Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks

Research on automated text summarization relies heavily on human and automatic evaluation. While recent work on human evaluation mainly adopted intrinsic evaluation methods, judging the generic quality of text summaries, e.g. informativeness and coherence, our work focuses on evaluating the usefulness of text summaries with extrinsic methods. We carefully design three different downstream tasks for extrinsic human evaluation of summaries, i.e., question answering, text classification and text similarity assessment. We carry out experiments using system rankings and user behavior data to evaluate the performance of different summarization models. We find summaries are particularly useful in tasks that rely on an overall judgment of the text, while being less effective for question answering tasks. The results show that summaries generated by fine-tuned models lead to higher consistency in usefulness across all three tasks, as rankings of fine-tuned summarization systems are close across downstream tasks according to the proposed extrinsic metrics. Summaries generated by models in the zero-shot setting, however, are found to be biased towards the text classification and similarity assessment tasks, due to its general and less detailed summary style. We further evaluate the correlation of 14 intrinsic automatic metrics with human criteria and show that intrinsic automatic metrics perform well in evaluating the usefulness of summaries in the question-answering task, but are less effective in the other two tasks. This highlights the limitations of relying solely on intrinsic automatic metrics in evaluating the performance and usefulness of summaries.
BAND: Biomedical Alert News Dataset

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. These questions necessitate the model's expert reasoning abilities, thereby offering valuable insights into the outbreak of the disease. The BAND dataset brings new challenges to the NLP world, requiring better inference capability of the content and the ability to infer important information. We provide several benchmark tasks, including Named Entity Recognition (NER), Question Answering (QA), and Event Extraction (EE), to demonstrate existing models' capabilities and limitations in handling epidemiology-specific tasks. It is worth noting that some models may lack the human-like inference capability required to fully utilize the corpus. To the best of our knowledge, the BAND corpus is the largest corpus of well-annotated biomedical outbreak alert news with elaborately designed questions, making it a valuable resource for epidemiologists and NLP researchers alike.
FaceFusion: Exploiting Full Spectrum of Multiple Datasets

The size of training dataset is known to be among the most dominating aspects of training high-performance face recognition embedding model. Building a large dataset from scratch could be cumbersome and time-intensive, while combining multiple already-built datasets poses the risk of introducing large amount of label noise. We present a novel training method, named FaceFusion. It creates a fused view of different datasets that is untainted by identity conflicts, while concurrently training an embedding network using the view in an end-to-end fashion. Using the unified view of combined datasets enables the embedding network to be trained against the entire spectrum of the datasets, leading to a noticeable performance boost. Extensive experiments confirm superiority of our method, whose performance in public evaluation datasets surpasses not only that of using a single training dataset, but also that of previously known methods under various training circumstances.
Prepared for submission to JCAP Inflationary Potential as seen from Different Angles: Model Compatibility from Multiple CMB Missions

The cosmic microwave background (CMB) temperature and polarization anisotropies, as observed by independent astronomical missions such as WMAP, Planck, and most recently the Atacama Cosmology Telescope and the South Pole Telescope have played a vital role in accurately constraining cosmological theories and models, establishing cosmic inflation as the most widely accepted theory for describing the physics of the early Universe. However, the absence of a definitive detection of B-mode polarization and the emerging discrepancies among different CMB experiments present a challenge in determining which inflationary models best explain the observed data. In this work, we further explore this difficulty and conduct a case study by analyzing four well-known inflationary potentials in light of the latest CMB temperature and polarization anisotropy measurements and lensing data released by the Planck satellite and the Atacama Cosmology Telescope. Additionally, we incorporate B-modes polarization data from the BICEP/Keck Collaboration, as well as Baryon Acoustic Oscillations and Redshift Space Distortions measurements from BOSS DR12 and eBOSS DR16. We show that the most typical models such as Starobinsky and α-attractors are in disagreement with the Atacama Cosmology Telescope small-scale CMB measurements, particularly when combined with B-modes polarization data. On the other hand, these potentials are in perfect agreement with the Planck measurements at larger angular scales. This dichotomy makes it challenging to identify a single model or a group of models that can be universally considered as the preferred choice based on all available CMB observations.
ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters

Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into semantically equivalent motions for target characters with different morphologies due to the ambiguous nature of the problem. This work presents a novel learning-based motion retargeting framework, Adversarial Correspondence Embedding (ACE), to retarget human motions onto target characters with different body dimensions and structures. Our framework is designed to produce natural and feasible character motions by leveraging generative-adversarial networks (GANs) while preserving high-level motion semantics by introducing an additional feature loss. In addition, we pretrain a character motion prior that can be controlled in a latent embedding space and seek to establish a compact correspondence. We demonstrate that the proposed framework can produce retargeted motions for three different characters – a quadrupedal robot with a manipulator, a crab character, and a wheeled manipulator. We further validate the design choices of our framework by conducting baseline comparisons and a user study. We also showcase sim-to-real transfer of the retargeted motions by transferring them to a real Spot robot.
Contact-Prioritized Planning of Impact-Resilient Aerial Robots With an Integrated Compliant Arm

This article develops an impact-resilient aerial robot (s-ARQ) equipped with a compliant arm to sense contacts and reduce collision impact and featuring a real-time contact force estimator and a nonlinear motion controller to handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions. Further, a new collision-inclusive planning method that aims to prioritize contacts to facilitate aerial robot navigation in cluttered environments is proposed. A range of simulated and physical experiments demonstrate key benefits of the robot and the contact-prioritized (CP) planner. Experimental results show that the compliant robot has only a 4% weight increase but around 40% impact reduction in drop tests and wall collision tests. s-ARQ can handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions at 3.0 m/s with a success rate of 100%. Our proposed compliant robot and CP planning method can accelerate computation time while having shorter trajectory time and larger clearances compared to A$^\ast$ and RRT$^\ast$ planners with velocity constraints. Online planning tests in partially known environments further demonstrate the preliminary feasibility of our method to apply in practical use cases.
Decay Estimate of Maxwell-Higgs System on Schwarzschild Black Holes

In this paper, we prove the decay estimate of Maxwell-Higgs system on four dimensional Schwarzschild spacetimes. We show that if the field equations support a Morawetz type estimate supported around the trapped surface, the uniform decay properties in the entire exterior of the Schwarzschild black holes can be obtained by using Sobolev inequalities and energy estimates. Our results also consider various forms of physical potential such as the mass terms, $\phi^4$-theory, sine Gordon potential, and Toda potential.
Design a Delicious Lunchbox in Style

We propose a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-image synthesis. To accurately depict and design scenes with multiple occluded objects, we design a pre-trained ordering recovery model and a generative adversarial network to predict layout and composite novel box lunch presentations. In the experiments, we devise the Bento800 dataset to evaluate the performance of the text-to-image synthesis model and the layout generation&image composition model. This paper is the continuation of our previous paper works. We also present additional experiments and qualitative performance comparisons to verify the effectiveness of our proposed method. Bento800 dataset is available at https://github.com/Yutong-Zhou-cv/Bento800_Dataset
Discriminative Entropy Clustering and its Relation to K-means and SVM

Maximization of mutual information between the model's input and output is formally related to"decisiveness"and"fairness"of the softmax predictions, motivating such unsupervised entropy-based losses for discriminative models. Recent self-labeling methods based on such losses represent the state of the art in deep clustering. First, we discuss a number of general properties of such entropy clustering methods, including their relation to K-means and unsupervised SVM-based techniques. Disproving some earlier published claims, we point out fundamental differences with K-means. On the other hand, we show similarity with SVM-based clustering allowing us to link explicit margin maximization to entropy clustering. Finally, we observe that the common form of cross-entropy is not robust to pseudo-label errors. Our new loss addresses the problem and leads to a new EM algorithm improving the state of the art on many standard benchmarks.
Chasing the Speed of Light: Low-Latency Planetary-Scale Adaptive Byzantine Consensus

