Reviewerly/exHarmony huggingface dataset:
A 2D synthesized image improves the 3D search for foveated visual systems.

Current medical imaging increasingly relies on 3D volumetric data making it difficult to thoroughly search all regions of the volume. In some applications (e.g., Digital Breast Tomosynthesis), the volumetric data is typically paired with a synthesized 2D image (2D-S) generated from the corresponding 3D volume. We investigate how this image pairing affects the search for spatially large and small signals. Observers searched for these signals in 3D volumes, 2D-S images, and while viewing both. We hypothesize that lower spatial acuity in the observers’ visual periphery hinders the search for the small signals in the 3D images. However, the inclusion of the 2D-S guides eye movements to suspicious locations, improving the observer’s ability to find the signals in 3D. Behavioral results show that the 2D-S, used as an adjunct to the volumetric data, improves the localization and detection of the small (but not large) signal compared to 3D alone. There is a concomitant reduction in search errors as well. To understand this process at a computational level, we implement a foveated search model (FSM) that executes human eye movements and then processes points in the image with varying spatial detail based on their eccentricity from fixations. The FSM predicts human performance for both signals and captures the reduction in search errors when the 2D-S supplements the 3D search. Our experimental and modeling results delineate the utility of 2D synthesized images in 3D search—limit the reliance on low- resolution peripheral processing by guiding attention to regions of interest, effectively reducing errors.
The Implementation of Hierarchical Linear Spline Regression Model to Maternal Mortality Rate Data in Indonesia

Hierarchical spline nonparametric regression analysis is one regression method to identify the correlation pattern among the variables with unidentified curves and nested data structure using a spline truncated regression approach on its function.On the maternal mortality rate, there is a nested data structure between the regional/ city level and provincial level.This study aimed to identify the correlation between the response variable and predictor variable from the maternal mortality rate data in 2020.There are two analyzed levels consisting of the city/ regional level (level-1) with 256 cities/ regencies and the provincial level (level-2).The level-1 predictor variable is total pregnant women taking blood booster supplements and total pregnant women attending K4 antenatal care and the level-2 predictor variable is poverty rates and school enrollment rates.A spline truncated approach has high flexibility and this study employs Maximum Likelihood as its estimator.The finding suggested that if a province has a lower poverty rate than 7.24%, maternal mortality rates tend to increase.If a province has lower enrolment rates than 67.8 %, the maternal mortality rate tends to decrease.If a regency or a city has fewer pregnant women taking blood booster supplements than 2258, maternal mortality rates tend to decrease.If a regency or a city has fewer pregnant women attending K4 antenatal care than 2258, the maternal mortality rate tends to decrease.
Teachers’ Teaching Strategies For Teaching Speaking Skills At Junior High School

A teaching strategy is defined as a set of activities carried out to achieve a learning goal. This study aims to analyse the strategies implemented by the teacher for eighth grade students at one of Junior High School in Pasuruan, East Java. Furthermore, this study looks into the teacher's motivations for using the strategies in teaching speaking skills. This research used qualitative research with a case study design. The instruments used in this research were observations and interviews. The results of this research show that the teachers implemented the teaching strategies proposed by Thornbury. Moreover, the teacher was known for applying other strategies such as reading aloud, repetition drills, and modelling cooperative learning as they aligned with the students’ competencies. These research findings indicate that the teacher successfully implemented her strategies in teaching speaking skills, which can be proven from the students’ responses that they are motivated to learn English, especially speaking skills.
Pengaruh Penggunaan Media Pop-Up Book Terhadap Peningkatan Minat Belajar Peserta Didik Kelas IV Sekolah Dasar

