Reviewerly/exHarmony huggingface dataset:
Multichip multidimensional quantum networks with entanglement retrievability

Quantum networks provide the framework for quantum communication, clock synchronization, distributed quantum computing, and sensing. Implementing large-scale and practical quantum networks relies on the development of scalable architecture and integrated hardware that can coherently interconnect many remote quantum nodes by sharing multidimensional entanglement through complex-medium quantum channels. We demonstrate a multichip multidimensional quantum entanglement network based on mass-manufacturable integrated-nanophotonic quantum node chips fabricated on a silicon wafer by means of complementary metal-oxide-semiconductor processes. Using hybrid multiplexing, we show that multiple multidimensional entangled states can be distributed across multiple chips connected by few-mode fibers. We developed a technique that can efficiently retrieve multidimensional entanglement in complex-medium quantum channels, which is important for practical uses. Our work demonstrates the enabling capabilities of realizing large-scale practical chip-based quantum entanglement networks.
Chiral Quantum Optics in the Bulk of Photonic Quantum Hall Systems

We study light-matter interactions in the bulk of a two-dimensional photonic lattice system, where photons are subject to the combined effect of a synthetic magnetic field and an orthogonal synthetic electric field. In this configuration, chiral waveguide modes appear in the bulk region of the lattice, in direct analogy to transverse Hall currents in electronic systems. By evaluating the non-Markovian dynamics of emitters that are coupled to those modes, we identify critical coupling conditions, under which the shape of the spontaneously emitted photons becomes almost fully symmetric. Combined with a directional, dispersionless propagation, this property enables a complete reabsorption of the photon by another distant emitter, without relying on any time-dependent control. We show that this mechanism can be generalized to arbitrary in-plane synthetic potentials, thereby enabling flexible realizations of reconfigurable networks of quantum emitters with arbitrary chiral connectivity.7 MoreReceived 3 March 2023Revised 15 June 2023Accepted 28 June 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030306Published 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 AreasHybrid quantum systemsQuantum Hall effectQuantum opticsQuantum optics with artificial atomsSynthetic gauge fieldsTopological effects in photonic systemsAtomic, Molecular & OpticalQuantum InformationInterdisciplinary Physics
OBTAINING UNIQUE BY ANALYZING DNA USING A NEURO-FUZZY ALGORITHM

In this study, the problems of accurate exemplification and a trustworthy test for judging DNA base-calling were discussed. The study of this problem was done by utilizing a model to gather data and develop a suitable design according to the problem's criteria. The height, peakness, and spacing of the first most likely candidate (the base), as well as the peakness and height of the second most likely candidate, are the three input variables in our model. By gathering data from base calling, these three attributes may be computed in the three subsystems and utilized in the main system to calculate the analysis value for each base in DNA sequencing. The MATLAB program was used to create this model. It's looking into applying the neuro fuzzy algorithm to solve the problem. These methods are delivering successful outcomes with high workplace performance and easy access to very precise data.
Exploring the Implication of ChatGPT AI for Business: Efficiency and Challenges

This empirical study aims to examine the impact of ChatGPT AI on various business sectors, evaluating its benefits and challenges. Specifically, the study analyzes how ChatGPT AI is transforming business operations and enhancing customer experience in customer service, e-commerce, healthcare, finance, marketing, and developer business sectors. Using a comprehensive literature review approach, this study draws on relevant academic articles to identify and analyze the key applications of AI in the business sector, the benefits realized, and the challenges faced in adopting this technology. The study explores the implications of ChatGPT AI for businesses, including its potential to improve efficiency, reduce costs, and enhance competitiveness. Additionally, ethical and legal considerations surrounding the use of ChatGPT AI in business operations are evaluated, including issues related to data privacy, bias, and transparency. The study provides insights into future directions for the application of ChatGPT AI in business and the potential impact of this technology on various business sectors. The objective is to provide a comprehensive review of the impact of ChatGPT AI on businesses, its potential benefits, and challenges for businesses looking to adopt this technology. Overall, this study highlights the importance of considering both the benefits and limitations of AI adoption in businesses, as well as the ethical and legal implications of its use.
Transformational leadership for technology integration in schools: Empowering teachers to use technology in a more demanding way

