Reviewerly/exHarmony huggingface dataset:
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain

Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. However, this approach is increasingly proven to be impractical owing to the substantial computational requirements associated with training such large language models. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a viable solution by selectively fine-tuning a small subset of additional parameters, significantly reducing the computational requirements for domain adaptation. In this study, we propose Clinical LLaMA-LoRA, a PEFT adapter layer built upon the open-sourced LLaMA model. Clinical LLaMA-LoRA is trained using clinical notes obtained from the MIMIC-IV database, thereby creating a specialised adapter designed for the clinical domain. Additionally, we propose a two-step PEFT framework which fuses Clinical LLaMA-LoRA with Downstream LLaMA-LoRA, another PEFT adapter specialised for downstream tasks. We evaluate this framework on multiple clinical outcome prediction datasets, comparing it to clinically trained language models. Our proposed framework achieves a state-of-the-art AUROC score averaged across all clinical downstream tasks. We observe substantial improvements of 6-9% AUROC score in the large-scale multilabel classification tasks, such as diagnoses and procedures classification.
Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing

Two hybrid quantum-classical reservoir computing models are presented to reproduce low-order statistical properties of a two-dimensional turbulent Rayleigh-B\'enard convection flow at a Rayleigh number Ra=1e+5 and a Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root mean square velocity and temperature and the turbulent convective heat flux. Both quantum algorithms differ by the arrangement of the circuit layers of the quantum reservoir, in particular the entanglement layers. The second of the two quantum circuit architectures, denoted as H2, enables a complete execution of the reservoir update inside the quantum circuit without the usage of external memory. Their performance is compared with that of a classical reservoir computing model. Therefore, all three models have to learn the nonlinear and chaotic dynamics of the turbulent flow at hand in a lower-dimensional latent data space which is spanned by the time-dependent expansion coefficients of the 16 most energetic Proper Orthogonal Decomposition (POD) modes. These training data are generated by a POD snapshot analysis from direct numerical simulations of the original turbulent flow. All reservoir computing models are operated in the reconstruction mode. We analyse different measures of the reconstruction error in dependence on the hyperparameters which are specific for the quantum cases or shared with the classical counterpart, such as the reservoir size and the leaking rate. We show that both quantum algorithms are able to reconstruct the essential statistical properties of the turbulent convection flow successfully with similar performance compared to the classical reservoir network. Most importantly, the quantum reservoirs are by a factor of 4 to 8 smaller in comparison to the classical case.
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions

Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining tasks. Although real-world signed relationships can be influenced by multiple latent factors, most existing methods often oversimplify the modeling of signed relationships by relying on social theories and treating them as simplistic factors. This limits their expressiveness and their ability to capture the diverse factors that shape these relationships. In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions. We adopt a disentangled framework that separates each embedding into distinct factors, allowing for capturing multiple latent factors. We also explore lightweight graph convolutions that focus solely on sign and direction, without depending on social theories. Additionally, we propose a decoder that effectively classifies an edge's sign by considering correlations between the factors. To further enhance disentanglement, we jointly train a self-supervised factor discriminator with our encoder and decoder. Throughout extensive experiments on real-world signed directed graphs, we show that DINES effectively learns disentangled node representations, and significantly outperforms its competitors in the sign prediction task.
Quantum Solutions to the Privacy vs. Utility Tradeoff

In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membership inference attacks on generative models. Our architecture can be used on top of any existing classical or quantum generative models. We argue that the use of quantum gates associated with unitary operators provides inherent advantages compared to standard Differential Privacy based techniques for establishing guaranteed security from all polynomial-time adversaries.
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search

Search spaces hallmark the advancement of Neural Architecture Search (NAS). Large and complex search spaces with versatile building operators and structures provide more opportunities to brew promising architectures, yet pose severe challenges on efficient exploration and exploitation. Subsequently, several search space shrinkage methods optimize by selecting a single sub-region that contains some well-performing networks. Small performance and efficiency gains are observed with these methods but such techniques leave room for significantly improved search performance and are ineffective at retaining architectural diversity. We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance. Our approach leverages locality, the relationship between structural and performance similarity, to efficiently extract many pockets of well-performing networks. We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6\% in ImageNet under mobile constraints, best-in-class Kendal-Tau, architectural diversity, and search space size.
Non-Hermitian Parent Hamiltonian from Generalized Quantum Covariance Matrix

