When diagnosing the brain tumor, doctors usually make a diagnosis by observing multimodal brain images from the axial view, the coronal view and the sagittal view, respectively. And then they make a comprehensive decision to confirm the brain tumor based on the information obtained from multi-views. Inspired by this diagnosing process and in order to further utilize the 3D information hidden in the dataset, this paper proposes a multi-view dynamic fusion framework to improve the performance of brain tumor segmentation. The proposed framework consists of 1) a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view and 2) the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views as an integrate one and two different fusion methods, the voting method and the weighted averaging method, have been adopted to evaluate the fusing process. Moreover, the multi-view fusion loss, which consists of the segmentation loss, the transition loss and the decision loss, is proposed to facilitate the training process of multi-view learning networks so as to keep the consistency of appearance and space, not only in the process of fusing segmentation results, but also in the process of training the learning network. \par By evaluating the proposed framework on BRATS 2015 and BRATS 2018, it can be found that the fusion results from multi-views achieve a better performance than the segmentation result from the single view and the effectiveness of proposed multi-view fusion loss has also been proved. Moreover, the proposed framework achieves a better segmentation performance and a higher efficiency compared to other counterpart methods.
A Non-cooperative Game-based Distributed Beam Scheduling for 5G mm-Wave Networks

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efﬁcient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative game theory. In particular, we ﬁrst transform the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game in which each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via ﬁnding the Nash Equilibrium (NE) of the scheduling game. We prove the existence of NE and provide sufﬁcient conditions guaranteeing the uniqueness of NE by utilizing the equivalence between the non-cooperative game and the Variational Inequality (VI) problem. A corresponding parallel updating algorithm for ﬁnding the NE is proposed which is proved to globally Part of this work
Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python

The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the development of new software tools. Here we introduce an open source Python package named Bambi (BAyesian Model Building Interface) that is built on top of the PyMC probabilistic programming framework and the ArviZ package for exploratory analysis of Bayesian models. Bambi makes it easy to specify complex generalized linear hierarchical models using a formula notation similar to those found in R. We demonstrate Bambi's versatility and ease of use with a few examples spanning a range of common statistical models including multiple regression, logistic regression, and mixed-effects modeling with crossed group specific effects. Additionally we discuss how automatic priors are constructed. Finally, we conclude with a discussion of our plans for the future development of Bambi.
Residual Energy-Based Models for Text

Current large-scale auto-regressive language models (Radford et al., 2019; Liu et al., 2018; Graves, 2013) display impressive ﬂuency and can generate convincing text. In this work we start by asking the question: Can the generations of these models be reliably distinguished from real text by statistical discriminators? We ﬁnd experimentally that the answer is aﬃrmative when we have access to the training data for the model, and guardedly aﬃrmative even if we do not. This suggests that the auto-regressive models can be improved by incorporating the (globally normalized) discriminators into the generative process. We give a formalism for this using the Energy-Based Model framework, and show that it indeed improves the results of the generative models, measured both in terms of perplexity and in terms of human evaluation.
On Relating 'Why?' and 'Why Not?' Explanations

Explanations of Machine Learning (ML) models often address a 'Why? ' question. Such explanations can be related with selecting feature-value pairs which are sufficient for the prediction. Recent work has investigated explanations that address a 'Why Not? ' question, i.e. finding a change of feature values that guarantee a change of prediction. Given their goals, these two forms of explaining predictions of ML models appear to be mostly unrelated. However, this paper demonstrates otherwise, and establishes a rigorous formal relationship between 'Why? ' and 'Why Not? ' explanations. Concretely, the paper proves that, for any given instance, 'Why? ' explanations are minimal hitting sets of 'Why Not? ' explanations and vice-versa. Furthermore, the paper devises novel algorithms for extracting and enumerating both forms of explanations.
The Dirichlet problem for Kolmogorov-Fokker-Planck type equations with rough coefficients

