Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Future Potential of an Upgraded ALMA to Image Planet-forming Disks at Sub-astronomical-unit Scales

In recent years, ALMA has been able to observe large-scale substructures within protoplanetary disks. Comparison with the predictions from models of planet–disk interactions has indicated that most of these disk substructures can be explained by the presence of planets with the mass of Neptune or larger at orbital radii of ≈5–100 au. Better resolution is needed to observe structures closer to the star, where terrestrial planets are expected to form, as well as structures opened by planets with masses lower than Neptune. We investigate the capabilities of a possible extension to ALMA that would double the longest baseline lengths in the array to detect and resolve disk substructures opened by Earth-mass and super-Earth planets at orbital radii of 1–5 au. By simulating observations of a family of disk models using this extended configuration in ALMA Bands 6 and 7, we show that an upgraded ALMA would detect gaps in disks formed by super-Earths as close as 1 au, as well as Earth-mass planets down to 2–3 au from the young host stars in nearby star-forming regions.
Quantum Neuron with Separable-State Encoding

The use of advanced quantum neuron models for pattern recognition applications requires fault tolerance. Therefore, it is not yet possible to test such models on a large scale in currently available quantum processors. As an alternative, we propose a quantum perceptron (QP) model that uses a reduced number of multi-qubit gates and is therefore less susceptible to quantum errors in current actual quantum computers with limited tolerance. The proposed quantum algorithm is superior to its classical counterpart, although since it does not take full advantage of quantum entanglement, it provides a lower encoding power than other quantum algorithms using multiple qubit entanglement. However, the use of separable-sate encoding allows for testing the algorithm and different training schemes at a large scale in currently available non-fault tolerant quantum computers. We demonstrate the performance of the proposed model by implementing a few qubits version of the QP in a simulated quantum computer. The proposed QP uses an N-ary encoding of the binary input data characterizing the patterns. We develop a hybrid (quantum-classical) training procedure for simulating the learning process of the QP and test their efficiency.
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem

Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due to exponential worst-case complexity. Inspired by the recent success of contrastive pre-training for images, we conduct a scientiﬁc study of the eﬀect of augmentation design on contrastive pre-training for the Boolean satisﬁability problem. While typical graph contrastive pre-training uses label-agnostic augmentations, our key insight is that many combinatorial problems have well-studied invariances, which allow for the design of label-preserving augmentations . We ﬁnd that label-preserving augmentations are critical for the success of contrastive pre-training. We show that our representations are able to achieve comparable test accuracy to fully-supervised learning while using only 1% of the labels. We also demonstrate that our representations are more transferable to larger problems from unseen domains.
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

We present CortexODE, a deep learning framework for cortical surface reconstruction. CortexODE leverages neural ordinary differential equations (ODEs) to deform an input surface into a target shape by learning a diffeomorphic flow. The trajectories of the points on the surface are modeled as ODEs, where the derivatives of their coordinates are parameterized via a learnable Lipschitz-continuous deformation network. This provides theoretical guarantees for the prevention of self-intersections. CortexODE can be integrated to an automatic learning-based pipeline, which reconstructs cortical surfaces efficiently in less than 5 seconds. The pipeline utilizes a 3D U-Net to predict a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans, and further generates a signed distance function that represents an initial surface. Fast topology correction is introduced to guarantee homeomorphism to a sphere. Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively. The proposed pipeline is evaluated on large-scale neuroimage datasets in various age groups including neonates (25-45 weeks), young adults (22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate that the CortexODE-based pipeline can achieve less than 0.2mm average geometric error while being orders of magnitude faster compared to conventional processing pipelines.
Multi-Channel Speech Denoising for Machine Ears

This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments. We recorded approximately 100 hours of audio data with reverberation and moderate environmental noise using a pair of microphone arrays placed around each of the two ears and then mixed sound recordings to simulate adverse acoustic scenes. Then, we trained a multi-channel speech denoising network (MCSDN) on the mixture of recordings. To improve the training, we employ an unsupervised method, complex angular central Gaussian mixture model (cACGMM), to acquire cleaner speech from noisy recordings to serve as the learning target. We propose a MCSDN-Beamforming-MCSDN framework in the inference stage. The results of the subjective evaluation show that the cACGMM improves the training data, resulting in better noise reduction and user preference, and the entire system improves the intelligibility and listening experience in noisy situations.
A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models