The present study aims to investigate whether the transformational leadership practices of school principals support teachers in using digital technologies in a way that fosters students' engagement in cognitively demanding learning activities in class. To this end, teachers' positive beliefs about digital technology, their technical skills, the digital school infrastructure, and teachers' skills in teaching with digital technologies were investigated as relevant mediators of the relationship between transformational leadership and technology integration. Based on a survey of 2247 upper secondary education teachers in Switzerland, multilevel correlation and structural equation modeling analysis indicated that transformational leadership had a significant and positive impact on the digital school infrastructure, teachers' positive beliefs about digital technology, their technical skills, and their skills in teaching with digital technologies. In turn, all of these factors, except the digital school infrastructure, significantly and positively predicted higher levels of technology integration. These findings position principals’ transformational leadership approaches as crucial in supporting teachers as key players in technology integration in the classroom.
Robust control and optimal Rydberg states for neutral atom two-qubit gates

We investigate the robustness of two-qubit gates to deviations of experimental controls, on a neutral atom platform utilizing Rydberg states. We construct robust CZ gates - employing techniques from quantum optimal control - that retain high Bell state fidelity $F > 0.999$ in the presence of significant deviations of the coupling strength to the Rydberg state. Such deviations can arise from laser intensity noise and atomic motion in an inhomogeneous coupling field. We also discuss methods to mitigate errors due to deviations of the laser detuning. The designed pulses operate on timescales that are short compared to the fundamental decay timescale set by spontaneous emission and blackbody radiation. We account for the finite lifetime of the Rydberg state in both the optimisation and fidelity calculations - this makes the gates conducive to noisy intermediate-scale quantum experiments, meaning that our protocols can reduce infidelity on near-term quantum computing devices. We calculate physical properties associated with infidelity for strontium-88 atoms - including lifetimes, polarisabilities and blockade strengths - and use these calculations to identify optimal Rydberg states for our protocols, which allows for further minimisation of infidelity.
The Moderation of Organizational Readiness on the Relationship Between Toe Factors and Fintech Adoption and Financial Performance

Purpose of the study: The purpose of the study was to discuss the impact of the TOE factors on FinTech Adoption and Financial Performance and the role of organizational readiness as moderation in Jordanian commercial banks.&#x0D; &#x0D; Theoretical framework: In order to examine how to embrace Fintech and its effects on Financial Performance, this study provides an enhanced technology acceptance model (TOE) that includes relative advantage, top management support, competitive pressure, and technological compatibility.&#x0D; &#x0D; Method: A questionnaire that we created and distributed to bank managers, department heads, and supervisors working for Jordanian commercial banks yielded 215 valid replies. To test the hypotheses, we used a structural equation model (SEM) to analyze the data and examine the correlations between all latent variables.&#x0D; &#x0D; Results and conclusion: The results reveal that a positive and significant relationship between Relative Advantage, Top Management Support, and Competitive Pressure on Fintech Adoption and technological compatibility has an insignificant effect on Fintech Adoption, Also showed a positive and significant relationship between Fintech Adoption and Financial Performance. The significant moderating positive effect of organizational readiness on the relationship between Technological Compatibility and Competitive Pressure and Fintech Adoption. This study also shows that does not have a significant moderating positive effect of organizational readiness on the relationship between Relative Advantage, Top Management Support, and Fintech Adoption.&#x0D; &#x0D; Search implications: The study's findings imply the need for further research and exploration into FinTech Adoption to improve the financial performance of commercial banks to remain competitive in the market.&#x0D; Originality value: This research contributes to integrating the adoption of Fintech's trust with TOE, this study adds to the body of knowledge on the usage of Fintech in banks by offering a more thorough understanding of the factors that influence users' views.
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model