We establish the existence and uniqueness, in bounded as well as unbounded Lipschitz type cylinders of the forms UX ×VY,t and Ω×R m ×R, of weak solutions to CauchyDirichlet problems for the strongly degenerate parabolic operator L := ∇X · (A(X,Y, t)∇X) +X · ∇Y − ∂t, assuming that A = A(X,Y, t) = {ai,j(X,Y, t)} is a real m × m-matrix valued, measurable function such that A(X,Y, t) is symmetric, bounded and uniformly elliptic. Subsequently we solve the continuous Dirichlet problem and establish the representation of the solution using associated parabolic measures. The paper is motivated, through our recent studies [12, 13, 14], by a growing need and interest to gain a deeper understanding of the Dirichlet problem for the operator L in Lipschitz type domains. The key idea underlying our results is to prove, along the lines of Brezis and Ekeland [4, 5], and in particular following the recent work of S. Armstrong and J-C. Mourrat [3] concerning variational methods for the kinetic Fokker-Planck equation, that the solution can be obtained as the minimizer of a uniformly convex functional. 2000 Mathematics Subject Classification. 35K65, 35K70, 35H20, 35R03.
Structure and composition of C-S-H compounds up to 143 GPa

We synthesized two C-S-H compounds from a mixture of carbon and sulfur in hydrogen and from sulfur in mixed methane-hydrogen fluids at 4 GPa. X-ray synchrotron single-crystal diffraction and Raman spectroscopy have been applied to these samples up to 58 and 143 GPa, respectively. Both samples show a similar Al2Cu type I4/mcm basic symmetry, while the hydrogen subsystem evolves with pressure via variously ordered molecular and extended modifications. The methane bearing sample lowers symmetry to an orthorhombic Pnma structure after laser heating to 1400 K at 143 GPa. The results suggest that superconducting C-S-H compounds are structurally different from a common Im-3m H3S.
Privacy Analysis and Evaluation Policy of Blockchain-based Anonymous Cryptocurrencies

In blockchain-based anonymous cryptocurrencies, due to their tamper-resistance and transparency characteristics, transaction data are initially required to be anonymous, with the help of various cryptographic techniques, e.g., commitment schemes and zero-knowledge proofs. Also, cryptocurrencies are different from existing anonymous messaging protocols regarding the software architecture and the underlying security model. Due to these differences, the sense of anonymity must be specifically defined for anonymous cryptocurrencies, and the anonymity in each anonymous cryptocurrency must be analyzed and evaluated based on the specific architecture model. In this paper, we first propose a specific architecture model with three software layers to anonymous cryptocurrencies. Next, we introduce definitions of fundamental privacy properties (Pfitzmann's anonymity, unlinkability, and pseudonymity) and comprehensively analyze each privacy property for each architecture layer of anonymous cryptocurrencies to establish a privacy evaluation policy for anonymous cryptocurrencies. Finally, we fairly compare the privacy of current leading anonymous cryptocurrencies (e.g., Zerocash, CryptoNote, and Mimblewimble) using the privacy evaluation policy.
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA

One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pretraining and supervised training data with transformer-based models. Second, explicit, symbolic knowledge encoded in knowledge bases. Our approach combines both—exploiting the powerful implicit reasoning of transformer models for answer prediction, and integrating symbolic representations from a knowledge graph, while never losing their explicit semantics to an implicit embedding. We combine diverse sources of knowledge to cover the wide variety of knowledge needed to solve knowledge-based questions. We show our approach, KRISP (Knowledge Reasoning with Implicit and Symbolic rePresentations), significantly out-performs state-of-the-art on OK-VQA, the largest available dataset for open-domain knowledge-based VQA. We show with extensive ablations that while our model successfully exploits implicit knowledge reasoning, the symbolic answer module which explicitly connects the knowledge graph to the answer vocabulary is critical to the performance of our method and generalizes to rare answers.1
A Bayesian State-Space Approach to Mapping Directional Brain Networks

Abstract The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient’s intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We build a state-space multivariate autoregression (SSMAR) for iEEG data to model the underlying directional brain network. To produce scientifically interpretable network results, we incorporate into the SSMAR the scientific knowledge that the underlying brain network tends to have a cluster structure. Specifically, we assign to the SSMAR parameters a stochastic-blockmodel-motivated prior, which reflects the cluster structure. We develop a Bayesian framework to estimate the SSMAR, infer directional connections, and identify clusters for the unobserved network edges. The new method is robust to violations of model assumptions and outperforms existing network methods. By applying the new method to an epileptic patient’s iEEG data, we reveal seizure initiation and propagation in the patient’s directional brain network and discover a unique directional connectivity property of the SOZ. Overall, the network results obtained in this study bring new insights into epileptic patients’ normal and abnormal epileptic brain mechanisms and have the potential to assist neurologists and clinicians in localizing the SOZ—a long-standing research focus in epilepsy diagnosis and treatment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
The role of time estimation in decreased impatience in intertemporal choice.

