Reviewerly/exHarmony huggingface dataset:
Faster Learned Sparse Retrieval with Guided Traversal

Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency. Current learned sparse models learn the weights of terms in documents and, sometimes, queries; however, they exploit different vocabulary structures, document expansion techniques, and query expansion strategies, which can make them slower than traditional sparse models such as BM25. In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model's "guidance" to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. Our experiments show that our guided processing heuristic is able to boost the efficiency of the underlying learned sparse model by a factor of four without any measurable loss of effectiveness.
Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)

The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhancing the former's capabilities and battery lives. In a multiuser system, the joint allocation of communication-and-computation ($\text{C}^\text{2}$) resources (i.e., scheduling and bandwidth allocation) is made challenging by adopting efficient inference techniques, batching and early exiting, and further complicated by the heterogeneity in users' requirements on accuracy and latency. Batching groups multiple tasks into one batch for parallel processing to reduce time-consuming memory access and thereby boosts the throughput (i.e., completed task per second). On the other hand, early exiting allows a task to exit from a deep-neural network without traversing the whole network to support a tradeoff between accuracy and latency. In this work, we study optimal $\text{C}^\text{2}$ resource allocation with batching and early exiting, which is an NP-complete integer programming problem. A set of efficient algorithms are designed under the criterion of maximum throughput by tackling the challenge. Experimental results demonstrate that both optimal and sub-optimal $\text{C}^\text{2}$ resource allocation algorithms can leverage integrated batching and early exiting to double the inference throughput compared with conventional schemes.
Loss-based Sequential Learning for Active Domain Adaptation

Active domain adaptation (ADA) studies have mainly addressed query selection while following existing domain adaptation strategies. However, we argue that it is critical to consider not only query selection criteria but also domain adaptation strategies designed for ADA scenarios. This paper introduces sequential learning considering both domain type (source/target) or labelness (labeled/unlabeled). We first train our model only on labeled target samples obtained by loss-based query selection. When loss-based query selection is applied under domain shift, unuseful high-loss samples gradually increase, and the labeled-sample diversity becomes low. To solve these, we fully utilize pseudo labels of the unlabeled target domain by leveraging loss prediction. We further encourage pseudo labels to have low self-entropy and diverse class distributions. Our model significantly outperforms previous methods as well as baseline models in various benchmark datasets.
Bug Characteristics in Quantum Software Ecosystem

With the advance in quantum computing in recent years, quantum software becomes vital for exploring the full potential of quantum computing systems. Quantum programming is different from classical programming, for example, the state of a quantum program is probabilistic in nature, and a quantum computer is error-prone due to the instability of quantum mechanisms. Therefore, the characteristics of bugs in quantum software projects may be very different from that of classical software projects. This work aims to understand the characteristics of bugs in quantum software projects, in order to provide insights to help devise effective testing and debugging mechanisms. To achieve this goal, we conduct an empirical study on the bug reports of 125 quantum software projects. We observe that quantum software projects are more buggy than classical software projects and that quantum project bugs are more costly to fix than classical project bugs. We also identify the types of the bugs and the quantum programming components where they occurred. Our study shows that the bugs are spread across different components, but quantum-specific bugs particularly appear in the compiler, gate operation, and state preparation components. The three most occurring types of bugs are Program anomaly bugs, Configuration bugs, and Data type and structure bugs. Our study highlights some particularly challenging areas in quantum software development, such as the lack of scientific quantum computation libraries that implement comprehensive mathematical functions for quantum computing. Quantum developers also seek specialized data manipulation libraries for quantum software engineering like Numpy for quantum computing. Our findings also provide insights for future work to advance the quantum program development, testing, and debugging of quantum software, such as providing tooling support for debugging low-level circuits.
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia

Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease management. A verbal autopsy(VA) can provide information about a COD in areas without robust death registration systems. A VA consists of structured data, combining numeric and binary features, and unstructured data as part of an open-ended narrative text. This study assesses the performance of various machine learning approaches when analyzing both the structured and unstructured components of the VA report. The algorithms were trained and tested via cross-validation in the three settings of binary features, text features and a combination of binary and text features derived from VA reports from rural South Africa. The results obtained indicate narrative text features contain valuable information for determining COD and that a combination of binary and text features improves the automated COD classification task. Keywords: Diabetes Mellitus, Verbal Autopsy, Cause of Death, Machine Learning, Natural Language Processing
Reinforcement Teaching

