Reviewerly/exHarmony huggingface dataset:
Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.
Representation Learning in Deep RL via Discrete Information Bottleneck

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank

Unbiased learning to rank (ULTR) studies the problem of mitigating various biases from implicit user feedback data such as clicks, and has been receiving considerable attention recently. A popular ULTR approach for real-world applications uses a two-tower architecture, where click modeling is factorized into a relevance tower with regular input features, and a bias tower with bias-relevant inputs such as the position of a document. A successful factorization will allow the relevance tower to be exempt from biases. In this work, we identify a critical issue that existing ULTR methods ignored - the bias tower can be confounded with the relevance tower via the underlying true relevance. In particular, the positions were determined by the logging policy, i.e., the previous production model, which would possess relevance information. We give both theoretical analysis and empirical results to show the negative effects on relevance tower due to such a correlation. We then propose three methods to mitigate the negative confounding effects by better disentangling relevance and bias. Empirical results on both controlled public datasets and a large-scale industry dataset show the effectiveness of the proposed approaches.
On majorization for polynomials sharing a common interlacer

We give necessary and sufficient conditions for majorization of realrooted polynomials sharing a common interlacer by means of residues coming from fraction decomposition. We also introduce a motivated notion called strong majorization, and we show how it relates to standard majorization.
Induced Matching below Guarantees: Average Paves the Way for Fixed-Parameter Tractability

In this work, we study the Induced Matching problem: Given an undirected graph $G$ and an integer $\ell$, is there an induced matching $M$ of size at least $\ell$? An edge subset $M$ is an induced matching in $G$ if $M$ is a matching such that there is no edge between two distinct edges of $M$. Our work looks into the parameterized complexity of Induced Matching with respect to "below guarantee" parameterizations. We consider the parameterization $u - \ell$ for an upper bound $u$ on the size of any induced matching. For instance, any induced matching is of size at most $n / 2$ where $n$ is the number of vertices, which gives us a parameter $n / 2 - \ell$. In fact, there is a straightforward $9^{n/2 - \ell} \cdot n^{O(1)}$-time algorithm for Induced Matching [Moser and Thilikos, J. Discrete Algorithms]. Motivated by this, we ask: Is Induced Matching FPT for a parameter smaller than $n / 2 - \ell$? In search for such parameters, we consider $MM(G) - \ell$ and $IS(G) - \ell$, where $MM(G)$ is the maximum matching size and $IS(G)$ is the maximum independent set size of $G$. We find that Induced Matching is presumably not FPT when parameterized by $MM(G) - \ell$ or $IS(G) - \ell$. In contrast to these intractability results, we find that taking the average of the two helps -- our main result is a branching algorithm that solves Induced Matching in $49^{(MM(G) + IS(G))/ 2 - \ell} \cdot n^{O(1)}$ time. Our algorithm makes use of the Gallai-Edmonds decomposition to find a structure to branch on.
Un/interactive Fish in Posthuman Ethical Design

In the posthuman era, what counts as ethical has dispersed and re-distributed to a nonhuman-centered network that includes nonhuman species and nonliving matter. This paper serves as a resistance and a possible way out against traditional human-pet ownership in therapeutic robot development. The paper consists of research and project-based speculative design that aims to recalibrate human relationships with others through the public interactive project Not-my-fish. In the project Not-my-fish, the others include fish, digital fish, and computers. The project is developed in a healthcare facility setting to offer an alternative healthcare modality with un-interactive interaction and flow as core concepts. The overarching research question is: what would a human-nonhuman relationship look like through the lens of Taoist-posthumanism, and how do we design such a relationship? More practically, how to create a mutually beneficial interactive system with limited human-initiated interaction in human-computer interaction? Firstly, I draw on Posthumanism, Taoism, and Contractualism as theoretical frameworks to form the foundation regarding the human-fish relationship. Second, I analyze and transcribe the theories into the project Not-my-fish in terms of un-interactive interaction. Third, I apply the concept of flow as design guidelines and feature four aspects of the flow in the human-fish interaction: ecological flow (the physical surroundings of the human-fish interaction), sensory flow (the physical senses and perception of humans and fish), social flow (the shared and unique social behavior of humans and fish), and data flow (the system’s memory load and data lifecycle). Fourth, I discuss how the participating parties, such as human individuals, computer-simulated fish, and institutional partners, could mutually benefit from this partnership. The project Not-my-fish rearticulates the relationship between humans and fish and humans and computers. It views the human-fish relationships from posthuman and Taoist perspectives to detach the idea of human-nonhuman ownership and promote reciprocity and equilibrium. Nevertheless, some challenges are presented for human partners and design aspects regarding the transition from human-centered to posthuman interactive media. However, this paper demonstrates a practical way for human society to develop a posthuman design in public interactives and serve the public good at the same time.
Body Epistemes