With the increasing of model capacity brought by pre-trained language models, there emerges boosting needs for more knowledgeable natural language processing (NLP) models with advanced functionalities including providing and making flexible use of encyclopedic and commonsense knowledge. The mere pre-trained language models, however, lack the capacity of handling such knowledge-intensive NLP tasks alone. To address this challenge, large numbers of pre-trained language models augmented with external knowledge sources are proposed and in rapid development. In this paper, we aim to summarize the current progress of pre-trained language model-based knowledge-enhanced models (PLMKEs) by dissecting their three vital elements: knowledge sources, knowledge-intensive NLP tasks, and knowledge fusion methods. Finally, we present the challenges of PLMKEs based on the discussion regarding the three elements and attempt to provide NLP practitioners with potential directions for further research.
Term Rewriting Based On Set Automaton Matching

In this article we investigate how a subterm pattern matching algorithm can be exploited to implement efficient term rewriting procedures. From the left-hand sides of the rewrite system we construct a set automaton, which can be used to find all redexes in a term efficiently. We formally describe a procedure that, given a rewrite strategy, interleaves pattern matching steps and rewriting steps and thus smoothly integrates redex discovery and subterm replacement. We then present an efficient implementation that instantiates this procedure with outermost rewriting, and present the results of some experiments. Our implementation shows to be competitive with comparable tools.
Open-Ended Reinforcement Learning with Neural Reward Functions

Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We propose a different approach that uses reward functions encoded by neural networks. These are trained iteratively to reward more complex behavior. In high-dimensional robotic environments our approach learns a wide range of interesting skills including front-flips for Half-Cheetah and one-legged running for Humanoid. In the pixel-based Montezuma's Revenge environment our method also works with minimal changes and it learns complex skills that involve interacting with items and visiting diverse locations. The implementation of our approach can be found in this link: https://github.com/amujika/Open-Ended-Reinforcement-Learning-with-Neural-Reward-Functions.
End-To-End Music Remastering System Using Self-Supervised And Adversarial Training

Mastering is an essential step in music production, but it is also a challenging task that has to go through the hands of experienced audio engineers, where they adjust tone, space, and volume of a song. Remastering follows the same technical process, in which the context lies in mastering a song for the times. As these tasks have high entry barriers, we aim to lower the barriers by proposing an end-to-end music remastering system that transforms the mastering style of input audio to that of the target. The system is trained in a self-supervised manner, in which released pop songs were used for training. We also anticipated the model to generate realistic audio reflecting the reference’s mastering style by applying a pre-trained encoder and a projection discriminator. We validate our results with quantitative metrics and a subjective listening test and show that the model generated samples of mastering style similar to the target.
The porous medium equation with large data on Cartan-Hadamard manifolds under general curvature bounds

<p style='text-indent:20px;'>We consider very weak solutions of the Cauchy problem for the porous medium equation on Cartan-Hadamard manifolds, that are assumed to satisfy general curvature bounds and to be stochastically complete. We identify a class of initial data that can grow at infinity at a prescribed rate, which depends on the assumed curvature bounds through an integral function, such that the corresponding solution exists at least on <inline-formula><tex-math id="M1">\begin{document}$ [0,T] $\end{document}</tex-math></inline-formula> for a suitable <inline-formula><tex-math id="M2">\begin{document}$ T>0 $\end{document}</tex-math></inline-formula>. The maximal existence time <inline-formula><tex-math id="M3">\begin{document}$ T $\end{document}</tex-math></inline-formula> is estimated in terms of a suitable weighted norm of the initial datum. Our results are sharp, in the sense that slower growth rates yield global existence, whereas one can construct data with critical growth for which the corresponding solutions blow up in finite time. Under further assumptions, uniqueness of very weak solutions is also proved, in the same growth class. </p>
Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge

Internet memes have become a dominant method of communication; at the same time, however, they are also increasingly being used to advocate extremism and foster derogatory beliefs. Nonetheless, we do not have a firm understanding as to which perceptual aspects of memes cause this phenomenon. In this work, we assess the efficacy of current state-of-the-art multimodal machine learning models toward hateful meme detection, and in particular with respect to their generalizability across platforms. We use two benchmark datasets comprising 12,140 and 10,567 images from 4chan's"Politically Incorrect"board (/pol/) and Facebook's Hateful Memes Challenge dataset to train the competition's top-ranking machine learning models for the discovery of the most prominent features that distinguish viral hateful memes from benign ones. We conduct three experiments to determine the importance of multimodality on classification performance, the influential capacity of fringe Web communities on mainstream social platforms and vice versa, and the models' learning transferability on 4chan memes. Our experiments show that memes' image characteristics provide a greater wealth of information than its textual content. We also find that current systems developed for online detection of hate speech in memes necessitate further concentration on its visual elements to improve their interpretation of underlying cultural connotations, implying that multimodal models fail to adequately grasp the intricacies of hate speech in memes and generalize across social media platforms.
Residual-based error estimation and adaptivity for stabilized immersed isogeometric analysis using truncated hierarchical B-splines

We propose an adaptive mesh refinement strategy for immersed isogeometric analysis, with application to steady heat conduction and viscous flow problems. The proposed strategy is based on residual-based error estimation, which has been tailored to the immersed setting by the incorporation of appropriately scaled stabilization and boundary terms. Element-wise error indicators are elaborated for the Laplace and Stokes problems, and a THB-spline-based local mesh refinement strategy is proposed. The error estimation . and adaptivity procedure is applied to a series of benchmark problems, demonstrating the suitability of the technique for a range of smooth and non-smooth problems. The adaptivity strategy is also integrated in a scan-based analysis workflow, capable of generating reliable, error-controlled, results from scan data, without the need for extensive user interactions or interventions.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis

We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a general FL-HPO solution framework that can address use cases of tabular data and any Machine Learning (ML) model including gradient boosting training algorithms and therefore further expands the scope of FL-HPO. FLoRA enables single-shot FL-HPO: identifying a single set of good hyper-parameters that are subsequently used in a single FL training. Thus, it enables FL-HPO solutions with minimal additional communication overhead compared to FL training without HPO. We theoretically characterize the optimality gap of FL-HPO, which explicitly accounts for the heterogeneous non-IID nature of the parties' local data distributions, a dominant characteristic of FL systems. Our empirical evaluation of FLoRA for multiple ML algorithms on seven OpenML datasets demonstrates significant model accuracy improvements over the considered baseline, and robustness to increasing number of parties involved in FL-HPO training.
Mechanical Properties of Acoustically Levitated Granular Rafts

We investigate a model system for the rotational dynamics of inertial many-particle clustering, in which sub-millimeter objects are acoustically levitated in air. Driven by scattered sound, levitated grains self-assemble into a monolayer of particles, forming mesoscopic granular rafts with both an acoustic binding energy and a bending rigidity. Detuning the acoustic trap can give rise to stochastic forces and torques that impart angular momentum to levitated objects. As the angular momentum of a quasi-two-dimensional granular raft is increased, the raft deforms from a disk to an ellipse, eventually pinching off into multiple separate rafts, in a mechanism that resembles the break-up of a liquid drop. We extract the raft effective surface tension and elastic modulus, and show that non-pairwise acoustic forces give rise to effective elastic moduli that scale with the raft size. We also show that the raft size controls the microstructural basis of plastic deformation, resulting in a transition from fracture to ductile failure.
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning

Many goal-reaching reinforcement learning (RL) tasks have empirically verified that rewarding the agent on subgoals improves convergence speed and practical performance. We attempt to provide a theoretical framework to quantify the computational benefits of rewarding the completion of subgoals, in terms of the number of synchronous value iterations. In particular, we consider subgoals as one-way intermediate states, which can only be visited once per episode and propose two settings that consider these one-way intermediate states: the one-way single-path (OWSP) and the one-way multi-path (OWMP) settings. In both OWSP and OWMP settings, we demonstrate that adding intermediate rewards to subgoals is more computationally efficient than only rewarding the agent once it completes the goal of reaching a terminal state. We also reveal a trade-off between computational complexity and the pursuit of the shortest path in the OWMP setting: adding intermediate rewards significantly reduces the computational complexity of reaching the goal but the agent may not find the shortest path, whereas with sparse terminal rewards, the agent finds the shortest path at a significantly higher computational cost. We also corroborate our theoretical results with extensive experiments on the MiniGrid environments using Q-learning and some popular deep RL algorithms.
Profesjonsetikk for lærere