The purpose of this study was to determine the effect of the use of Pop-Up Book learning media on the learning interest of fourth-grade students at Primary School. This research method uses the Quantitative Pre-Experiment method with the One Group pretest and post-test design model. Which uses treatment and measurement of 1 class with a quantitative approach. The sample in this study was class IV which amounted to 19 students. The instrument in this study was observed to see student activities, questionnaires, and documentation. The analysis technique used is descriptive statistical analysis technique and inferential statistical analysis. Based on the results of the pretest and post-test data analysis, the results of the pretest calculation are included in the sufficient category with a value of 59% in the value range of 40% ≤ KP ≤ 60%. And for posttest calculations, the result of the calculation is at the value range of 80% ≤ KP ≤ 100% with an average value of 83% included in the very good category. Based on the results of observations obtained the overall percentage is 94% which is included in the very good category. In addition, this research is also proven by using the normality test with the help of SPSS, and the results of the significance value 0,200 &gt; 0,05, it can be concluded that the residual value is normally distributed. The results of the hypothesis test using the paired sample T-test with the help of SPSS with the provisions of Sig &lt; α is 0,000 &lt; 0,05 and t_count &gt; t-table is 11,649 &gt; 2,101 showed that there was a significant average difference between the pretest and posttest learning outcomes. Thus it can be said that H1 was received and H0 rejected, with this there is an effect of using Pop-Up Book media on increasing student interest in learning.
THE CONCENTRATION EFFECT OF K2SO4 ELECTROLITE SOLUTION ON GLOW DISCHARGE PLASMA ELECTROLYSIS

Plasma electrolysis is currently an environmentally friendly technology that is used as an alternative to nitrogen fixation, clean water treatment, batik waste degradation and the synthesis of other compounds. The success indicator of plasma electrolysis technology is seen from the formation of glow discharge. One of the factors that influence the formation of plasma in this method is the electrolyte solution. The electrolyte solution used in this study is K2SO4 because its conductivity causes electron jumps. This study aims to determine the effect of various concentrations of K2SO4 electrolyte solution on glow discharge plasma at the anode.In the plasma electrolysis reactor with stainless steel cathode and tungsten anode and 0.01 M electrolyte solution; 0.02 M; and 0.03 M K2SO4. The formation of glow discharge plasma occurs at the anode. Changes in concentration affect the critical voltage (VD) where the higher the concentration (0.03 M), glow discharge occurs at a lower voltage (600 V). This is due to the presence of high dielectric strength in the solution causing lower discharge strength to occur in solutions with lower electrolyte concentrations.Keywords: anode, plasma electrolysis, K2SO4 electrolyte, glow discharge
LITERATURE SURVEY- DOG INTRUSION DETECTION SYSTEM AND ALARM SYSTEM BASED ON THE SIZE OF THE DOG AND BREED DETECTION TO DETECT LOST PET DOGS IN OUR COLLEGE PREMISES

In our project, we implement a dog intrusion detection and breed classifier system to prevent the encounter with dogs in our college and find lost pet dogs in our college .We are planning to implement this by using a camera on a spot where the dogs are most likely to be seen in our college premises.When a dog comes into the Field of View (FOV) of the camera, the device scans the output image using machine learning as well as deep learning algorithms to detect the size and breed of the dog.A notification will be sent to the site mentioning the size of the dog so that students and faculty can avert the dog if it is not a small dog.The system is also implemented in such a way that it can be used to detect lost pet dogs in the college premises.As most of the pet dogs are of a specific breed, we can use the breed detection technology to detect the lost pet dogs.The breed of the lost pet dog must be fed into the system.If the breed of dog detected and the breed of the dog lost is same, the system will send notification to the owner of the lost oet dog with image detected to verify if it's really theirs.We use python programming language and main hardware components used for this project are cameras and the hardware used would be raspberry pi or orange pi.
Run-Based Semantics for RPQs

RPQs (regular path queries) are an important building block of most query languages for graph databases. They are generally evaluated under homomorphism semantics; in particular only the endpoints of the matched walks are returned. However, practical applications often need the full matched walks to compute aggregate values. In those cases, homomorphism semantics are not suitable since the number of matched walks can be infinite. Hence, graph-database engines adapt the semantics of RPQs, often neglecting theoretical red flags. For instance, the popular query language Cypher uses trail semantics, which ensures the result to be finite at the cost of making computational problems intractable. We propose a new kind of semantics for RPQs, including in particular simple-run and binding-trail semantics, as a candidate to reconcile theoretical considerations with practical aspirations. Both ensure the output to be finite in a way that is compatible with homomorphism semantics: projection on endpoints coincides with homomorphism semantics. Hence, testing the emptiness of result is tractable, and known methods readily apply. Moreover, simple-run and binding-trail semantics support bag semantics, and enumeration of the bag of results is tractable.
Extractive Explanations for Interpretable Text Ranking

Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of parameters, these (typically transformer-based) models are often non-interpretable in that ranking decisions can not be clearly attributed to specific parts of the input documents. In this paper we propose ranking models that are inherently interpretable by generating explanations as a by-product of the prediction decision. We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document. Thereafter, we solely use the explanation or selection to make the prediction, making explanations first-class citizens in the ranking process. Technically, we treat sentence selection as a latent variable trained jointly with the ranker from the final output. To that end, we propose an end-to-end training technique for Select-and-Rank models utilizing reparameterizable subset sampling using the Gumbel-max trick. We conduct extensive experiments to demonstrate that our approach is competitive to state-of-the-art methods. Our approach is broadly applicable to numerous ranking tasks and furthers the goal of building models that are interpretable by design. Finally, we present real-world applications that benefit from our sentence selection method.
EURO: ESPnet Unsupervised ASR Open-source Toolkit

This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the state-of-the-art UASR learning method introduced by the Wav2vec-U, originally implemented at FAIRSEQ, which leverages self-supervised speech representations and adversarial training. In addition to wav2vec2, EURO extends the functionality and promotes reproducibility for UASR tasks by integrating S3PRL and k2, resulting in flexible frontends from 27 self-supervised models and various graph-based decoding strategies. EURO is implemented in ESPnet and follows its unified pipeline to provide UASR recipes with a complete setup. This improves the pipeline's efficiency and allows EURO to be easily applied to existing datasets in ESPnet. Extensive experiments on three mainstream self-supervised models demonstrate the toolkit's effectiveness and achieve state-of-the-art UASR performance on TIMIT and LibriSpeech datasets. EURO will be publicly available at https://github.com/espnet/espnet, aiming to promote this exciting and emerging research area based on UASR through open-source activity.
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals

We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily. The PiggyBack supports the full stack of visual question answering tasks, specifically data processing, model fine-tuning, and result visualisation. We integrate visual-language models, pretrained by HuggingFace, an open-source API platform of deep learning technologies; however, it cannot be runnable without programming skills or deep learning understanding. Hence, our PiggyBack supports an easy-to-use browser-based user interface with several deep learning visual language pretrained models for general users and domain experts. The PiggyBack includes the following benefits: Free availability under the MIT License, Portability due to web-based and thus runs on almost any platform, A comprehensive data creation and processing technique, and ease of use on deep learning-based visual language pretrained models. The demo video is available on YouTube and can be found at https://youtu.be/iz44RZ1lF4s.
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test

Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.
Non-Deterministic Approximation Fixpoint Theory and Its Application in Disjunctive Logic Programming

Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to dealing with non-deterministic constructs that allow to handle indefinite information, represented e.g. by disjunctive formulas. This is done by generalizing the main constructions and corresponding results of AFT to non-deterministic operators, whose ranges are sets of elements rather than single elements. The applicability and usefulness of this generalization is illustrated in the context of disjunctive logic programming.
Model-Free Forecasting of Partially Observable Spatiotemporally Chaotic Systems

Reservoir computing is a powerful tool for forecasting turbulence because its simple architecture has the computational efficiency to handle large systems. Its implementation, however, often requires full state-vector measurements and knowledge of the system nonlinearities. We use nonlinear projector functions to expand the system measurements to a high dimensional space and then feed them to a reservoir to obtain forecasts. We demonstrate the application of such reservoir computing networks on spatiotemporally chaotic systems, which model several features of turbulence. We show that using radial basis functions as nonlinear projectors enables complex system nonlinearities to be captured robustly even with only partial observations and without knowing the governing equations. Finally, we show that when measurements are sparse or incomplete and noisy, such that even the governing equations become inaccurate, our networks can still produce reasonably accurate forecasts, thus paving the way towards model-free forecasting of practical turbulent systems.
Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models

