Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrable quantum many-body scars in a non-integrable large-N spin chain

Exact quantum many-body scar states in non-integrable models that have been found in recent years all share the common property of being lowly entangled, therefore representable by matrix product states. Here, we present a novel type of partly integrable one-dimensional multicomponent model that can be viewed as a minimal generalization to the spin- 12 XXZ model with reduced frustration. In certain subspaces of the Hilbert space, exact eigenstates are Bethe Ansatz integrable, embedding the spectra of spin- 12 XXZ Hamiltonians. For the rest of Hilbert space, it’s non-integrable in the sense that its scattering matrices violate Yang-Baxter’s equation. This model in turn can be viewed as a low-energy eﬀective Hamiltonian for the “coprime quantum spin ladder” model [1]. We show the entanglement entropy of its ground state scales linearly with the size of the subchain with suﬃciently large number of local degrees of freedom, and argue that the scar states have entanglement entropy scalings similar to those established for Bethe Ansatz integrable spin- 12 chains.
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System

Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.
Sequential Linear Discriminant Analysis in High Dimensions Using Individual Discriminant Functions

High dimensional classification has been highlighted for last two decades and much research has been conducted in order to circumvent challenges encountered in high dimensions. While existing methods have focused mainly on developing classification rules assuming independence of covariates or using regularization on the sample covariance matrix or the sample mean vector or among others, we propose a novel approach that employs the"discriminatory power"of each covariate, selects a set of important variables yielding the lowest misclassification rate empirically, and constructs the optimal linear classifier with selected variables. We carry out simulation studies and analyze real data sets to illustrate the performance of our proposed classifier by comparing it with existing classifiers.
ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence

The Gumbel-softmax distribution, or Concrete distribution, is often used to relax the discrete charac-teristics of a categorical distribution and enable back-propagation through differentiable reparameterization. Although it reliably yields low variance gradients, it still relies on a stochastic sampling process for optimization. In this work, we present a relaxed categorical analytic bound (ReCAB), a novel divergence-like metric which corresponds to the upper bound of the Kullback-Leibler divergence (KLD) of a relaxed categorical distribution. The proposed metric is easy to implement because it has a closed form solution, and empirical results show that it is close to the actual KLD. Along with this new metric, we propose a relaxed categorical analytic bound variational autoencoder (ReCAB-VAE) that successfully models both continuous and relaxed discrete latent representations. We implement an emotional text-to-speech synthesis system based on the proposed framework, and show that the proposed system ﬂexibly and stably controls emotion expressions with better speech quality compared to baselines that use stochastic estimation or categorical distribution approximation.
A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency, and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3-D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This article investigates a wide range of model-free and model-based algorithms for viewpoints and path planning for 3-D reconstruction of large-scale objects. It presents a bibliography of more than 200 references to cover different aspects of the topic. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3-D reconstruction purposes. In addition to discussing the evaluation strategies, this article also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.
Null controllability of the linear Stabilized Kuramoto-Sivashinsky system using moment method

This paper deals with the null controllability of a coupled parabolic system, which is Kuramoto-Sivashinsky-Korteweg-de Vries equation coupled with heat equation through first order derivative. More precisely, we prove the null controllability of the system with a single localized bilinear interior control acting on either of the components of the coupled system, and with a single periodic boundary control acting through zeroth order derivatives of either of the components. We employ the well-known moment method to study the controllability of the concerned system.
Learning Self-adaptations for IoT Networks: A Genetic Programming Approach

Internet of Things (IoT) is a pivotal technology in application domains that require connectivity and interoperability between large numbers of devices. IoT systems predominantly use a software-defined network (SDN) architecture as their core communication backbone. This architecture offers several advantages, including the flexibility to make IoT networks self-adaptive through software programmability. In general, self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this paper, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the logic / code of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. We instantiate and empirically assess this idea in the context of IoT networks. Specifically, using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of SDN-based IoT networks. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.
Interstellar planetesimals