Meta-learning strives to learn about and improve a student's machine learning algorithm. However, existing meta-learning methods either only work with differentiable algorithms or are hand-crafted to improve one specific component of an algorithm. We develop a unifying meta-learning framework, called Reinforcement Teaching, to improve the learning process of any algorithm. Under Reinforcement Teaching, a teaching policy is learned, through reinforcement, to improve a student's learning. To effectively learn such a teaching policy, we introduce a parametric-behavior embedder that learns a representation of the student's learnable parameters from its input/output behavior. Further, we use learning progress to shape the teacher's reward, allowing it to more quickly maximize the student's performance. To demonstrate the generality of Reinforcement Teaching, we conduct experiments where a teacher learns to significantly improve both reinforcement and supervised learning algorithms, outperforming hand-crafted heuristics and previously proposed parameter representations. Results show that Reinforcement Teaching is capable of not only unifying different meta-learning approaches, but also effectively leveraging existing tools from reinforcement learning research.
Quantum error mitigation by hidden inverses protocol in superconducting quantum devices

We present a method to improve the convergence of variational algorithms based on hidden inverses to mitigate coherent errors. In the context of error mitigation, this means replacing the on hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H$_{\rm 2}$ ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how hidden inverses \& randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high-level performance.
Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams

Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning perspective, challenges arise when the agent is prevented from leveraging large fully-annotated dataset, but rather the interactions with supervisory signals are sparsely distributed over space and time. This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that allows the agent to learn by observing what is moving in the attended locations. Spatio-temporal stochastic coherence along the attention trajectory, paired with a contrastive term, leads to an unsupervised learning criterion that naturally copes with the considered setting. Differently from most existing works, the learned representations are used in open-set class-incremental classification of each frame pixel, relying on few supervisions. Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream. Inheriting features from state-of-the art models is not as powerful as one might expect.
Creative rotation in conceptual art: Marcel Duchamp вЂ“ composer, John Cage вЂ“ artist

The subject of this study is the intersection points of music and fine art, demonstrated by the example of the creative interaction of the conceptual artist Marcel Duchamp and composer John Cage. The possibility of reformatting creativity from the visual to the sound field is considered as a role rotation, within which Duchamp appears as a composer, and Cage as an artist. An attempt is made to explain the motivation for such a choice. What is the meaning of this, and what do people mean when they choose this or that image for themselves, and create this or that image for themselves. How important it is to change the artistic image and creative image for the master. The methodology of the research consists in a comparative analysis of these concepts using already known works on this topic and personal observations of the author. In conceptualism, an idea is valued above the process of its materialization in a work of art, and therefore, the technique of execution is not a decisive factor. In such a creative paradigm, the artist's tools themselves become not so essential, which can be paints, a brush or other means of visual representation of the concept, as well as sheet music or a graphic score. The means of artistic expression - sounds or visual elements вЂ“ also turn out to be equivalent. All of them focus around the concept - the dominant point of any kind of creativity. This conclusion becomes the key for the conducted research in this article.
Graphical Residual Flows

Graphical flows add further structure to normalizing flows by encoding non-trivial variable dependencies. Previous graphical flow models have focused primarily on a single flow direction: the normalizing direction for density estimation, or the generative direction for inference. However, to use a single flow to perform tasks in both directions, the model must exhibit stable and efficient flow inversion. This work introduces graphical residual flows, a graphical flow based on invertible residual networks. Our approach to incorporating dependency information in the flow, means that we are able to calculate the Jacobian determinant of these flows exactly. Our experiments confirm that graphical residual flows provide stable and accurate inversion that is also more time-efficient than alternative flows with similar task performance. Furthermore, our model provides performance competitive with other graphical flows for both density estimation and inference tasks.
Metallogenic Belt Map of Northeast Asia

Metallogenic belt map of Northeast Asia at a scale 1:5 M is constructed on the base of geodynamic (terranes and overlap assamblages) map of a same scale. Metallogenic belts are outlined based on the analyses of geodynamics and metallogeny of each main structural units.
Efficacy of virtual education program to evaluate the knowledge of young mothers in Developmental Milestones

Efficacy of Virtual Education Program to evaluate the Knowledge of Young Mothers in Developmental Milestones. -A survey in Delhi/NCR. Objective: To evaluate the effectiveness of virtual education program to assess knowledge of young mothers in identifying developmental milestones. Design: A Cross section study was performed on young mothers via online mode of (pre-posttest). Outcome measures: Google form. Population: Young mothers within Delhi NCR, with no more than two children, and a mean age of 20-45 years were included. Backgroud and Purpose: In this study online session have given to assess the knowledge of young mothers in identifying developmental milestones. The purpose of this study was (1) to evaluate the efficacy of online education program on knowledge of young mothers to identifying developmental milestones, (2) To evaluate the outcomes of pre-test and posttest. Subjects. The participants were 100 young mothers from Delhi NCR. Methods. Self-structured questionnaire on knowledge regarding developmental milestones was used in this study, the questionnaire consists of 12 multiple choice answer questions in both the pre-test and post-test, with a video demonstrating the chronological progression of each gross motor milestone ranging from first month of life to twelve months respectfully. Each mother participated in the questionnaire.
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On the method of bibliographic reconstruction of lost library collections (case study of Orenburg Theological Seminary)