In this work, we present some recommendations on the evaluation of state-of-the-art generative models for constrained generation tasks. The progress on generative models has been rapid in recent years. These large-scale models have had three impacts: firstly, the fluency of generation in both language and vision modalities has rendered common average-case evaluation metrics much less useful in diagnosing system errors. Secondly, the same substrate models now form the basis of a number of applications, driven both by the utility of their representations as well as phenomena such as in-context learning, which raise the abstraction level of interacting with such models. Thirdly, the user expectations around these models and their feted public releases have made the technical challenge of out of domain generalization much less excusable in practice. Subsequently, our evaluation methodologies haven't adapted to these changes. More concretely, while the associated utility and methods of interacting with generative models have expanded, a similar expansion has not been observed in their evaluation practices. In this paper, we argue that the scale of generative models could be exploited to raise the abstraction level at which evaluation itself is conducted and provide recommendations for the same. Our recommendations are based on leveraging specifications as a powerful instrument to evaluate generation quality and are readily applicable to a variety of tasks.
Knowledge-augmented Deep Learning and Its Applications: A Survey

Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack of interpretability. Various prior knowledge often exists in the target domain and their use can alleviate the deficiencies with deep learning. To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as knowledge-augmented deep learning (KADL). In this survey, we define the concept of KADL, and introduce its three major tasks, i.e., knowledge identification, knowledge representation, and knowledge integration. Different from existing surveys that are focused on a specific type of knowledge, we provide a broad and complete taxonomy of domain knowledge and its representations. Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning. The thorough and critical reviews of numerous papers help not only understand current progresses but also identify future directions for the research on knowledge-augmented deep learning.
Task-Specific Embeddings for Ante-Hoc Explainable Text Classification

Current state-of-the-art approaches to text classification typically leverage BERT-style Transformer models with a softmax classifier, jointly fine-tuned to predict class labels of a target task. In this paper, we instead propose an alternative training objective in which we learn task-specific embeddings of text: our proposed objective learns embeddings such that all texts that share the same target class label should be close together in the embedding space, while all others should be far apart. This allows us to replace the softmax classifier with a more interpretable k-nearest-neighbor classification approach. In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions. (2) This facilitates qualitative inspection of the training data, helping us to better understand the problem space and identify labelling quality issues. (3) The learned distances to some degree generalize to unseen classes, allowing us to incrementally add new classes without retraining the model. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy, thus pointing to practical applicability of our proposed approach.
Qubit-controlled directional edge states in waveguide QED

We propose an in-situ tunable chiral quantum system, composed of a quantum emitter coupled to a waveguide based on the Rice-Mele model (where we alternate both the on-site potentials and tunnel couplings between sites in the waveguide array). Specifically, we show that the chirality of photonic bound state, that emerges in the bandgap of the waveguide, depends only on the energy of the qubit; a parameter that is easy to tune in many artificial atoms. In contrast to previous proposals that have either shown imperfect chirality or fixed directionality, our waveguide QED scheme achieves both perfect chirality and the capability to switch the directionality on demand with just one tunable element in the device. We also show that our model is easy to implement in both state-of-the-art superconducting circuit and quantum dot architectures. The results show technological promise in creating long-range couplers between qubits while maintaining, in principle, zero crosstalk.
Learning Agile Paths from Optimal Control

Efficient motion planning algorithms are of central importance for deploying robots in the real world. Unfortunately, these algorithms often drastically reduce the dimensionality of the problem for the sake of feasibility, thereby foregoing optimal solutions. This limitation is most readily observed in agile robots, where the solution space can have multiple additional dimensions. Optimal control approaches partially solve this problem by finding optimal solutions without sacrificing the complexity of the environment, but do not meet the efficiency demands of real-world applications. This work proposes an approach to resolve these issues simultaneously by training a machine learning model on the outputs of an optimal control approach.