Reviewerly/exHarmony huggingface dataset:
The Crucial Role of Normalization in Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) is a recently proposed gradient-based optimizer (Foret et al., ICLR 2021) that greatly improves the prediction performance of deep neural networks. Consequently, there has been a surge of interest in explaining its empirical success. We focus, in particular, on understanding the role played by normalization, a key component of the SAM updates. We theoretically and empirically study the effect of normalization in SAM for both convex and non-convex functions, revealing two key roles played by normalization: i) it helps in stabilizing the algorithm; and ii) it enables the algorithm to drift along a continuum (manifold) of minima -- a property identified by recent theoretical works that is the key to better performance. We further argue that these two properties of normalization make SAM robust against the choice of hyper-parameters, supporting the practicality of SAM. Our conclusions are backed by various experiments.
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution

Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are fluctuating at every time of sampling, especially for samplers with few resampled steps. This inherent randomness of diffusion models results in ineffectiveness and instability, making it challenging for users to guarantee the quality of SR results. However, our work takes this randomness as an opportunity: fully analyzing and leveraging it leads to the construction of an effective plug-and-play sampling method that owns the potential to benefit a series of diffusion-based SR methods. More in detail, we propose to steadily sample high-quality SR images from pre-trained diffusion-based SR models by solving diffusion ordinary differential equations (diffusion ODEs) with optimal boundary conditions (BCs) and analyze the characteristics between the choices of BCs and their corresponding SR results. Our analysis shows the route to obtain an approximately optimal BC via an efficient exploration in the whole space. The quality of SR results sampled by the proposed method with fewer steps outperforms the quality of results sampled by current methods with randomness from the same pre-trained diffusion-based SR model, which means that our sampling method "boosts" current diffusion-based SR models without any additional training.
Bifibrations of polycategories and classical multiplicative linear logic

In this thesis, we develop the theory of bifibrations of polycategories. We start by studying how to express certain categorical structures as universal properties by generalising the shape of morphism. We call this phenomenon representability and look at different variations, namely the correspondence between representable multicategories and monoidal categories, birepresentable polycategories and $\ast$-autonomous categories, and representable virtual double categories and double categories. We then move to introduce (bi)fibrations for these structures. We show that it generalises representability in the sense that these structures are (bi)representable when they are (bi)fibred over the terminal one. We show how to use this theory to lift models of logic to more refined ones. In particular, we illustrate it by lifting the compact closed structure of the category of finite dimensional vector spaces and linear maps to the (non-compact) $\ast$-autonomous structure of the category of finite dimensional Banach spaces and contractive maps by passing to their respective polycategories. We also give an operational reading of this example, where polylinear maps correspond to operations between systems that can act on their inputs and whose outputs can be measured/probed and where norms correspond to properties of the systems that are preserved by the operations. Finally, we recall the B\'enabou-Grothendieck correspondence linking fibrations to indexed categories. We show how the B-G construction can be defined as a pullback of virtual double categories and we make use of fibrational properties of vdcs to get properties of this pullback. Then we provide a polycategorical version of the B-G correspondence.
SummIt: Iterative Text Summarization via ChatGPT

Text summarization systems have made significant progress in recent years, but typically generate summaries in one single step. However, the one-shot summarization setting is sometimes inadequate, as the generated summary may contain hallucinations or overlook essential details related to the reader's interests. This paper addresses this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, resembling humans' iterative process when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We automatically evaluate the performance of our framework on three benchmark summarization datasets. We also conduct a human evaluation to validate the effectiveness of the iterative refinements and identify a potential issue of over-correction.
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing

The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers. Yet, most studies are limited to coarse-grained tags and high quality written content; while we know little about their influence when it comes to models in production that face lexical errors. We expand these setups and design an adversarial attack to verify if the use of morphological information by parsers: (i) contributes to error propagation or (ii) if on the other hand it can play a role to correct mistakes that word-only neural parsers make. The results on 14 diverse UD treebanks show that under such attacks, for transition- and graph-based models their use contributes to degrade the performance even faster, while for the (lower-performing) sequence labeling parsers they are helpful. We also show that if morphological tags were utopically robust against lexical perturbations, they would be able to correct parsing mistakes.
Inverse Preference Learning: Preference-based RL without a Reward Function

Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods na\"ively combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve performance and query complexity by using larger and more complex reward architectures such as transformers. Instead of using highly complex architectures, we develop a new and parameter-efficient algorithm, Inverse Preference Learning (IPL), specifically designed for learning from offline preference data. Our key insight is that for a fixed policy, the $Q$-function encodes all information about the reward function, effectively making them interchangeable. Using this insight, we completely eliminate the need for a learned reward function. Our resulting algorithm is simpler and more parameter-efficient. Across a suite of continuous control and robotics benchmarks, IPL attains competitive performance compared to more complex approaches that leverage transformer-based and non-Markovian reward functions while having fewer algorithmic hyperparameters and learned network parameters. Our code is publicly released.
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models

Recently, growing interest has been aroused in extending the multimodal capability of large language models (LLMs), e.g., vision-language (VL) learning, which is regarded as the next milestone of artificial general intelligence. However, existing solutions are prohibitively expensive, which not only need to optimize excessive parameters, but also require another large-scale pre-training before VL instruction tuning. In this paper, we propose a novel and affordable solution for the effective VL adaption of LLMs, called Mixture-of-Modality Adaptation (MMA). Instead of using large neural networks to connect the image encoder and LLM, MMA adopts lightweight modules, i.e., adapters, to bridge the gap between LLMs and VL tasks, which also enables the joint optimization of the image and language models. Meanwhile, MMA is also equipped with a routing algorithm to help LLMs achieve an automatic shift between single- and multi-modal instructions without compromising their ability of natural language understanding. To validate MMA, we apply it to a recent LLM called LLaMA and term this formed large vision-language instructed model as LaVIN. To validate MMA and LaVIN, we conduct extensive experiments under two setups, namely multimodal science question answering and multimodal dialogue. The experimental results not only demonstrate the competitive performance and the superior training efficiency of LaVIN than existing multimodal LLMs, but also confirm its great potential as a general-purpose chatbot. More importantly, the actual expenditure of LaVIN is extremely cheap, e.g., only 1.4 training hours with 3.8M trainable parameters, greatly confirming the effectiveness of MMA. Our project is released at https://luogen1996.github.io/lavin.
Contrastive Learning of Sentence Embeddings from Scratch

Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences in an unsupervised manner. However, even in the case of unlabeled data, their acquisition presents challenges in certain domains due to various reasons. To address these issues, we present SynCSE, a contrastive learning framework that trains sentence embeddings with synthesized data. Specifically, we explore utilizing large language models to synthesize the required data samples for contrastive learning, including (1) producing positive and negative annotations given unlabeled sentences (SynCSE-partial), and (2) generating sentences along with their corresponding annotations from scratch (SynCSE-scratch). Experimental results on sentence similarity and reranking tasks indicate that both SynCSE-partial and SynCSE-scratch greatly outperform unsupervised baselines, and SynCSE-partial even achieves comparable performance to the supervised models in most settings.
SlipCover: Near Zero-Overhead Code Coverage for Python

Coverage analysis is widely used but can suffer from high overhead. This overhead is especially acute in the context of Python, which is already notoriously slow (a recent study observes a roughly 30x slowdown vs. native code). We find that the state-of-the-art coverage tool for Python, coverage.py, introduces a median overhead of 180% with the standard Python interpreter. Slowdowns are even more extreme when using PyPy, a JIT-compiled Python implementation, with coverage.py imposing a median overhead of 1,300%. This performance degradation reduces the utility of coverage analysis in most use cases, including testing and fuzzing, and precludes its use in deployment. This paper presents SlipCover, a novel, near-zero overhead coverage analyzer for Python. SlipCover works without modifications to either the Python interpreter or PyPy. It first processes a program's AST to accurately identify all branches and lines. SlipCover then dynamically rewrites Python bytecodes to add lightweight instrumentation to each identified branch and line. At run time, SlipCover periodically de-instruments already-covered lines and branches. The result is extremely low overheads -- a median of just 5% -- making SlipCover suitable for use in deployment. We show its efficiency can translate to significant increases in the speed of coverage-based clients. As a proof of concept, we integrate SlipCover into TPBT, a targeted property-based testing system, and observe a 22x speedup.
Generative AI for Bayesian Computation

