Reviewerly/exHarmony huggingface dataset:
Detecting High-Dimensional Entanglement in Cold-Atom Quantum Simulators

Quantum entanglement has been identified as a crucial concept underlying many intriguing phenomena in condensed matter systems, such as topological phases or many-body localization. Recently, instead of considering mere quantifiers of entanglement such as entanglement entropy, the study of entanglement structure in terms of the entanglement spectrum has shifted to a focus leading to new insights into fractional quantum Hall states and topological insulators, among others. What remains a challenge is the experimental detection of such fine-grained properties of quantum systems. The development of protocols for detecting features of the entanglement spectrum in cold-atom systems, which are one of the leading platforms for quantum simulation, is thus highly desirable and will open up new avenues for experimentally exploring quantum many-body physics. Here, we present a method to bound the width of the entanglement spectrum, or entanglement dimension, of cold atoms in lattice geometries, requiring only measurements in two experimentally accessible bases and utilizing ballistic time-of-flight (TOF) expansion. Building on previous proposals for entanglement certification for photon pairs, we first consider entanglement between two atoms of different atomic species and later generalize to higher numbers of atoms per species and multispecies configurations showing multipartite high-dimensional entanglement. Through numerical simulations, we show that our method is robust against typical experimental noise effects and thus will enable high-dimensional entanglement certification in systems of up to eight atoms using currently available experimental techniques.9 MoreReceived 12 May 2023Accepted 5 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040338Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionEntanglement in quantum gasesQuantum simulationQuantum Information, Science & TechnologyAtomic, Molecular & Optical
Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.
The fall of creativity: A librarian’s role in the world of AI

T he world is a global landscape that is forever evolving with the rise of technology, information access, and labor outsourcing. Artificial intelligence is growing exponentially in the United States, in many ways aiding in general tasks. However, generative AI is learning and developing a high rate of consciousness, which could soon replace our role as human creators. We as librarians must take a step back and reflect on the long-term effects of this technology, not just react. We must ask ourselves, what world do we want to leave for the future generations? How do we want to steer these conversations? For decades America was at the cutting edge of the industrial revolution; manufacturing plants flourished throughout the country, and creating material goods was a key component of the American Dream. With the advancements in technology, production work labor required less human intervention. Eventually most manufacturing left the United States as labor and automation could be found cheaper elsewhere. 1 Yet American ingenuity and creativity remained; even flourishing in the new technology-dense landscape. With faster computers, cars, and machines, time was freed up for humans to explore their thoughts and ideas. This shift is seen through the dramatic increase of entrepreneurship, influencers, television programs, movies, artists, writers, and online creative communities. As a society we entered the age of the mind.
Correlation-guided Semantic Consistency Network for Visible-infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) has raised more attention in night-time surveillance applications due to the struggle to capture valid appearance information under poor illumination conditions via visible cameras. Existing works usually separate the modality-specific and modality-irrelevant information in visible and infrared features, or project features of two modalities into a unified embedding feature space directly, which aims to eliminate huge modality discrepancies. However, these methods neglect the intra-modality and inter-modality correlations. We argue that the correlations can implicitly guide the network to discover the modality-irrelevant information, thus more beneficial for eliminating huge modality discrepancies and preserving individual differences. To this end, we propose a novel framework, termed as correlation-guided semantic consistency network (CSC-Net), to explore and exploit the intra-modality and inter-modality correlations. Specifically, CSC-Net consists of a cross-modality semantic alignment (CSA) module, a cross-granularity discrepancy awareness (CDA) module, and a probability consistency constraint (PCC) module. CSA mines the inter-modality correlation by calculating the semantic similarity between modalities to explore modality-irrelevant features, and then transfers the learned features to the backbone network to face the input of only single modality images. To preserve the individual differences, CDA sufficiently utilizes the intra-modality correlation via exploring the multi-granularity discriminative information. Finally, PCC constrains the network at the probability level, cooperating with the CSA which constrains at the feature level, to further alleviate the modality discrepancy. Extensive experiments on two public VI-ReID datasets SYSU-MM01 and RegDB have verified the effectiveness of our approach.
CTVSR: Collaborative Spatial-Temporal Transformer for Video Super-Resolution