This research concerns the relationships among space, story, and body in nonfictional Virtual Reality (VR) projects with the user as a first-person protagonist. What was once called immersive journalism (i.e., attempting to offer users physical experiences of factual journalism) has become increasingly film-oriented over the last five years with the further development of head-mounted displays for the consumer market. As such, projects often tell stories that relate to reality and allow protagonists to enter realistic narratives. Mel Slater has investigated VR projects since 2005, analyzing factors such as place illusion (PI) and plausibility illusion (Psi). In later writings, Slater expanded his research to include the body-oriented sense of embodiment (SoE) (Slater 2009; Slater et al. 2010; De La Pena et al. 2010; Kilteni et al. 2012). Since nonfictional VR mostly tells a story in the direction of the users, incorporating them into the story design, the effects of narration also should be considered. Domenic Arsénault (2005) noted three levels of narrative immersion in VR when dealing with narrative projects. Considering this, it is possible to develop a four-level matrix including place, plausibility, body perception, and narration. Starting in 2020, a Graduate student research project at the University of Leipzig was set up using this matrix to analyse four current examples for a small, non-representative study of twenty-four participants. Our findings demonstrate that space and story in first-person VR experiences are very important, while the installation of plausibility and coherence is less significant. The connecting factor of these two levels is one's own body. Users read, experience, and understand projects with their senses, perceptions, and cognition, making a discussion of an episteme of the body possible. Thus, stories from real life are not conveyed indirectly but experienced directly and personally.
Aural Authenticity and Reality in Soundscape of VR Documentary

The documentary concept is a 'notoriously slippery eel,' or even the slipperiest one in the history of cinema (Kahana, 2016). With the involvement of virtual reality in the production of documentaries, the dialectic between virtuality and authenticity makes this concept even more elusive. Does documentary exist in VR films? When the spaces and mise-en-scène of the documentary are all artificially created through computing software, can it still be considered an authentic form of a documentary? How can this sub-genre of documentary continue to exist as a kind of proclaimed ‘non-fiction’ when the film is based entirely on fictional visual input? This paper aims to put the discussion on visual realities aside and provides a perspective for understanding how real auditory characteristics are built up in virtual environments (VE) of virtual reality documentaries. We develop this argument in three parts. In the first part, we define a virtual reality documentary and distinguish it from other virtual reality films based on Bill Nichols’s analysis of the boundary between traditional documentary and other types of films. Then, we describe the design and implementation of the aural simulation systems with HRTF (Head-Related Transfer Function) in virtual reality documentary sound design to illustrate how it could reproduce realistic physical hearing for viewers. In the final part, we explore the concept of ‘soundscape’ as it applies to VR documentaries, attempting to show that the sense of authenticity in the audition is related to generating the genius loci (spirit of place) of the viewers based on the case analysis of Anne Frank House VR. Such a spirit of place is embedded in the hearing experience of those who experienced it.
Cryptocurrency generation system using blockchain