The author discusses preservation of documented heritage through reconstruction of library collections. She refers to the experience of Orenburg Theological Seminary Library which collections were lost in 1919. While it is possible to reconstruct library collections and archives, reconstruction methods are much more well-developed and applied to lost archives. However in librarianship, the methods are applied primarily in practice rather than developed as research methods. This study is intended to substantiate the efficiency of the method of document collections reconstruction in reviving lost library collections. In all times, the books that do not meet certain criteria, e. g. ideological, have been annihilated physically, or removed and transferred to special depositories. It is most unlikely that these books can be detected and brought back to the libraries. The method of archival reconstruction is used to recreate descriptive model of lost library. The specific tools are used for this purpose; therefore, this method can be called library collection bibliographic reconstruction when the inventory or catalog comprising both preserved and irrevocably lost publications is conducted. This method was used to reconstruct the lost collection of Orenburg Theological Seminary Library
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks

We study the implicit bias of stochastic gradient descent to favor low-depth solutions when training deep neural networks. Recent results in the literature suggest that penultimate layer representations learned by a classifier over multiple classes exhibit a clustering property, called neural collapse. First, we empirically show that neural collapse generally strengthens when increasing the number of layers. In addition, we demonstrate that neural collapse extends beyond the penultimate layer and emerges in intermediate layers as well, making the higher layers essentially redundant. We characterize a notion of effective depth which measures the minimal layer that enjoys neural collapse. In this regard, we hypothesize and empirically show that gradient descent implicitly selects neural networks of small effective depths. Finally, we theoretically and empirically show that the effective depth of a trained neural network monotonically increases when training with extended portions of random labels and connecting it with generalization.
Factors controlling entrapment of platinum-group minerals in chromite, Troodos, Cyprus

Chromite from the Troodos massif, Cyprus, contains ubiquitous inclusions of platinum-group minerals (PGM), 90%, of which are laurite of up to 20µm in diameter. Determination of iron valence in chromite by microprobe suggests that PGM’s are preferentially trapped in zones (sectors) of increased Fe3+/Fe2+ ratios. Raman spectra confirm this observation indicating in such zones ferritchromite peak at 726 cm-1, and trace laurite bond at 355.8 cm-1. Atomic force microscope (AMF) studies show that chromite zones are 3-4µm wide. Zonation is disturbed around PGM’s inclusions. Microareas of chromite surrounding PGM inclusions are slightly enriched in Mg and depleted in Cr.
Analysis of atmospheric effects on the continuous variable quantum key distribution

Atmospheric effects have significant influence on the performance of a free-space optical continuous variable quantum key distribution (CVQKD) system. In this paper, we investigate how the transmittance, excess noise and interruption probability caused by atmospheric effects affect the secret-key rate (SKR) of the CVQKD. Three signal wavelengths, two weather conditions, two detection schemes, and two types of attacks are considered in our investigation. An expression aims at calculating the interruption probability is proposed based on the Kolmogorov spectrum model. The results show that a signal using long working wavelength can propagate much further than that of using short wavelength. Moreover, as the wavelength increases, the influence of interruption probability on the SKR becomes more significant, especially within a certain transmission distance. Therefore, interruption probability must be considered for CVQKD by using long-signal wavelengths. Furthermore, different detection schemes used by the receiver will result in different transmission distances when subjected to individual attacks and collective attacks, respectively.
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators

Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allows for the exploration of accelerator designs and configuration space. However, preparing models for evaluation and exploring configuration space in STONNE is a manual developer-timeconsuming process, which is a barrier for research. This paper introduces Bifrost, an end-to-end framework for the evaluation and optimization of reconfigurable DNN inference accelerators. Bifrost operates as a frontend for STONNE and leverages the TVM deep learning compiler stack to parse models and automate offloading of accelerated computations. We discuss Bifrost's advantages over STONNE and other tools, and evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost introduces a module leveraging AutoTVM to efficiently explore accelerator designs and dataflow mapping space to optimize performance. This is demonstrated by tuning the MAERI architecture and generating efficient dataflow mappings for AlexNet, obtaining an average speedup of $50\times$ for the convolutional layers and $11\times$ for the fully connected layers. Our code is available at www.github.com/gicLAB/bifrost.
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning (including Transformers) for several medical applications. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
From Hyperbolic Geometry Back to Word Embeddings

We choose random points in the hyperbolic disc and claim that these points are already word representations. However, it is yet to be uncovered which point corresponds to which word of the human language of interest. This correspondence can be approximately established using a pointwise mutual information between words and recent alignment techniques.