This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC).Our formulation relaxes the conditional independence assumptions used in conventional CTC and incorporates linguistic knowledge through the explicit output dependency obtained by BERT contextual embedding.BERT-CTC attends to the full contexts of the input and hypothesized output sequences via the self-attention mechanism.This mechanism encourages a model to learn inner/interdependencies between the audio and token representations while maintaining CTC's training efficiency.During inference, BERT-CTC combines a mask-predict algorithm with CTC decoding, which iteratively refines an output sequence.The experimental results reveal that BERT-CTC improves over conventional approaches across variations in speaking styles and languages.Finally, we show that the semantic representations in BERT-CTC are beneficial towards downstream spoken language understanding tasks.
Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment

Unpaired cross-lingual image captioning has long suffered from irrelevancy and disfluency issues, due to the inconsistencies of the semantic scene and syntax attributes during transfer.In this work, we propose to address the above problems by incorporating the scene graph (SG) structures and the syntactic constituency (SC) trees.Our captioner contains the semantic structure-guided image-to-pivot captioning and the syntactic structure-guided pivot-to-target translation, two of which are joined via pivot language.We then take the SG and SC structures as pivoting, performing cross-modal semantic structure alignment and cross-lingual syntactic structure alignment learning.We further introduce cross-lingual&cross-modal back-translation training to fully align the captioning and translation stages.Experiments on English↔Chinese transfers show that our model shows great superiority in improving captioning relevancy and fluency.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications.In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels.However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs.We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation.Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework.We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples.Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes.Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset. 1
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder

Despite pre-training's progress in many important NLP tasks, it remains to explore effective pre-training strategies for dense retrieval.In this paper, we propose RetroMAE, a new retrieval oriented pre-training paradigm based on Masked Auto-Encoder (MAE).RetroMAE is highlighted by three critical designs.1) A novel MAE workflow, where the input sentence is polluted for encoder and decoder with different masks.The sentence embedding is generated from the encoder's masked input; then, the original sentence is recovered based on the sentence embedding and the decoder's masked input via masked language modeling.2) Asymmetric model structure, with a full-scale BERT like transformer as encoder, and a one-layer transformer as decoder.3) Asymmetric masking ratios, with a moderate ratio for encoder: 15∼30%, and an aggressive ratio for decoder: 50∼70%.Our framework is simple to realize and empirically competitive: the pre-trained models dramatically improve the SOTA performances on a wide range of dense retrieval benchmarks, like BEIR and MS MARCO.The source code and pre-trained models are made publicly available at https://github.com/staoxiao/RetroMAEso as to inspire more interesting research.
Re3: Generating Longer Stories With Recursive Reprompting and Revision

We consider the problem of automatically generating longer stories of over two thousand words.Compared to prior work on shorter stories, long-range plot coherence and relevance are more central challenges here.We propose the Recursive Reprompting and Revision framework (Re 3 ) to address these challenges by (a) prompting a general-purpose language model to construct a structured overarching plan, and (b) generating story passages by repeatedly injecting contextual information from both the plan and current story state into a language model prompt.We then revise by (c) reranking different continuations for plot coherence and premise relevance, and finally (d) editing the best continuation for factual consistency.Compared to similar-length stories generated directly from the same base model, human evaluators judged substantially more of Re 3 's stories as having a coherent overarching plot (by 14% absolute increase), and relevant to the given initial premise (by 20%). Autoregressive ContextAI, Open Philanthropy, DARPA under the Se-maFor program (HR00112020054), the Machine Common Sense (MCS) program under Cooperative Agreement N66001-19-2-4032, and the NSF through a fellowship to the first author.The content does not necessarily reflect the position or the policy of
Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