Quantum inverse problem is defined as how to determine a local Hamiltonian from a single eigenstate? This question is valid not only in Hermitian system but also in non-Hermitian system. So far, most attempts are limited to Hermitian systems, while the possible non-Hermitian solution remains outstanding. In this work, we generalize the quantum covariance matrix method to the cases that are applicable to non-Hermitian systems, through which we are able to explicitly reconstruct the non-Hermitian parent Hamiltonian from an arbitrary pair of biorthogonal eigenstates. As concrete examples, we successfully apply our approach in spin chain with Lee-Yang singularity and a non-Hermitian interacting fermion model. Some generalization and further application of our approach are also discussed. Our work provides a systematical and efficient way to construct non-Hermitian Hamiltonian from a single pair of biorthogonal eigenstates and shed light on future exploration on non-Hermitian physics.
T-MARS: Improving Visual Representations by Circumventing Text Feature Learning

Large web-sourced multimodal datasets have powered a slew of new methods for learning general-purpose visual representations, advancing the state of the art in computer vision and revolutionizing zero- and few-shot recognition. One crucial decision facing practitioners is how, if at all, to curate these ever-larger datasets. For example, the creators of the LAION-5B dataset chose to retain only image-caption pairs whose CLIP similarity score exceeded a designated threshold. In this paper, we propose a new state-of-the-art data filtering approach motivated by our observation that nearly 40% of LAION's images contain text that overlaps significantly with the caption. Intuitively, such data could be wasteful as it incentivizes models to perform optical character recognition rather than learning visual features. However, naively removing all such data could also be wasteful, as it throws away images that contain visual features (in addition to overlapping text). Our simple and scalable approach, T-MARS (Text Masking and Re-Scoring), filters out only those pairs where the text dominates the remaining visual features -- by first masking out the text and then filtering out those with a low CLIP similarity score of the masked image. Experimentally, T-MARS outperforms the top-ranked method on the "medium scale" of DataComp (a data filtering benchmark) by a margin of 6.5% on ImageNet and 4.7% on VTAB. Additionally, our systematic evaluation on various data pool sizes from 2M to 64M shows that the accuracy gains enjoyed by T-MARS linearly increase as data and compute are scaled exponentially. Code is available at https://github.com/locuslab/T-MARS.
Categorified Path Calculus

Path calculus, or graphical linear algebra, is a string diagram calculus for the category of matrices over a base ring. It is the usual string diagram calculus for a symmetric monoidal category, where the monoidal product is the direct sum of matrices. We categorify this story to develop a surface diagram calculus for the bicategory of matrices over a base bimonoidal category. This yields a surface diagram calculus for any bimonoidal category by restricting to diagrams for 1x1 matrices. We show how additional structure on the base category, such as biproducts, duals and the dagger, adds structure to the resulting calculus. Applied to categorical quantum mechanics this yields a new graphical proof of the teleportation protocol.
DeepOnto: A Python Package for Ontology Engineering with Deep Learning

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).
TGRL: An Algorithm for Teacher Guided Reinforcement Learning

Learning from rewards (i.e., reinforcement learning or RL) and learning to imitate a teacher (i.e., teacher-student learning) are two established approaches for solving sequential decision-making problems. To combine the benefits of these different forms of learning, it is common to train a policy to maximize a combination of reinforcement and teacher-student learning objectives. However, without a principled method to balance these objectives, prior work used heuristics and problem-specific hyperparameter searches to balance the two objectives. We present a $\textit{principled}$ approach, along with an approximate implementation for $\textit{dynamically}$ and $\textit{automatically}$ balancing when to follow the teacher and when to use rewards. The main idea is to adjust the importance of teacher supervision by comparing the agent's performance to the counterfactual scenario of the agent learning without teacher supervision and only from rewards. If using teacher supervision improves performance, the importance of teacher supervision is increased and otherwise it is decreased. Our method, $\textit{Teacher Guided Reinforcement Learning}$ (TGRL), outperforms strong baselines across diverse domains without hyper-parameter tuning.
STRATEGI PENGAJARAN GURU DALAM MENINGKATKAN KEMAMPUAN MEMBACA SISWA KELAS IV SD N NIRMALA