Video super-resolution (VSR) is important in video processing for reconstructing high-definition image sequences from corresponding continuous and highly-related video frames. However, existing VSR methods have limitations in fusing spatial-temporal information. Some methods only fuse spatial-temporal information on a limited range of total input sequences, while others adopt a recurrent strategy that gradually attenuates the spatial information. While recent advances in VSR utilize Transformer-based methods to improve the quality of the upscaled videos, these methods require significant computational resources to model the long-range dependencies, which dramatically increases the model complexity. To address these issues, we propose a Collaborative Transformer for Video Super-Resolution (CTVSR). The proposed method integrates the strengths of Transformer-based and recurrent-based models by concurrently assimilating the spatial information derived from multi-scale receptive fields and the temporal information acquired from temporal trajectories. In particular, we propose a Spatial Enhanced Network (SEN) with two key components: Token Dropout Attention (TDA) and Deformable Multi-head Cross Attention (DMCA). TDA focuses on the key regions to extract more informative features, and DMCA employs deformable cross attention to gather information from adjacent frames. Moreover, we introduce a Temporal-trajectory Enhanced Network (TEN) that computes the similarity of a given token with temporally-related tokens in the temporal trajectory, which is different from previous methods that evaluate all tokens within the temporal dimension. With comprehensive quantitative and qualitative experiments on four widely-used VSR benchmarks, the proposed CTVSR achieves competitive performance with relatively low computational consumption and high forward speed.
Versa-DNN: A Versatile Architecture Enabling High-Performance and Energy-Efficient Multi-DNN Acceleration

Emerging applications utilize numerous Deep Neural Networks (DNNs) to address multiple tasks simultaneously. As these applications continue to expand, there is a growing need for off-chip memory access optimization and innovative architectures that can adapt to diverse computation, memory, and communication requirements of various DNN models. To address these challenges, we propose Versa-DNN, a versatile DNN accelerator that can provide efficient computation, memory, and communication support for the simultaneous execution of multiple DNNs. Versa-DNN features three unique designs: a flexible off-chip memory access optimization strategy, adaptable communication fabrics, and a communication and computational aware scheduling algorithm. The proposed off-chip memory optimization strategy can improve performance and energy efficiency by increasing hardware utilization, eliminating excess data duplication, and reducing off-chip memory accesses. The adaptable communication fabrics consist of distributed buffers, processing elements, and a flexible Network-on-Chip (NoC), which can dynamically morph and fission to support distinct communication and computation needs for simultaneously running DNN models. Furthermore, the proposed scheduling policy manages the simultaneous execution of multiple DNN models with improved performance and energy efficiency. Simulation results using several DNN models, show that the proposed Versa-DNN architecture achieves 41%, 238%, 392% throughput speedup and 30%, 59%, 63% energy reduction on average for different workloads when compared to state-of-the-art accelerators such as Planaria, Herald, and AI-MT, respectively.
Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification

This study presents a novel approach to medical text classification using a deep active incremental learning model, aiming to improve the automation of the preauthorization process in medical health insurance. By automating decision-making for request approval or denial through text classification techniques, the primary focus is on real-time prediction, utilization of limited labeled data, and continuous model improvement. The proposed approach combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with active learning, using uncertainty sampling to facilitate expert-based sample selection and online learning for continuous updates. The proposed model demonstrates improved predictive accuracy over a baseline Long Short-Term Memory (LSTM) model. Through active learning iterations, the proposed model achieved a 4% improvement in balanced accuracy over 100 iterations, underscoring its efficiency in continuous refinement using limited labeled data.
The Longest Subsequence-Repeated Subsequence Problem