During the formation of our solar system, a large number of planetesimals were ejected into interstellar space by gravitational encounters with the planets. Debris disks observations and numerical simulations indicate that many other planetary systems, now known to be quite common, would have undergone a similar dynamical clearing process. It is therefore expected that the galaxy should be teeming with expelled planetesimals, largely unaltered since their ejection. This is why astronomers were perplexed that none had been detected passing through the solar system. Then, in 2017, the discovery of1I/’Oumuamua transformed the situation from puzzlement to bewilderment. Its brief visit and limited observations left important questions about its nature and origin unanswered and raised the possibility that 1I/’Oumuamua could be a never-seen-before intermediate product of planet formation. If so, this could open a new observational window to study the primordial building blocks of planets, setting unprecedented constraints on planet formation models. Two years later 2I/Borisov was discovered, with an unquestionable cometary composition, conﬁrming that a population of icy interstellar planetesimals exists. These objects have remained largely unchanged since their ejection, like time capsules of their planetary system most distant past. Interstellar planetesimals could potentially be trapped into star and planet formation environments, acting as seeds for planet formation, helping overcome the meter-size barrier that challenges the growth of cm-sized pebbles into km-sized objects. Interstellar planetesimals play a pivotal role in our understanding of planetary system formation and evolution and point to the possibility that one day, we will be able to hold a fragment from another world in our hand. of massive bombardment and planetesimals clearing in the solar system may have overlapped with the period that the star cluster was still bound. from the dynamical point of view, a window of opportunity existed for the transfer of material between the solar system and other planetary systems in the cluster. At the time, there is evidence that liquid water could have been present near the Earth’s surface. This transfer of solid material is of interest in the context of lithopanspermia if life had an early start in the solar system or in other planetary systems in its neighborhood. Belbruno et al.
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models

Conditional image generation has paved the way for several breakthroughs in image editing, generating stock pho-tos and 3-D object generation. This continues to be a signiﬁcant area of interest with the rise of new state-of-the-art methods that are based on diffusion models. However, diffusion models provide very little control over the generated image, which led to subsequent works exploring techniques like classiﬁer guidance, that provides a way to trade off di-versity with ﬁdelity. In this work, we explore techniques to condition diffusion models with carefully crafted input noise artifacts. This allows generation of images conditioned on semantic attributes. This is different from existing approaches that input Gaussian noise and further introduce conditioning at the diffusion model’s inference step. Our experiments over several examples and conditional settings show the potential of our approach.
CORWA: A Citation-Oriented Related Work Annotation Dataset

Academic research is an exploratory activity to discover new solutions to problems. By this nature, academic research works perform literature reviews to distinguish their novelties from prior work. In natural language processing, this literature review is usually conducted under the “Related Work” section. The task of related work generation aims to automatically generate the related work section given the rest of the research paper and a list of papers to cite. Prior work on this task has focused on the sentence as the basic unit of generation, neglecting the fact that related work sections consist of variable length text fragments derived from different information sources. As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources. We train a strong baseline model that automatically tags the CORWA labels on massive unlabeled related work section texts. We further suggest a novel framework for human-in-the-loop, iterative, abstractive related work generation.
Graph Neural Networks for Propositional Model Counting

Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are still mostly approached with traditional solvers. Here we tackle this gap by presenting an architecture based on the GNN framework for belief propagation (BP) of Kuch et al., extended with self-attentive GNN and trained to approximately solve the #SAT problem. We ran a thorough experimental investigation, showing that our model, trained on a small set of random Boolean formulae, is able to scale effectively to much larger problem sizes, with comparable or better performances of state of the art approximate solvers. Moreover, we show that it can be efficiently fine-tuned to provide good generalization results on different formulae distributions, such as those coming from SAT-encoded combinatorial problems.
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction

Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance. We propose the GRAPHCACHE (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences. The global property features act as dataset-level prior knowledge for RE, and a complement to the sentence-level features. Inspired by the classical caching technique in computer systems, we develop GRAPHCACHE to update the property representations in an online manner. Overall, GRAPHCACHE yields significant effectiveness gains on RE and enables efficient message passing across all sentences in the dataset.
XSTEM: An exemplar-based stemming algorithm

Stemming is the process of reducing related words to a standard form by removing affixes from them. Existing algorithms vary with respect to their complexity, configurability, handling of unknown words, and ability to avoid under- and over-stemming. This paper presents a fast, simple, configurable, high-precision, high-recall stemming algorithm that combines the simplicity and performance of word-based lookup tables with the strong generalizability of rule-based methods to avert problems with out-of-vocabulary words.
Attract me to Buy: Advertisement Copywriting Generation with Multimodal Multi-structured Information