Bayesian Generative AI (BayesGen-AI) methods are developed and applied to Bayesian computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the parameter of interest as a mapping (a.k.a. deep learner) from a large simulated dataset. This provides a generator that we can evaluate at the observed data and provide draws from the posterior distribution. This method applies to all forms of Bayesian inference including parametric models, likelihood-free models, prediction and maximum expected utility problems. Bayesian computation is then equivalent to high dimensional non-parametric regression. Bayes Gen-AI main advantage is that it is density-free and therefore provides an alternative to Markov Chain Monte Carlo. It has a number of advantages over vanilla generative adversarial networks (GAN) and approximate Bayesian computation (ABC) methods due to the fact that the generator is simpler to learn than a GAN architecture and is more flexible than kernel smoothing implicit in ABC methods. Design of the Network Architecture requires careful selection of features (a.k.a. dimension reduction) and nonlinear architecture for inference. As a generic architecture, we propose a deep quantile neural network and a uniform base distribution at which to evaluate the generator. To illustrate our methodology, we provide two real data examples, the first in traffic flow prediction and the second in building a surrogate for satellite drag data-set. Finally, we conclude with directions for future research.
Empirical Challenge for NC Theory

Horn-satisfiability or Horn-SAT is the problem of deciding whether a satisfying assignment exists for a Horn formula, a conjunction of clauses each with at most one positive literal (also known as Horn clauses). It is a well-known P-complete problem, which implies that unless P = NC, it is a hard problem to parallelize. In this paper, we empirically show that, under a known simple random model for generating the Horn formula, the ratio of hard-to-parallelize instances (closer to the worst-case behavior) is infinitesimally small. We show that the depth of a parallel algorithm for Horn-SAT is polylogarithmic on average, for almost all instances, while keeping the work linear. This challenges theoreticians and programmers to look beyond worst-case analysis and come up with practical algorithms coupled with respective performance guarantees.
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE TEBAK KATA DAN KELILING BERKELOMPOK TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN IPS DI KELAS VIII SMP NEGERI 2 KOTA JAMBI

AbstractThis study aims to determine the learning outcomes of students who are taught using a cooperative learning model type Guess the word and group around in social studies subjects in class VIII SMP Negeri 2 Jambi City and a comparison of the cooperative learning model type Guess the Word and Go Around Groups on student learning outcomes in the subject. Social Studies lessons in Class VIII SMP Negeri 2 Jambi City. The type of research conducted is a quasi-experiment. The guessing type cooperative learning model for class VII C (control) and the group traveling type cooperative learning model for class VIII B (experimental). Sampling in this study used purposive sampling technique, so that 38 students of class VIII C were selected as the control class and 32 students of class VIII B as the experimental class. Data collection techniques are carried out by distributing questions to research respondents and documentation. The data analysis technique used the t-test. The results showed that the average value of posttest learning outcomes for class VIII B (experimental class) was 10,063 and the average value for learning outcomes for social studies subjects in the posttest stage in class VIII C was 8.079. Based on these results, the average value of learning outcomes for social studies subjects at the posttest stage in class VIII B is higher than class VIII C. The results of the t-test show a t-count value of 8.480 and a significance value of 0.000 &lt;0.05, so H0 is rejected. and Hadi Accept. Based on these results, the cooperative learning model type Guess the Word and Go Around in Groups has an effect on student learning outcomes in social studies subjects in Class VIII SMP Negeri 2 Jambi City.Keywords: Cooperative Learning Model Type Guess the Word and Mobile Group, Learning Outcomes
PENGARUH KEMAMPUAN AWAL SISWA DAN PEMBELAJARAN ONLINE TERHADAP HASIL BELAJAR SISWA KELAS VII DI SMP NEGERI 2 KOTA JAMBI

AbstractThis study aims to determine (1) the description of students' initial abilities, online learning and learning outcomes of class VII students at SMP Negeri 2 Jambi City. (2) The influence of students' initial abilities and learning outcomes of class VII students at SMP Negeri 2 Jambi City. (3) The influence of online learning and student learning outcomes of class VII at SMP Negeri 2 Jambi City. (4) The influence of students' initial abilities and online learning on student learning outcomes at SMP Negeri 2 Jambi City. This research design uses quantitative research methods, with data collection techniques through observation, interviews, and questionnaires. Where the population of this research is 222 students with a sample of 143 students. The data processing techniques in this study are Instrument Validity Test, Reliability Test, Quantitative Descriptive Analysis, Normality Test, Homogeneity Test, Multicollinearity Test, Heteroscedasticity Test, Simple Regression Test, Multiple Regression Test. The results showed that (1) the initial ability of students to obtain a TCR score of 80.95% with a good category. Meanwhile, the TCR value of online learning obtained a score of 80.25% in the good category. (2) There is an effect of students' initial ability on student learning outcomes of 2,546 with a significant value of 0.12. (3) There is an effect of online learning on student learning outcomes of 2,546 with a significant value of 0.15. (4) There is an effect of X1 and X2 on Y with a Ftable value of 4.523 with a significance of 0.12. Thus, the initial ability describes the readiness of students to accept the lessons that will be delivered by the teacher, and online learning is quite influential on the learning activities carried out between students and teachers. So even in the process of changing learning activities from offline to online, it still affects the learning activities of students even though it can determine the success of students in learning.Keywords: Initial Ability, Online Learning, Learning Outcomes
A study on how to improve users' perceived playfulness in and continuance intention with VR technology to paint in virtual natural landscapes