Transformer-based language models (LMs) are at the core of modern NLP, but their internal prediction construction process is opaque and largely not understood.In this work, we make a substantial step towards unveiling this underlying prediction process, by reverseengineering the operation of the feed-forward network (FFN) layers, one of the building blocks of transformer models.We view the token representation as a changing distribution over the vocabulary, and the output from each FFN layer as an additive update to that distribution.Then, we analyze the FFN updates in the vocabulary space, showing that each update can be decomposed to sub-updates corresponding to single FFN parameter vectors, each promoting concepts that are often human-interpretable.We then leverage these findings for controlling LM predictions, where we reduce the toxicity of GPT2 by almost 50%, and for improving computation efficiency with a simple early exit rule, saving 20% of computation on average.
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions.However, they treat all labels and label embeddings as uncorrelated individuals, ignoring the dependencies among them.Besides, they conduct the decoding for the two tasks independently, without leveraging the correlations between them.Therefore, in this paper, we first construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies: (1) statistical dependencies based on labels' co-occurrence patterns and hierarchies in slot labels; (2) rich relations among the label nodes.Then we propose a novel model termed ReLa-Net.It can capture beneficial correlations among the labels from HLG.The label correlations are leveraged to enhance semantic-label interactions.Moreover, we also propose the label-aware inter-dependent decoding mechanism to further exploit the label correlations for decoding.Experiment results show that our ReLa-Net significantly outperforms previous models.Remarkably, ReLa-Net surpasses the previous best model by over 20% in terms of overall accuracy on MixATIS dataset.
A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict

Hate speech consists of types of content (e.g.text, audio, image) that express derogatory sentiments and hate against certain people or groups of individuals.The internet, particularly social media and microblogging sites, have become an increasingly popular platform for expressing ideas and opinions.Hate speech is prevalent in both offline and online media.A substantial proportion of this kind of content is presented in different modalities (e.g.text, image, video).Taking into account that hate speech spreads quickly during political events, we present a novel multimodal dataset composed of 5680 text-image pairs of tweets data related to the Russia-Ukraine war and annotated with a binary class: "hate" or "no-hate" The baseline results show that multimodal resources are relevant to leverage the hateful information from different types of data.The baselines and dataset provided in this paper may boost researchers in direction of multimodal hate speech, mainly during serious conflicts such as war contexts.
MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning

Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling.However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives.We present MAGMA -a simple method for augmenting generative language models with additional modalities using adapter-based finetuning.Building on Frozen (Tsimpoukelli et al., 2021), we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input.The pretraining is entirely end-to-end using a single language modeling objective, simplifying optimization compared to previous approaches.Importantly, the language model weights remain unchanged during training, allowing for transfer of encyclopedic knowledge and in-context learning abilities from language pretraining.MAGMA outperforms Frozen on open-ended generative tasks, achieving state of the art results on the OKVQA benchmark and competitive results on a range of other popular VL benchmarks, while pretraining on ∼ 0.2% of the number of samples used to train SimVLM (Wang et al., 2021).
Are Large Pre-Trained Language Models Leaking Your Personal Information?

Are Large Pre-Trained Language Models Leaking Your Personal Information?In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information.Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name.We find that PLMs do leak personal information due to memorization.However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low.We hope this work could help the community to better understand the privacy risk of PLMs and bring new insights to make PLMs safe. 1
Towards Tracing Knowledge in Language Models Back to the Training Data

Language models (LMs) have been shown to memorize a great deal of factual knowledge contained in their training data.But when an LM generates an assertion, it is often difficult to determine where it learned this information and whether it is true.In this paper, we propose the problem of fact tracing: identifying which training examples taught an LM to generate a particular factual assertion.Prior work on training data attribution (TDA) may offer effective tools for identifying such examples, known as "proponents".We present the first quantitative benchmark to evaluate this.We compare two popular families of TDA methods -gradient-based and embedding-based -and find that much headroom remains.For example, both methods have lower proponent-retrieval precision than an information retrieval baseline (BM25) that does not have access to the LM at all.We identify key challenges that may be necessary for further improvement such as overcoming the problem of gradient saturation, and also show how several nuanced implementation details of existing neural TDA methods can significantly improve overall fact tracing performance. 1