Dette er et kvalitativt studie som tar for seg temaet profesjonsetikk for laerere. Jeg har i oppgaven analysert Laererprofesjonens etiske plattform for a undersoke hvordan dokumentets innhold kan forstas og etterleves blant laerere. Dataene fra analysen har jeg droftet opp imot teori som baserer seg pa profesjonsstudier med en sosiologisk tilnaerming. Noen sentrale forskere jeg stotter meg pa er Harald Grimen og Svein Aage Christoffersen, men jeg har ogsa brukt annen litteratur hvor det har vaert hensiktsmessig. I analysen kommer det frem at Laererprofesjonens etiske plattform inneholder bade verdier og plikter som laerere er forpliktet overfor og at dokumentets innhold kan forstas og etterleves pa ulike mater.
CMU’s Machine Translation System for IWSLT 2019

In Neural Machine Translation (NMT) the usage of sub-words and characters as source and target units offers a simple and flexible solution for translation of rare and unseen words. However, selecting the optimal subword segmentation involves a trade-off between expressiveness and flexibility, and is language and dataset-dependent. We present Block Multitask Learning (BMTL), a novel NMT architecture that predicts multiple targets of different granularities simulta- neously, removing the need to search for the optimal seg- mentation strategy. Our multi-task model exhibits improvements of up to 1.7 BLEU points on each decoder over single-task baseline models with the same number of parameters on datasets from two language pairs of IWSLT15 and one from IWSLT19. The multiple hypotheses generated at different granularities can also be combined as a post-processing step to give better translations.
End-to-end Speech Translation System Description of LIT for IWSLT 2019

This paper describes our end-to-end speech translation system for the speech translation task of lectures and TED talks from English to German for IWSLT Evaluation 2019. We propose layer-tied self-attention for end-to-end speech translation. Our method takes advantage of sharing weights of speech encoder and text decoder. The representation of source speech and the representation of target text are coordinated layer by layer, so that the speech and text can learn a better alignment during the training procedure. We also adopt data augmentation to enhance the parallel speech-text corpus. The En-De experimental results show that our best model achieves 17.68 on tst2015. Our ASR achieves WER of 6.6% on TED-LIUM test set. The En-Pt model can achieve about 11.83 on the MuST-C dev set.
VoIP: Wireless, P2P and New Enterprise Voice over IP

Understand how new network technologies impact VoIP! Voice over Internet Protocol (VoIP) is revolutionizing the way people communicate both in the corporate world and in personal life. The enormous success of VoIP has led to its adoption in a wide range of networking technologies. Each network technology has its unique features and poses distinct challenges for the performance of VoIP. VoIP: Wireless, P2P and New Enterprise Voice over IPdescribes the issues arising in the deployment of VoIP in an emerging heterogeneous network environment. Along with a brief overview of the concepts, protocols, algorithms, and equipment involved in realizing VoIP, this book focuses on two areas: quality and performance issues in deploying VoIP over various network settings, and the new mechanisms and protocols in these emerging networks to assist the deployment of VoIP. VoIP: Wireless, P2P and New Enterprise Voice over IP: Discusses the basics of VoIP, VoIP codecs and VoIP Protocols including SIP and H.323. Details new technologies such as P2P technology, VoWiFi, WiMax, and 3G Networks. Explains the QoS issues arising from deploying VoIP using the new technologies. Solves the performance issues that arise when VoIP is deployed over different network technologies. This book is an invaluable resource for professional network engineers, designers, managers, researchers, decision makers and project managers overseeing VoIP implementations. Market analysts, consultants, and those studying advanced undergraduate and graduate courses on data, voice and multimedia communications will also find this book insightful.
Edge Detection Techniques: A Survey

An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. Edge detection process significantly reduces the amount of data and filters out useless information, while preserving the essential structural properties in an image. Since computer vision involves the recognition and classification of objects in an image, edge detections is a vital tool. In this paper, the main aim is to study edge detection process based on different techniques.
Expensive and Cheap Wine Words Revisited

Abstract Previous work has quantitatively analyzed expert wine descriptions to identify some so-called “expensive” and “cheap” words that are indicative of a wine's price. This paper revisits that work. In particular, I examine whether words previously identified as expensive and cheap ones are still indicative of a wine's price when using the same methods and a different, larger dataset. My findings mostly confirm previous conclusions, although many directions for further research into expensive and cheap wine words remain open. (JEL Classifications: D83, L66)