Blockchain technology has sparked renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works have mainly focused on improving the scalability and throughput of these protocols, few have addressed latency. We present FlashConsensus, a novel transformation for optimizing the latency of quorum-based BFT consensus protocols. FLASHCONSENSUS uses an adaptive resilience threshold that enables faster transaction ordering when the system contains few faulty replicas. Our construction exploits adaptive weighted replication to automatically assign high voting power to the fastest replicas, forming small quorums that significantly speed up consensus. Even when using such quorums with a smaller resilience threshold, FlashConsensus still satisfies the standard SMR safety and liveness guarantees with optimal resilience, thanks to the judicious integration of abortable SMR and BFT forensics techniques. Our experiments with tens of replicas spread in all continents show that FLASHCONSENSUS can order transactions with finality in less than 0.4s, half the time of a PBFT-like protocol (with optimal consensus latency) in the same network, and matching the latency of this protocol running on the theoretically best possible internet links (transmitting at 67% of the speed of light).
Learning from demonstrations: An intuitive VR environment for imitation learning of construction robots

Construction robots are challenging the traditional paradigm of labor intensive and repetitive construction tasks. Present concerns regarding construction robots are focused on their abilities in performing complex tasks consisting of several subtasks and their adaptability to work in unstructured and dynamic construction environments. Imitation learning (IL) has shown advantages in training a robot to imitate expert actions in complex tasks and the policy thereafter generated by reinforcement learning (RL) is more adaptive in comparison with pre-programmed robots. In this paper, we proposed a framework composed of two modules for imitation learning of construction robots. The first module provides an intuitive expert demonstration collection Virtual Reality (VR) platform where a robot will automatically follow the position, rotation, and actions of the expert's hand in real-time, instead of requiring an expert to control the robot via controllers. The second module provides a template for imitation learning using observations and actions recorded in the first module. In the second module, Behavior Cloning (BC) is utilized for pre-training, Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) are combined to achieve a trade-off between the strength of imitation vs. exploration. Results show that imitation learning, especially when combined with PPO, could significantly accelerate training in limited training steps and improve policy performance.
Chiral model of twisted bilayer graphene realized in a monolayer

We demonstrate that a single layer of graphene subject to a superlattice potential nearly commensurate to a $\sqrt{3} \times \sqrt{3}$ supercell exactly maps to the chiral model of twisted bilayer graphene, albeit with half as many degrees of freedom. We comprehensively review the properties of this ``half-chiral model,'' including the interacting phases stabilized at integer fillings and the effects of substrate-induced symmetry breaking. We list candidate substrates that could produce a superlattice potential on graphene with the correct periodicity to access the flat band limit. Experimental measurements on a half-chiral moire heterostructure, in which valley-skyrmions cannot form, could yield insights on the physics they mediate in twisted bilayer graphene.
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA

Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems' specific strengths and weaknesses. To address this limitation, we introduce SALSA, an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. We develop twenty one linguistically grounded edit types, covering the full spectrum of success and failure across dimensions of conceptual, syntactic and lexical simplicity. Using SALSA, we collect 19K edit annotations on 840 simplifications, revealing discrepancies in the distribution of simplification strategies performed by fine-tuned models, prompted LLMs and humans, and find GPT-3.5 performs more quality edits than humans, but still exhibits frequent errors. Using our fine-grained annotations, we develop LENS-SALSA, a reference-free automatic simplification metric, trained to predict sentence- and word-level quality simultaneously. Additionally, we introduce word-level quality estimation for simplification and report promising baseline results. Our data, new metric, and annotation toolkit are available at https://salsa-eval.com.
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4

Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values. Human evaluations are also used in summarization tasks to compare outputs from various systems, complementing existing automatic metrics. Despite their significance, however, there has been limited research probing these pairwise or $k$-wise comparisons. The collective impact and relative importance of factors such as output length, informativeness, fluency, and factual consistency are still not well understood. It is also unclear if there are other hidden factors influencing human judgments. In this paper, we conduct an in-depth examination of a collection of pairwise human judgments released by OpenAI. Utilizing the Bradley-Terry-Luce (BTL) model, we reveal the inherent preferences embedded in these human judgments. We find that the most favored factors vary across tasks and genres, whereas the least favored factors tend to be consistent, e.g., outputs are too brief, contain excessive off-focus content or hallucinated facts. Our findings have implications on the construction of balanced datasets in human preference evaluations, which is a crucial step in shaping the behaviors of future LLMs.
An Ensemble Semi-Supervised Adaptive Resonance Theory Model With Explanation Capability for Pattern Classification

Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this article proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as SSL-ART. Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy ARTMAP structure to map the established prototype nodes to the target classes using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is devised to associate a prototype node with more than one class label. The main advantages of SSL-ART include the capability of: (i) performing online learning, (ii) reducing the number of redundant prototype nodes through the OtM mapping scheme and minimizing the effects of noisy samples, and (iii) providing an explanation facility for users to interpret the predicted outcomes. In addition, a weighted voting strategy is introduced to form an ensemble SSL-ART model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART, assigns a different weight to each class based on its performance pertaining to the corresponding class. The aim is to mitigate the effects of training data sequences on all SSL-ART members and improve the overall performance of WESSL-ART. The experimental results on eighteen benchmark data sets, three artificially generated data sets, and a real-world case study indicate the benefits of the proposed SSL-ART and WESSL-ART models for tackling pattern classification problems.
Towards Optimizing Storage Costs on the Cloud

We study the problem of optimizing data storage and access costs on the cloud while ensuring that the desired performance or latency is unaffected. We first propose an optimizer that optimizes the data placement tier (on the cloud) and the choice of compression schemes to apply, for given data partitions with temporal access predictions. Secondly, we propose a model to learn the compression performance of multiple algorithms across data partitions in different formats to generate compression performance predictions on the fly, as inputs to the optimizer. Thirdly, we propose to approach the data partitioning problem fundamentally differently than the current default in most data lakes where partitioning is in the form of ingestion batches. We propose access pattern aware data partitioning and formulate an optimization problem that optimizes the size and reading costs of partitions subject to access patterns.We study the various optimization problems theoretically as well as empirically, and provide theoretical bounds as well as hardness results. We propose a unified pipeline of cost minimization, called SCOPe that combines the different modules. We extensively compare the performance of our methods with related baselines from the literature on TPC-H data as well as enterprise datasets (ranging from GB to PB in volume) and show that SCOPe substantially improves over the baselines. We show significant cost savings compared to platform baselines, of the order of 50% to 83% on enterprise Data Lake datasets that range from terabytes to petabytes in volume.
Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control

Existing works on outline-conditioned text generation typically aim to generate text using provided outlines as rough sketches, such as keywords and phrases. However, these approaches make it challenging to control the quality of text generation and assess consistency between outlines and generated texts due to lack of clarity and rationality of the rough outlines. In this paper, we introduce a novel text generation task called Precise Outline-conditioned Generation, which requires generating stories based on specific, sentence-level outlines. To facilitate research on this task, we construct two new datasets, WPOG and CDM. We provide strong baselines based on fine-tuning models such as BART and GPT-2, and evaluating zero-shot performance of models such as ChatGPT and Vicuna. Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models. To address this issue, we propose an explicit outline utilization control approach and a novel framework that leverages the task duality between summarization and generation. Experimental results show that the proposed approaches effectively alleviate the issue of imbalanced outline utilization and enhance the quality of precise outline-conditioned text generation for both fine-tuning and zero-shot settings.
NeuralMatrix: Moving Entire Neural Networks to General Matrix Multiplication for Efficient Inference

In this study, we introduce NeuralMatrix, a novel framework that enables the computation of versatile deep neural networks (DNNs) on a single general matrix multiplication (GEMM) accelerator. The proposed approach overcomes the specificity limitations of ASIC-based accelerators while achieving application-specific acceleration levels compared to general-purpose processors such as CPUs and GPUs. We address the challenges of mapping both linear and nonlinear operations in DNN computation to general matrix multiplications and the impact of using a GEMM accelerator on DNN inference accuracy. Extensive experiments are conducted on various DNN models from three popular categories (i.e., CNN, Transformers, and GNN) as illustrative backbone models. Our results demonstrate that DNNs suffer only up to a 2.02% accuracy loss after being converted to general matrix multiplication, while achieving 113x to 19.44x improvements in throughput per power compared to CPUs and GPUs.