The article describes the Bitcoin cryptocurrency generation system.The use of blockchain technology and encryption algorithm SHA-256 in the Bitcoin generation system is considered.The aim of the work is to research the technologies and value of cryptocurrency.The article describes the structure of Bitcoin and blockchain -the technology that underlies this cryptocurrency.An analysis of existing problems with Bitcoin and its future is presented.Bitcoin is a cryptocurrency, meaning it is supported by a source code that uses highly complex algorithms to prevent unauthorized duplication or creation of Bitcoin units.The code's underlying principles, known as cryptography, are based on advanced mathematical and computer engineering principles.It is virtually impossible to break Bitcoin's source code and manipulate the currency's supply.There are few ways to get the Bitcoin: use the calculation power of the computers to solve the algorithms, use the services to trade the currency, purchase the cryptocurrency for real money, sell something for Bitcoins.Thanks to its decentralized technology blockchain, it could be used securely without any doubt that your payment has not proceeded.It uses blocks which contain all the information about the previous transactions.It helps identify the history of all the payments.But as everything it has its own drawbacks.Bitcoin is hard to mine nowadays.The award for mining a block decreases every time someone joins the mining process.In addition, it consumes a lot of electricity, which is not infinite as Bitcoin itself.So, despite, it's the most popular and used cryptocurrency it could be improved in the way of payment areas where it can be used, the way it's mined and the emission of the currency.
Cramming: Training a Language Model on a Single GPU in One Day

Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we get with a single GPU in just one day? We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for a single day on a single consumer GPU. Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario. We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings. Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting.
VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.
Two Modes of Nonmonotonic Consequence

This is the text of my speech at the Logica Universalis webinar, which took place on May 11, 2022. I discuss two ways to implement a semantic approach to nonmonotonic consequence relations in an arbitrary propositional language. For one particular language, we also discuss the proof-theoretic framework that we connect with this semantic approach.
Non-Gaussian state generation with time-gated photon detection

Non-Gaussian states of light, which are essential in fault-tolerant and universal optical quantum computation, are typically generated by a heralding scheme using photon detectors. Recently, it is theoretically shown that the large timing jitter of the photon detectors deteriorates the purity of the generated non-Gaussian states [T. Sonoyama, $\textit{et al}$., Phys. Rev. A $\textbf{105}$, 043714 (2022)]. In this study, we generate non-Gaussian states with Wigner negativity by time-gated photon detection. We use a fast optical switch for time gating to effectively improve the timing jitter of a photon-number-resolving detector based on transition edge sensor from 50 ns to 10 ns. As a result, we generate non-Gaussian states with Wigner negativity of $-0.011\pm 0.004$, which cannot be observed without the time-gated photon detection method. These results confirm the effect of the timing jitter on non-Gaussian state generation experimentally for the first time and provide the promising method of high-purity non-Gaussian state generation.
Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation

Automatic Speech Recognition (ASR) systems frequently use a search-based decoding strategy aiming to find the best attainable transcript by considering multiple candidates. One prominent speech recognition decoding heuristic is beam search, which seeks the transcript with the greatest likelihood computed using the predicted distribution. While showing substantial performance gains in various tasks, beam search loses some of its effectiveness when the predicted probabilities are highly confident, i.e., the predicted distribution is massed for a single or very few classes. We show that recently proposed Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally confident predictions that may hamper beam search from truly considering a diverse set of candidates. We perform a layer analysis to reveal and visualize how predictions evolve, and propose a decoding procedure that improves the performance of fine-tuned ASR models. Our proposed approach does not require further training beyond the original fine-tuning, nor additional model parameters. In fact, we find that our proposed method requires significantly less inference computation than current approaches. We propose aggregating the top M layers, potentially leveraging useful information encoded in intermediate layers, and relaxing model confidence. We demonstrate the effectiveness of our approach by conducting an empirical study on varying amounts of labeled resources and different model sizes, showing consistent improvements in particular when applied to low-resource scenarios.
A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://github.com/antifen/mitosis-nuclei-segmentation.
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods

Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear autoencoders or specific training regimes (leading to vanishing or diverging compression rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders trained in the challenging proportional regime in which the input dimension scales linearly with the size of the representation. Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods; their structure is also unveiled, thus leading to a concise description of the features obtained via training. For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders. Finally, while the results are proved for Gaussian data, numerical simulations on standard datasets display the universality of the theoretical predictions.
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization

To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.