The role of specific cognitive processes in deviations from constant discounting in intertemporal choice is not well understood. We evaluated decreased impatience in intertemporal choice tasks independent of discounting rate and non-linearity in long-scale time representation; nonlinear time representation was expected to explain inconsistencies in discounting rate. Participants performed temporal magnitude estimation and intertemporal choice tasks. Psychophysical functions for time intervals were estimated by fitting linear and power functions, while discounting functions were estimated by fitting exponential and hyperbolic functions. The temporal magnitude estimates of 65% of the participants were better fit with power functions (mostly compression). 63% of the participants had intertemporal choice patterns corresponding best to hyperbolic functions. Even when the perceptual bias in the temporal magnitude estimations was compensated in the discounting rate computation, the data of 8 out of 14 participants continued exhibiting temporal inconsistency. The results suggest that temporal inconsistency in discounting rate can be explained to different degrees by the bias in temporal representations. Non-linearity in temporal representation and discounting rate should be evaluated on an individual basis. Keywords: Intertemporal choice, temporal magnitude, model comparison, impatience, time inconsistency
RG flows with global symmetry breaking and bounds from chaos

We discuss general aspects of renormalization group (RG) flows between two conformal fixed points in 4d with a broken continuous global symmetry in the UV. Every such RG flow can be described in terms of the dynamics of Nambu-Goldstone bosons of broken conformal and global symmetries. We derive the low-energy effective action that describes this class of RG flows from basic symmetry principles. We view the theory of Nambu-Goldstone bosons as a theory in anti-de Sitter space with the flat space limit. This enables an equivalent CFT3 formulation of these 4d RG flows in terms of spectral deformations of a generalized free CFT3. We utilize this dual description to impose further constraints on the low energy effective action associated with unitary RG flows in 4d by invoking the chaos bound in 3d. This approach naturally provides a set of independent monotonically decreasing C-functions for 4d RG flows with global symmetry breaking by explicitly relating 4d C-functions with certain out-of-time-order correlators that diagnose chaos in 3d. We also comment on a more general connection between RG and chaos in QFT. ar X iv :2 01 2. 10 45 0v 2 [ he pth ] 3 D ec 2 02 1
3D Object Detection with Pointformer

Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interactions among points in a local region, which learns context-dependent region features at an object level. A Global Transformer is designed to learn context-aware representations at the scene level. To further capture the dependencies among multi-scale representations, we propose Local-Global Transformer to integrate local features with global features from higher resolution. In addition, we introduce an efficient coordinate refinement module to shift down-sampled points closer to object centroids, which improves object proposal generation. We use Pointformer as the backbone for state-of-the-art object detection models and demonstrate significant improvements over original models on both indoor and outdoor datasets.
Searches for compact binary coalescence events using neural networks in the LIGO/Virgo second observation period

We present results on the search for the coalescence of compact binary mergers using convolutional neural networks and the LIGO/Virgo data, corresponding to the O2 observation period. Two-dimensional images in time and frequency are used as input, and two sets of neural networks are trained separately for low mass (0.2 - 2.0 Msun) and high mass (25 - 100 Msun) compact binary coalescence events. We explored neural networks trained with input information from a single or a pair of interferometers, indicating that the use of information from pairs leads to an improved performance. A scan over the full O2 data set using the convolutional neural networks for detection demonstrates that the performance is compatible with that from canonical pipelines using matched filtering techniques. No additional events with significant signal-to-noise ratio are found in the O2 data.
Unsupervised Cross-Lingual Speech Emotion Recognition Using Domain Adversarial Neural Network