COVID-19 has adversely affected public access to public green spaces. As a means of interacting with nature, parks and green spaces are an important aspect of residents' daily lives. In this study, the focus is on new digital solutions, such as the experience of painting in virtual natural settings through virtual reality technologies. This study examines factors that affect the user's perceived playfulness and continuance intention to paint in a virtual environment. A total of 732 valid samples were collected through a questionnaire survey, and a theoretical model was developed through structural equation model by analyzing attitude, perceived behavioral control, behavioral intention, continuance intention, and perceived playfulness. Results demonstrate that perceived novelty and perceived sustainability increase the positive attitude of users toward VR painting functions, whereas perceived interactivity and aesthetics have no impact on it within VR painting context. As users use VR painting, they are more concerned about time and money than equipment compatibility. This makes resource facilitating conditions a more influential factor for perceived behavior control than technology facilitating conditions.
Education of Patriotism Values through Poetry : The Heroic Journey of Cut Nyak Dien

One of the famous heroes of Aceh for her role in resisting colonialism is Cut Nyak Dien, who joined the fighters in direct opposition to the colonizers. Cut Nyak Dien continued to lead the resistance against the Dutch despite being a woman. She was feared by the Dutch for her ability to ignite the spirit of resistance among the Acehnese people. The patriotism upheld by Cut Nyak Dien is characterized by a deep love and loyalty for her homeland. Moreover, she was willing to sacrifice everything. In education, students should possess a high level of enthusiasm for learning in order to achieve high academic achievements. This article adopts a descriptive-analytical approach based on data gathered through literature review from various written sources. Thus, aspirations can be realized. The spirit of patriotism encompasses a never-surrender attitude, willingness to sacrifice, and unwavering determination that will bring prosperity and progress in the pursuit of the nation's ideals.
CHARACTER-BASED ENGLISH EDUTAINMENT LEARNING USING POWTOON MEDIA FOR FOURTH-GRADE STUDENTS

The latest trend in learning English for elementary school students is the implementation of Edutainment in learning. Edutainment can create a learning atmosphere that is fun, interesting, creative, innovative, and meaningful. One of the edutainment media is character-based PowToon. This study aims to determine the difference in English comprehension between students taught with PowToon media and those taught without PowToon media. This research is a mixed type with a posttest-only control group design. The experimental class was treated with PowToon media, and the control class was taught without using PowToon media. The data required consists of data on learning outcomes and student responses to the learning process. Learning outcomes were collected by written and oral tests, while questionnaires and interviews collected data on student responses. The data obtained were analyzed using SPSS v25.0. The research results show that using PowToon in learning English positively affects students' understanding of English. The characteristics displayed by the experimental class students during the study were nationalist, religious, honest, diligent, disciplined, independent, responsible, and empathetic.
Pengaruh Model Pembelajaran ALC Berbantuan Media Quizizz Untuk Meningkatkan Literasi Numerasi Matematika Siswa Sekolah Dasar

The research aims to find out the significant effect of the ALC Model Assisted by Quizizz Media with Conventional Learning Models to Improve Mathematical Numeracy Literacy for IV elementary School Students. This research has quasi-experimental. The total population in this study was 161 students and research sample is 60 students with 30 experimental class students and 30 control class students. Based on the hypothesis that has been tested that, data analysis using independent t-test for N-Gain Score shows that df 30 + 30 - 2 = 58 with the value of tcount = 3,341 at a significant of 5%, the ttable value is obtained, namely 2,002. From the calculations results is known that tcount &gt; ttable is 3,341 &gt; 2,002 and Sig. (2-tailed) &lt; 0.05 (0.001 &lt; 0.05). This shows there is a significant difference in the effect of mathematical numeracy literacy between students who study with the ALC Model assisted by Quizizz media with the Conventional Model to improve mathematical numeracy literacy for 4 grade students of SD Cluster 3 Tejakula Village in the 2021/2022 academic year.