Reviewerly/exHarmony huggingface dataset:
Offline RL With Resource Constrained Online Deployment

Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh conditions, an agent may lack the resources to fully observe the online environment before taking an action. We dub this situation the resource-constrained setting. This leads to situations where the offline dataset (available for training) can contain fully processed features (using powerful language models, image models, complex sensors, etc.) which are not available when actions are actually taken online. This disconnect leads to an interesting and unexplored problem in offline RL: Is it possible to use a richly processed offline dataset to train a policy which has access to fewer features in the online environment? In this work, we introduce and formalize this novel resource-constrained problem setting. We highlight the performance gap between policies trained using the full offline dataset and policies trained using limited features. We address this performance gap with a policy transfer algorithm which first trains a teacher agent using the offline dataset where features are fully available, and then transfers this knowledge to a student agent that only uses the resource-constrained features. To better capture the challenge of this setting, we propose a data collection procedure: Resource Constrained-Datasets for RL (RC-D4RL). We evaluate our transfer algorithm on RC-D4RL and the popular D4RL benchmarks and observe consistent improvement over the baseline (TD3+BC without transfer). The code for the experiments is available at https://github.com/JayanthRR/RC-OfflineRL.
Digital quantum simulation of strong correlation effects with iterative quantum phase estimation over the variational quantum eigensolver algorithm: $\mathrm{H_4}$ on a circle as a case study

The iterative quantum phase estimation algorithm, applied to calculating the ground state energies of quantum chemical systems, is theoretically appealing in its wide scope of being able to handle both weakly and strongly correlated regimes. However, the goodness of the initial state that is sent as an input to the algorithm could strongly decide the quality of the results obtained. In this work, we generate the initial state by using the classical-quantum hybrid variational quantum eigensolver algorithm with unitary coupled cluster ansatz. We apply the procedure to obtain the ground state energies of the H4 molecule on a circle, as the system exhibits an interplay of dynamic as well as static correlation effects at different geometries. Furthermore, we argue on the importance of static correlation in construction of the reference determinant, and propose a minimally parametrized unitary coupled cluster ansatz, which drastically reduces number of variational parameters while incorporating the static correlation effects in the wavefunction. We demonstrate that a carefully and appropriately prepared initial state can greatly reduce the effects of noise due to sampling in the estimation of the desired eigenphase.
Asymptotically Optimal Prepare-Measure Quantum Key Distribution Protocol

Quantum key distribution (QKD) could be the most significant application of quantum information theory. In nearly four decades, although substantial QKD protocols are developed, the BB84 protocol and its variants are still the most researched ones. It is well-known that the secure bound of qubit error rate (QBER) of BB84 protocol is about 11 $$\%$$ while it can be increased to 12.6 $$\%$$ by six-state protocol. It would not be surprising that employing more basis could increase the bound. However, what is the optimal protocol, and how to analyze it? In this paper, investigations of asymptotically optimal QKD protocols are proposed. Precisely, We present an abstraction of prepare-measure QKD protocols and investigate two special cases which are optimal among all protocols coding by the same states. Our analysis demonstrates that the asymptotically optimal QBER bounds coding by orthogonal qubits are about 27.28 $$\%$$ for both memory C-NOT attacks and memoryless C-NOT attacks while the bounds coding by non-orthogonal states in two mutually unbiased bases are about 22.73 $$\%$$ for memory and 28.69 $$\%$$ for memoryless C-NOT attacks. The protocols are idealized but might be asymptotically realized while their optimality indicates the ultimate potential of QKD protocols. Although the analysis only contains a special kind of attack, it provides a framework for investigating such protocols.
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts

Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can make them less effective when assisting humans on more complex tasks. In response, we introduce the concept of Chaining LLM steps together, where the output of one step becomes the input for the next, thus aggregating the gains per step. We first define a set of LLM primitive operations useful for Chain construction, then present an interactive system where users can modify these Chains, along with their intermediate results, in a modular way. In a 20-person user study, we found that Chaining not only improved the quality of task outcomes, but also significantly enhanced system transparency, controllability, and sense of collaboration. Additionally, we saw that users developed new ways of interacting with LLMs through Chains: they leveraged sub-tasks to calibrate model expectations, compared and contrasted alternative strategies by observing parallel downstream effects, and debugged unexpected model outputs by "unit-testing" sub-components of a Chain. In two case studies, we further explore how LLM Chains may be used in future applications
A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks

Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies. This makes it difficult to keep track of where novel AI methods are successfully -- or still unsuccessfully -- applied, how progress is measured, how different advances might synergize with each other, and how future research should be prioritized. To help address these issues, we created the Intelligence Task Ontology and Knowledge Graph (ITO), a comprehensive, richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The current version of ITO contain 685,560 edges, 1,100 classes representing AI processes and 1,995 properties representing performance metrics. The goal of ITO is to enable precise and network-based analyses of the global landscape of AI tasks and capabilities. ITO is based on technologies that allow for easy integration and enrichment with external data, automated inference and continuous, collaborative expert curation of underlying ontological models. We make the ITO dataset and a collection of Jupyter notebooks utilising ITO openly available.
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning

Privacy is important considering the financial Domain as such data is highly confidential and sensitive. Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains such as customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data. In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features such as Differential Privacy (DP) and Federated Learning (FL). We present how to privately train NLP models and desirable privacy-utility tradeoffs and evaluate them on the Financial Phrase Bank dataset.
The case for the Humanities Citation Index (HuCI): a citation index by the humanities, for the humanities

Citation indexes are by now part of the research infrastructure in use by most scientists: a necessary tool in order to cope with the increasing amounts of scientific literature being published. Commercial citation indexes are designed for the sciences and have uneven coverage and unsatisfactory characteristics for humanities scholars, while no comprehensive citation index is published by a public organization. We argue that an open citation index for the humanities is desirable, for four reasons: it would greatly improve and accelerate the retrieval of sources, it would offer a way to interlink collections across repositories (such as archives and libraries), it would foster the adoption of metadata standards and best practices by all stakeholders (including publishers) and it would contribute research data to fields such as bibliometrics and science studies. We also suggest that the citation index should be informed by a set of requirements relevant to the humanities. We discuss four such requirements: source coverage must be comprehensive, including books and citations to primary sources; there needs to be chronological depth, as scholarship in the humanities remains relevant over time; the index should be collection-driven, leveraging the accumulated thematic collections of specialized research libraries; and it should be rich in context in order to allow for the qualification of each citation, for example by providing citation excerpts. We detail the fit-for-purpose research infrastructure which can make the Humanities Citation Index a reality. Ultimately, we argue that a citation index for the humanities can be created by humanists, via a collaborative, distributed and open effort.
Support Vector Machine Method with Word2vec for Covid-19 Vaccine Sentiment Classification on Twitter

Covid-19 has been a dangerous outbreak for the world that has lasted more than 2 years. Covid-19 has evolved or developed into several new variations, such as delta which is more dangerous than its initial variant. Vaccines became the world's solution to defend against Covid-19. In Indonesia, at the early stages of implementing mass vaccination programs, people had been involved in many pros and cons, to support or against the program. On social media such as Twitter, public opinions about vaccines are very diverse. This study investigates public sentiment towards the early stage of vaccination program conducted by the government. The classification method used in the sentiment analysis is the Support Vector Machine (SVM), among the positive, negative and neutral classes, with word embeddings extraction features. Data was collected and labeled by 12 crowd sourced annotators. The training and parameter tuning process was carried out to find the model that produced the best accuracy of validation data. From 400 testing data, the application of this optimal model resulted in an F1-score of 65% and an accuracy of 69%, higher than several machine learning methods in the same study.
Twitter User Sentiment Analysis on the Mysterious Hepatitis Issue Rstudio

In the past few years, Corona Virus Disease 2019 has become the most psychologically disturbing issue for Indonesian people since they first entered Indonesia in early 2020. After starting to subside in 2022, there is now a new issue, namely Mysterious Hepatitis, a frightening specter for parents. This mysterious hepatitis attacks many children and causes death. Social media is a source of Big Data. Media such as Twitter have been used to collect and analyze user data for several different purposes. Data can be used to analyze people's opinions on specific topics and incidents by applying sentiment analysis. Certain helpful information can be extracted from the analyzed data. The purpose of this study is to explore public or netizen opinions regarding mysterious hepatitis via data from netizens' tweets on Twitter. The method used in this research is to collect tweets, then clean the tweets, and finally analyze the data that has been processed via RStudio. The results of this study provide information that the words that often appear are acute, child, case, and health. From bigrams, related words often appear acute hepatitis, mysterious hepatitis, acute mysterious, children, and covid 19. By analyzing the type of emotion using the NRC Word-Emotion Association Lexicon method, Twitter users also provide various emotions related to the #HepatitisMysterius issue, including eight positive emotional responses and six positive emotional responses. â€œFearâ€ was the most frequent emotion followed by â€œanticipationâ€, â€œtrustâ€, â€œsurpriseâ€, â€œangerâ€, â€œdisgustâ€, â€œsadnessâ€ and â€œjoyâ€. From the results of this study, it also informs the advice to be careful with this mysterious hepatitis, because there is advice on being alert for hepatitis and taking care of your health
RAFT: A Real-World Few-Shot Text Classification Benchmark