Literacy is an important thing in students’ education in school this purpose of this research is to know strategies from teachers to improve the ability of the student to read will be held in SDN Nirmala targeting teachers and students in the fourth class. Qualitative research is showed teachers can improve their reading ability using the strategy and the correct method. Using the strategy of directed reading thinking activity (DRTA) applied by the fourth class teacher with the learning project model, the learning project model can improve the ability of proportion students to read. The obstacle factor in the process of student learning is generally located in the student’s characteristics and the student’s background does not support this since covid 19 pandemic along with learning loss in active learning.
Pemahaman dan Penanganan Kesulitan Membaca pada Anak-anak Kelas II: Studi Kasus di SD Negeri 54/I Bajubang

This study aims to describe the reading challenges faced by second-grade students at SD Negeri 54/I Bajubang and the contributions of teachers in addressing these challenges. A descriptive qualitative methodology is employed in this research. Educators and second-grade students from SD Negeri 54/I Bajubang participated in this study. Interviewing, observation, and documentation are some of the data collection techniques employed. The collected data are analyzed using the Miles and Huberman model, which involves data reduction, data presentation, and drawing conclusions. Based on the findings, the reading comprehension issues among some second-grade students at SD Negeri 54/I Bajubang reached 70%. Students who struggle with reading often face difficulties in letter recognition, reading meaningful and meaningless words, reading fluently and comprehending the text, and listening or comprehending auditory information.
PENGEMBANGAN E-MODUL MENGGUNAKAN FLIPBOOK PADA TEMA 9 SUBTEMA 1 BENDA TUNGGAL DAN CAMPURAN

This study aims to develop e-modules using flipbooks learners understands learning materials, increase student’ interest and learning motivation, then help teachers to be able to innovate in developing teaching materials in digital form like the e-module using a flipbook. this study uses the research and development method with the ADDIE approach. This research was conducted in five stages, namely the stages of needs analysis, design, development, implementation, and evaluation. The subjects of this study were VB class at SD Negeri Pajajaran consisting 28 students. The research was conducted in the even semester of the 2022/2023 school year. The results of this study indicate that the e-module using the flipbook that has been developed is very feasible. This is based on the percentage of media experts 97%, linguists experts 97%, material experts 96%, and student responses 92%. Based on this, it can be concluded that e-modules using flipbooks on the theme 9 sub-themes 1 single and mixed objects that are developed are very feasible to use during the learning activity process.
Meningkatan Kemampuan Berbicara Siswa melalui Storytelling pada Siswa Kelas II MI Al- Irsyad Kota Tangerang

Speaking skill is the ability to pronounce or pronounce an idea that exists in an individual which involves several aspects of pronunciation, vocabulary, and structure. This study aims to find out that there is a problem how to describe the improvement of speaking skills through storytelling in class II students of Mi Al-Irsyad, Tangerang City. The research method used is classroom action research (PTK) with data collection techniques of observation, tests, and interviews. The main objective of PTK is to solve real problems that occur in the classroom and increase the real activities of teachers in their professional development. The results of the study showed that there was an increase in speaking skills if the average score on student test results increased, namely individual absorption of at least 70 and classically 85% of students could speak. In addition, there was also a change in students' attitudes during the learning process which was marked by the activeness of students in expressing opinions, answering questions, the level of self-confidence, the use of appropriate language when telling stories, and responsibility in the learning process.
New Scale Mixture for Bayesian Adaptive Lasso Tobit Regression

Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as uniform density , mixing with standard exponential distribution based on a transformation of the mixture of uniform density and a particular gamma distribution formulation provided by Mallick &amp; Yi [2] They consider the new proposed hierarchical formulation model and prior distributions, as well as the full Conditional posterior distributions structural under non conditioning on σ2 which makes the uncertainty, of a unimodal full posterior, Conditioning on σ2 is important, because it guarantees a unimodal full posterior Park and Casella [3]. So, we can conclude that [1] proposed new hierarchical representation utilizing a Non- scale mixture distributions, which needs to deal with this problem . To address this problem we consider new hierarchical representation of the adaptive Bayesian lasso for Tobit model based on scale mixture of Uniform density, mixing with standard exponential distribution.
Calibration of a Structured Light Imaging System in Two-Layer Flat Refractive Geometry for Underwater Imaging

The development of a robust 3D imaging system for underwater applications is a crucial process in underwater imaging where the physical properties of the underwater environment make the implementation of such systems challenging. Calibration is an essential step in the application of such imaging systems and is performed to acquire the parameters of the image formation model and to enable 3D reconstruction. We present a novel calibration method for an underwater 3D imaging system comprising a pair of cameras, of a projector, and of a single glass interface that is shared between cameras and projector(s). The image formation model is based on the axial camera model. The proposed calibration uses a numerical optimization of a 3D cost function to determine all system parameters, thus avoiding the minimization of re-projection errors which require numerically solving a 12th order polynomial equation multiple times for each observed point. We also propose a novel stable approach to estimate the axis of the axial camera model. The proposed calibration was experimentally evaluated on four different glass interfaces, wherein several quantitative results were reported, including the re-projection error. The achieved mean angular error of the system’s axis was under 6∘, and the mean absolute errors for the reconstruction of a flat surface were 1.38 mm for normal glass interfaces and 2.82 mm for the laminated glass interface, which is more than sufficient for application.
VISUALISASI FENOMENA HARMONIS DAN CHAOS PADA GETARAN TERGANDENG BERBASIS KOMPUTASI NUMERIK RUNGE KUTTA

Research has been carried out on the visualization of harmonic and chaos phenomenont on coupled vibration physcal case using the Runge Kutta numerical computation method with the aim of applying the first to fourth order Runge Kutta computation method to obtain a second order differential equation solution on coupled vibration system, calculating the displacement value of objects using computation method Runge Kutta order first to fourth, obtained a graph of the displacement of objects againts time in case of coupled vibration for harmonic and chaos states at certain step width values and compare the convergence of the Runge Kutta method from first to fourth order with the special analytical method. The solution of coupled vibration equation which is classified as a second order differential equation was quite difficulted to solve analytically, so the Runge Kutta computation method was used to solve it as an alternative solution. The results of the research showed that the harmonic state of the system was obtained when the displacement graph showed the motion of each pendulum which was constant with the pendulum displacement position with respect to time in the form of a sinusoidal graph at a value of C1 = 40 N/m, C2 = 30 N/m, C = 10 N/m, C = 0 N/m and the chaotic state was represented by a graph of the displacement of the pendulum with respect to time with an irregular pattern. In this case, it was found that the fourth order Runge Kutta method converged faster than the first to third order Runge Kutta method with the best results obtained at a step width value of 0,001. The fourth order Runge Kutta method also has a smaller approximation average error value from first to third order Runge Kutta method was on the fourth order Runge Kutta method and the avarage error values are , and on the Runge Kutta method of first to third order.
POWER – A User Experience Research Model for Industrial Internet Design Practices

Industrial internet implementations are popular on a global scale. Different types of products, including both hardware and software, are included in industrial internet projects. Due to their complexity and diversity, user experience research for industrial internet products is difficult. To get insights for enhancing the industrial internet product experience, designers need an improved research method. In this paper, the POWER framework for user experience research in industrial internet design practices is promoted. POWER represents 5 factors in industrial internet design, which are people, object, workflow, environment and review. This framework emphasizes interventions of production work flow and user flow. Also, it promotes the importance of industry paradigms reviews to avoid common-sense mistakes. A showcase of how POWER framework helps designers to improve software user experience in car industry is demonstrated.