By using deep learning approaches, Speech Emotion Recognition (SER) on a single domain has achieved many excellent results. However, cross-domain SER is still a challenging task due to the distribution shift between source and target domains. In this work, we propose a Domain Adversarial Neural Network (DANN) based approach to mitigate this distribution shift problem for cross-lingual SER. Specifically, we add a language classifier and gradient reversal layer after the feature extractor to force the learned representation both language-independent and emotion-meaningful. Our method is unsupervised, i. e. , labels on target language are not required, which makes it easier to apply our method to other languages. Experimental results show the proposed method provides an average absolute improvement of 3.91% over the baseline system for arousal and valence classification task. Furthermore, we find that batch normalization is beneficial to the performance gain of DANN. Therefore we also explore the effect of different ways of data combination for batch normalization.
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach

The increased reliance on the Internet and the corresponding surge in connectivity demand has led to a significant growth in Internet-of-Things (IoT) devices. The continued deployment of IoT devices has in turn led to an increase in network attacks due to the larger number of potential attack surfaces as illustrated by the recent reports that IoT malware attacks increased by 215.7% from 10.3 million in 2017 to 32.7 million in 2018. This illustrates the increased vulnerability and susceptibility of IoT devices and networks. Therefore, there is a need for proper effective and efficient attack detection and mitigation techniques in such environments. Machine learning (ML) has emerged as one potential solution due to the abundance of data generated and available for IoT devices and networks. Hence, they have significant potential to be adopted for intrusion detection for IoT environments. To that end, this paper proposes an optimized ML-based framework consisting of a combination of Bayesian optimization Gaussian Process (BO-GP) algorithm and decision tree (DT) classification model to detect attacks on IoT devices in an effective and efficient manner. The performance of the proposed framework is evaluated using the Bot-IoT-2018 dataset. Experimental results show that the proposed optimized framework has a high detection accuracy, precision, recall, and F-score, highlighting its effectiveness and robustness for the detection of botnet attacks in IoT environments.
Adsorptive Removal of Phenol from Aqueous Solution Using Activated Carbon Prepared from Babul Sawdust

AbstractStudy on the adsorption of phenol onto activated carbon prepared from Babul (Acacia nilotica) sawdust (BAC), a waste from sawmill, is presented in this paper. The effect of various parameters such as adsorbent dose (m), initial pH (pH0), contact time (t), initial concentration (Co), and temperature (T) on adsorption capacity of BAC was tested using batch adsorption experiments. The kinetic study was performed by using different kinetic models, viz pseudo first, pseudo second order, and intraparticle diffusion. Langmuir, Freundlich, Redlich-Peterson, and Temkin isotherm equations were used to find the suitable and well-describing experimental isotherm data. The adsorption of phenol onto BAC follows the pseudo second order kinetic model, and equilibrium adsorption data follow the Redlich-Peterson and Freundlich isotherm equation. The maximum removal of phenol was found to be about 83% for the initial concentration of 50 mg/L and about 50% for the higher initial concentration of 500 mg/L at 10 kg/.
An X-Ray Spectral Study of the Origin of Reflection Features in Bare Seyfert 1 Galaxy ESO 511–G030

The reprocessed X-ray emission from active galactic nuclei is an important diagnostic tool to study the dynamics and geometry of the matter surrounding supermassive black holes (SMBHs). We present a broadband (optical-UV to hard X-ray) spectral study of the bare Seyfert 1 galaxy, ESO 511–G030, using multi-epoch Suzaku and XMM-Newton data from 2012 and 2007, respectively. The broadband spectra of ESO 511–G030 exhibit a UV bump, a prominent soft excess below , a relatively broad ( ) Fe emission line at , and a weak Compton hump at . The soft X-ray excess in ESO 511–G030 can be described either as the thermal Comptonization of disk seed photons by a warm ( ), optically thick ( ), and compact ( ) corona or as the blurred reflection from an untruncated and moderate to highly ionized accretion disk. However, for the blurred reflection, the model requires some extreme configuration of the disk and corona. Both these models prefer a rapidly spinning black hole ( ) and a compact corona, indicating a relativistic origin of the broad Fe emission line. We found an inner disk temperature of that characterizes the UV bump and the SMBH accretes at a sub-Eddington rate ( ).