Motivated by computing duplication patterns in sequences, a new fundamental problem called the longest subsequence-repeated subsequence (LSRS) is proposed. Given a sequence S of length n, a letter-repeated subsequence is a subsequence of S in the form of $$x_1^{d_1}x_2^{d_2}\cdots x_k^{d_k}$$ with $$x_i$$ a subsequence of S, $$x_j\ne x_{j+1}$$ and $$d_i\ge 2$$ for all i in [k] and j in $$[k-1]$$ . We first present an $$O(n^6)$$ time algorithm to compute the longest cubic subsequences of all the $$O(n^2)$$ substrings of S, improving the trivial $$O(n^7)$$ bound. Then, an $$O(n^6)$$ time algorithm for computing the longest subsequence-repeated subsequence (LSRS) of S is obtained. Finally we focus on two variants of this problem. We first consider the constrained version when $$\varSigma $$ is unbounded, each letter appears in S at most d times and all the letters in $$\varSigma $$ must appear in the solution. We show that the problem is NP-hard for $$d=4$$ , via a reduction from a special version of SAT (which is obtained from 3-COLORING). We then show that when each letter appears in S at most $$d=3$$ times, then the problem is solvable in $$O(n^4)$$ time.
Yolov3 Tabanlı Atık Tespit Sistemleri

Today, the increase in the number of people, advances in industry and technology cause an increase in the number of wastes generated with the acceleration of production. It is important for the future of our country and the world that these wastes are more easily identified and recycled. In the process of recycling wastes, the classification of wastes as well as their collection requires costly energy and manpower. Wastes are basically separated into paper, plastic, glass and metal. Various studies have been carried out to complete these processes in a shorter and easier way with technologies such as artificial intelligence, deep learning and image processing. In this study, a dataset of paper, plastic and food and beverage wastes that are common in the environment was created. In this dataset, paper cups, plastic water bottles and fast food wastes were detected from different locations in nature and photographed. These images were labeled and trained and tested with YoloV3 in deep learning algorithms. In addition, in order to compare the performance of the new dataset, studies were conducted on a global dataset used in the literature. As a result of the studies, it was observed that it was successful in classifying the newly created dataset and the global dataset.
An Investigation of LLMs’ Inefficacy in Understanding Converse Relations

Large Language Models (LLMs) have achieved remarkable success in many formal language oriented tasks, such as structural data-to-text and semantic parsing. However current benchmarks mostly follow the data distribution of the pre-training data of LLMs. Therefore, a natural question rises that do LLMs really understand the structured semantics of formal languages. In this paper, we investigate this problem on a special case, converse binary relation. We introduce a new benchmark ConvRe focusing on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. Our ConvRE features two tasks, Re2Text and Text2Re, which are formulated as multi-choice question answering to evaluate LLMs’ ability to determine the matching between relations and associated text. For the evaluation protocol, apart from different prompting methods, we further introduce variants to the test text and few-shot example text. We conduct experiments on three popular LLM families and have observed various scaling trends. The results suggest that LLMs often resort to shortcut learning and still face challenges on our proposed benchmark.
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. Previous works have achieved success by unifying a few tasks, such as Named Entity Recognition (NER) and Relation Extraction (RE), while they fall short of being true UIE models particularly when extracting other general schemas such as quadruples and quintuples. Additionally, these models used an implicit structural schema instructor, which could lead to incorrect links between types, hindering the model’s generalization and performance in low-resource scenarios. In this paper, we redefine the true UIE with a formal formulation that covers almost all extraction schemas. To the best of our knowledge, we are the first to introduce UIE for any kind of schemas. In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE. To avoid interference between different types, we reset the position ids and attention mask matrices. RexUIE shows strong performance under both full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis

Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language Models (LLMs) has made authorship analysis increasingly crucial for distinguishing between human-written and AI-generated texts. However, these authorship analysis tasks have primarily been focused on written texts, not considering spoken texts. Thus, we introduce the largest benchmark for spoken texts - \sf HANSEN( ̲Human ̲ANd ai ̲Spoken t ̲Ext be ̲Nchmark). \sf HANSEN encompasses meticulous curation of existing speech datasets accompanied by transcripts, alongside the creation of novel AI-generated spoken text datasets. Together, it comprises 17 human datasets, and AI-generated spoken texts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. To evaluate and demonstrate the utility of \sf HANSEN, we perform Authorship Attribution (AA) & Author Verification (AV) on human-spoken datasets and conducted Human vs. AI text detection using state-of-the-art (SOTA) models. While SOTA methods, such as, character n-gram or Transformer-based model, exhibit similar AA & AV performance in human-spoken datasets compared to written ones, there is much room for improvement in AI-generated spoken text detection. The \sf HANSEN benchmark is available at: https://huggingface.co/datasets/HANSEN-REPO/HANSEN
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models

Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the performance of LLMs is greatly influenced by the quality of these instructions, and manually writing effective instructions for each task is a laborious and subjective process. In this paper, we introduce Auto-Instruct, a novel method to automatically improve the quality of instructions provided to LLMs. Our method leverages the inherent generative ability of LLMs to produce diverse candidate instructions for a given task, and then ranks them using a scoring model trained on a variety of 575 existing NLP tasks. In experiments on 118 out-of-domain tasks, Auto-Instruct surpasses both human-written instructions and existing baselines of LLM-generated instructions. Furthermore, our method exhibits notable generalizability even with other LLMs that are not incorporated into its training process.
Revisiting Large Language Models as Zero-shot Relation Extractors

Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data even if under zero-shot setting. Recent studies have shown that large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt, which provides the possibility of extracting relations from text without any data and parameter tuning. This work focuses on the study of exploring LLMs, such as ChatGPT, as zero-shot relation extractors. On the one hand, we analyze the drawbacks of existing RE prompts and attempt to incorporate recent prompt techniques such as chain-of-thought (CoT) to improve zero-shot RE. We propose the summarize-and-ask (SumAsk) prompting, a simple prompt recursively using LLMs to transform RE inputs to the effective question answering (QA) format. On the other hand, we conduct comprehensive experiments on various benchmarks and settings to investigate the capabilities of LLMs on zero-shot RE. Specifically, we have the following findings: (i) SumAsk consistently and significantly improves LLMs performance on different model sizes, benchmarks and settings; (ii) Zero-shot prompting with ChatGPT achieves competitive or superior results compared with zero-shot and fully supervised methods; (iii) LLMs deliver promising performance in extracting overlapping relations; (iv) The performance varies greatly regarding different relations. Different from small language models, LLMs are effective in handling challenge none-of-the-above (NoTA) relation.
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness

Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them. Most existing methods focus solely on whether the reasoning chain leads to the correct conclusion, but this answer-oriented view may confound reasoning quality with other spurious shortcuts to predict the answer. To bridge this gap, we evaluate reasoning chains by viewing them as informal proofs that derive the final answer. Specifically, we propose ReCEval (Reasoning Chain Evaluation), a framework that evaluates reasoning chains via two key properties: (1) correctness, i.e., each step makes a valid inference based on information contained within the step, preceding steps, and input context, and (2) informativeness, i.e., each step provides new information that is helpful towards deriving the generated answer. We evaluate these properties by developing metrics using natural language inference models and 𝒱-Information. On multiple datasets, we show that ReCEval effectively identifies various error types and yields notable improvements compared to prior methods. We analyze the impact of step boundaries, and previous steps on evaluating correctness and demonstrate that our informativeness metric captures the expected flow of information in high-quality reasoning chains. Finally, we show that scoring reasoning chains based on ReCEval improves downstream task performance.
Scaling up CometKiwi: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task

We present the joint contribution of Unbabel and Instituto Superior Técnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: Sentence- and Word-level Quality Prediction and Fine-grained error span detection. For all tasks we build on the CometKiwi model (rei et al. 2022). Our multilingual approaches are ranked first for all tasks, reaching state-of-the-art performance for quality estimation at word-, span- and sentence-level granularity. Compared to the previous state-of-the-art, CometKiwi, we show large improvements in correlation with human judgements (up to 10 Spearman points) and surpassing the second-best multilingual submission with up to 3.8 absolute points.