Recently, online shopping has gradually become a common way of shopping for people all over the world. Wonderful merchandise advertisements often attract more people to buy. These advertisements properly integrate multimodal multi-structured information of commodities, such as visual spatial information and fine-grained structure information. However, traditional multimodal text generation focuses on the conventional description of what existed and happened, which does not match the requirement of advertisement copywriting in the real world. Because advertisement copywriting has a vivid language style and higher requirements of faithfulness. Unfortunately, there is a lack of reusable evaluation frameworks and a scarcity of datasets. Therefore, we present a dataset, E-MMAD (e-commercial multimodal multi-structured advertisement copywriting), which requires, and supports much more detailed information in text generation. Noticeably, it is one of the largest video captioning datasets in this field. Accordingly, we propose a baseline method and faithfulness evaluation metric on the strength of structured information reasoning to solve the demand in reality on this dataset. It surpasses the previous methods by a large margin on all metrics. The dataset and method are coming soon on \url{https://e-mmad.github. io/e-mmad.net/index.html}.
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations

An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in production environments is a challenging task. In this work, we present a neural entity linking system that connects the product and organization type entities in business conversations to their corresponding Wikipedia and Wikidata entries. The proposed system leverages Elasticsearch to ensure inference efficiency when deployed in a resource limited cloud machine, and obtains significant improvements in terms of inference speed and memory consumption while retaining high accuracy.
Encouraging Human Interaction With Robot Teams: Legible and Fair Subtask Allocations

Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encourage nearby humans to join their teams in the first place? Inspired by economics, we recognize that humans care about more than just team efficiency — humans also have biases and expectations for team dynamics. Our hypothesis is that the way inclusive robots divide the task (i.e., how the robots split a larger task into subtask allocations) should be both legible and fair to the human partner. In this paper we introduce a bilevel optimization approach that enables robot teams to identify high-level subtask allocations and low-level trajectories that optimize for legibility, fairness, or a combination of both objectives. We then test our resulting algorithm across studies where humans watch or play with robot teams. We find that our approach to generating legible teams makes the human’s role clear, and that humans typically prefer to join and collaborate with legible teams instead of teams that only optimize for efficiency. Incorporating fairness alongside legibility further encourages participation: when humans play with robots, we find that they prefer (potentially inefficient) teams where the subtasks or effort are evenly divided. See videos of our studies here: https://youtu.be/cfN7O5na3mg
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks

Convolutional neural networks are prevailing in deep learning tasks. However, they suffer from massive cost issues when working on mobile devices. Network pruning is an effective method of model compression to handle such problems. This paper presents a novel structured network pruning method with auxiliary gating structures which assigns importance marks to blocks in back-bone network as a criterion when pruning. Block-wise pruning is then realized by proposed voting strategy, which is different from prevailing methods who prune a model in small granularity like channel-wise. We further develop a three-stage training scheduling for the proposed architecture incorporating knowledge distillation for better performance. Our experiments demonstrate that our method can achieve state-of-the-arts compression performance for the classiﬁcation tasks. In addition, our approach can integrate synergisti-cally with other pruning methods by providing pretrained models, thus achieving a better performance than the unpruned model with over 93% FLOPs re-duced.
SeqNet: An Efficient Neural Network for Automatic Malware Detection

Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection. In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.
Intermittent air invasion in pervaporating compliant microchannels

Abstract We explore air invasion in a dead-end compliant water-filled microchannel containing a constriction. The phenomenon is driven by the pervaporation of the liquid present in the channel through the surrounding medium. The penetration is intermittent, jerky and characterised by a stop-and-go dynamics as the bubble escapes the constriction. We demonstrate that this sequence of arrest and jump of the bubble is due to an elasto-capillary coupling between the air–liquid interface and the elastic medium. When the interface enters the constriction, its curvature increases strongly, leading to a depressurisation within the liquid-filled channel that drives a compression of the channel. As the interface is forced to leave the constriction at a given threshold pressure, due to the ongoing loss of liquid content by pervaporation, the pressure is suddenly released, which gives rise to a rapid propagation of the air bubble away from the constriction, and a restoration of the rest shape of the channel. Combining macroscopic observations and confocal imaging, we present a comprehensive experimental study of this phenomenon. In particular, the effect of the channel geometry on the time of arrest in the constriction and the jump length is investigated. Our novel microfluidic design succeeds in mimicking the role of inter-vessel pits in plants, which transiently stop the propagation of air embolisms during long and severe droughts. It is expected to serve as a building block for further biomimetic studies in more complex leaf-like architectures, in order to recover this universal phenomenon of intermittent propagation reported in real leaves.