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don't directly answer this question. The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at https://raft.elicit.org .
Finding Facial Forgery Artifacts with Parts-Based Detectors

Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.
ElasticFace: Elastic Margin Loss for Deep Face Recognition

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks.
String Diagram Rewrite Theory III: Confluence with and without Frobenius

In this paper we address the problem of proving confluence for string diagram rewriting, which was previously shown to be characterised combinatorically as double-pushout rewriting with interfaces (DPOI) on (labelled) hypergraphs. For standard DPO rewriting without interfaces, confluence for terminating rewrite systems is, in general, undecidable. Nevertheless, we show here that confluence for DPOI, and hence string diagram rewriting, is decidable. We apply this result to give effective procedures for deciding local confluence of symmetric monoidal theories with and without Frobenius structure by critical pair analysis. For the latter, we introduce the new notion of path joinability for critical pairs, which enables finitely many joins of a critical pair to be lifted to an arbitrary context in spite of the strong non-local constraints placed on rewriting in a generic symmetric monoidal theory.
Birkhoff-James orthogonality in the trace norm, with applications to quantum resource theories

Numerous results are presented that characterize when a complex Hermitian matrix is Birkhoff-James orthogonal, in the trace norm, to a (Hermitian) positive semidefinite matrix or set of positive semidefinite matrices. For example, a simple-to-test criterion that determines which Hermitian matrices are Birkhoff-James orthogonal, in the trace norm, to the set of all positive semidefinite diagonal matrices is developed. Applications in the theory of quantum resources are explored. For example, the quantum states that have modified trace distance of coherence equal to $1$ (the maximal possible value) are characterized, and a connection between the modified trace distance of $2$-entanglement and the NPPT bound entanglement problem is established.
ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections

In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets. However, the current datasets are essentially designed for synchronic document collections (e.g., Wikipedia). Temporal news collections such as long-term news archives spanning several decades, are rarely used in training the models despite they are quite valuable for our society. To foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 532,444 question-answer pairs which is designed for temporal news QA. We divide our dataset into four subparts based on the question difficulty levels and the containment of temporal expressions, which we believe are useful for training and testing ODQA systems characterized by different strengths and abilities. The novel QA dataset-constructing framework that we introduce can be also applied to generate non-ambiguous questions of good quality over other types of temporal document collections.
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction

Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data). We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, allowing to report the best results to date on TACRED when fully trained. The analysis shows that our few-shot systems are specially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.
Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices. However, the optimization of quantum machine learning models presents numerous challenges arising from the imperfections of hardware and the fundamental obstacles in navigating an exponentially scaling Hilbert space. In this work, we evaluate the potential of contemporary methods in deep reinforcement learning to augment gradient based optimization routines in quantum variational circuits. We find that reinforcement learning augmented optimizers consistently outperform gradient descent in noisy environments. All code and pretrained weights are available to replicate the results or deploy the models at: https://github.com/lockwo/rl_qvc_opt.
Comparative Analysis for Quick Sort Algorithm under four Different Modes of Execution

This work presents a comparison for the performance of sequential sorting algorithms under four different modes of execution, the sequential processing mode, a conventional multi-threading implementation, multi-threading with OpenMP Library and finally parallel processing on a super computer. Quick Sort algorithm was selected to run the experiments performed by this effort and the algorithm was run using different arrays sizes and different number of processors. The results and findings were analyzed uncovering limitations as well as enhancement potentials of sequential sorting algorithms using parallelism.
Financial Risk Management and Financial Performance of International Authorisation Quoted Deposit Money Banks (DMBs) in Nigeria

Deposit money banks occupy vital position in every economic activity of a nation.This makes the banks to be proned to different aspects of business risk especially financial risk which could negatively affect the financial performance indicators of international authorized Nigerian Deposit Money Banks (DMBs).Hence, this study examined the influence of financial risk management on financial performance of international authorisation listed Nigerian DMB.The study employed expost facto research design and panel regression method of analysis covering the period of 2012-2019.The study concluded that financial risk management components affect financial performance measures-return on assets, return on equity as well as Tobin's Q of the international authorisation banks.It is therefore recommends that for Nigeria international authorisation quoted DMB to achieve enhanced and sustained financial performance indicators, bank customer should be well scrutinize, improvement in capital base and loans loss provision should be well maintained.