Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

SUMMARY To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models - PhenoBCBERT and PhenoGPT - for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes, due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models (LLMs) to automate the detection of phenotype terms, including those not in the current HPO. We compared these models to PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also showed strong performance in case studies on biomedical literature. We evaluated the strengths and weaknesses of BERT-based and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.
Perfect simulation from unbiased simulation

We show that any application of the technique of unbiased simulation becomes perfect simulation when coalescence of the two coupled Markov chains can be practically assured in advance. This happens when a fixed number of iterations is high enough that the probability of needing any more to achieve coalescence is negligible; we suggest a value of $10^{-20}$. This finding enormously increases the range of problems for which perfect simulation, which exactly follows the target distribution, can be implemented. We design a new algorithm to make practical use of the high number of iterations by producing extra perfect sample points with little extra computational effort, at a cost of a small, controllable amount of serial correlation within sample sets of about 20 points. Different sample sets remain completely independent. The algorithm includes maximal coupling for continuous processes, to bring together chains that are already close. We illustrate the methodology on a simple, two-state Markov chain and on standard normal distributions up to 20 dimensions. Our technical formulation involves a nonzero probability, which can be made arbitrarily small, that a single perfect sample point may have its place taken by a"string"of many points which are assigned weights, each equal to $\pm 1$, that sum to~$1$. A point with a weight of $-1$ is a"hole", which is an object that can be cancelled by an equivalent point that has the same value but opposite weight $+1$.
Discovering Local Binary Pattern Equation for Foreground Object Removal in Videos

Designing a novel Local Binary Pattern (LBP) process usually relies heavily on human experts' knowledge and experience in the area. Even experts are often left with tedious episodes of trial and error until they identify an optimal LBP for a particular dataset. To address this problem, we present a novel symbolic regression able to automatically discover LBP formulas to remove the moving parts of a scene by segmenting it into a background and a foreground. Experimental results conducted on real videos of outdoor urban scenes under various conditions show that the LBPs discovered by the proposed approach significantly outperform the previous state-of-the-art LBP descriptors both qualitatively and quantitatively. Our source code and data will be available online.
Impact of Orientation on the Bias of SRAM-Based PUFs

This paper investigates the impact of memory orientation on the bias pattern of SRAM-based PUFs. We designed and fabricated a 65nm CMOS chip that contains eleven SRAM macros that exercise different memory- and chip-level parameters. At the memory level, several parameters passed to the SRAM compiler are considered, including the number of addresses, the number of words, the aspect ratio, and the chosen bitcell. Chip-level decisions are considered during the floorplan, including the location and rotation of each SRAM macro in the testchip. In this study, we conduct a comprehensive analysis of different memory orientations and their effect on the biasing direction. Physical measurements performed on 50 fabricated chips revealed that specific memory orientations, namely R270 and MY90, exhibit a distinct negative biasing direction compared to other orientations. Importantly, this biasing direction remains consistent regardless of memory type, column mux ratio, memory size, or the utilization of SRAMs with different bitcells. Overall, this study highlights the significance of careful physical implementation and memory orientation selection in designing SRAM-based PUFs. Our findings can guide designers in the selection of SRAM memories with properties that make for better PUFs that potentially require less error correction effort to compensate for instability.
Solutions to the Fifth-Order KP II Equation Scatter

The fifth-order KP II equation $$ \partial_t u + \alpha \partial_x^3 u + \beta \partial_x^5 u + u \partial_x u + \partial_x^{-1} \partial_y^2u=0$$ ($\beta<0$, $\alpha>0$) is a nonlinear dispersive equation that models long dispersive waves in two space dimensions. We prove that solutions of the fifth-order KP II equation scatter to solutions of the corresponding linear equation $$ \partial_t v + \alpha\partial_x^3 v + \beta\partial_x^5 v + \partial_x^{-1} \partial_y^2 v = 0$$ for small data. Our proof uses builds on Hadac, Herr, and Koch's work (see ArXiv:0708.2011) on the third-order KP II equation.
Volterra Accentuated Non-Linear Dynamical Admittance (VANYA) to model Deforestation: An Exemplification from the Amazon Rainforest

Intelligent automation supports us against cyclones, droughts, and seismic events with recent technology advancements. Algorithmic learning has advanced fields like neuroscience, genetics, and human-computer interaction. Time-series data boosts progress. Challenges persist in adopting these approaches in traditional fields. Neural networks face comprehension and bias issues. AI's expansion across scientific areas is due to adaptable descriptors and combinatorial argumentation. This article focuses on modeling Forest loss using the VANYA Model, incorporating Prey Predator Dynamics. VANYA predicts forest cover, demonstrated on Amazon Rainforest data against other forecasters like Long Short-Term Memory, N-BEATS, RCN.
BigWavGAN: A Wave-To-Wave Generative Adversarial Network for Music Super-Resolution

Generally, Deep Neural Networks (DNNs) are expected to have high performance when their model size is large. However, large models failed to produce high-quality results commensurate with their scale in music Super-Resolution (SR). We attribute this to that DNNs cannot learn information commensurate with their size from standard mean square error losses. To unleash the potential of large DNN models in music SR, we propose BigWavGAN, which incorporates Demucs, a large-scale wave-to-wave model, with State-Of-The-Art (SOTA) discriminators and adversarial training strategies. Our discriminator consists of Multi-Scale Discriminator (MSD) and Multi-Resolution Discriminator (MRD). During inference, since only the generator is utilized, there are no additional parameters or computational resources required compared to the baseline model Demucs. Objective evaluation affirms the effectiveness of BigWavGAN in music SR. Subjective evaluations indicate that BigWavGAN can generate music with significantly high perceptual quality over the baseline model. Notably, BigWavGAN surpasses the SOTA music SR model in both simulated and real-world scenarios. Moreover, BigWavGAN represents its superior generalization ability to address out-of-distribution data. The conducted ablation study reveals the importance of our discriminators and training strategies. Samples are available on the demo page: https://mannmaruko.github. io/demopage/BigWavGAN/d.html.
Testing $Z$ boson rare decays $Z\to H_1 \gamma, A_1 \gamma$ with $(g-2)_{\mu}$, $\delta M_W$, and $BR(h_{\rm SM}\to Z\gamma)$ in the NMSSM

We study the rare decay process of $Z$ boson into photon, accompanied by a CP-even or CP-odd scalar. We present the analytical delineation of the processes through the model-independent parametrizations of the new physics couplings and, finally, consider the Next-to-Minimal Supersymmetric Standard Model to mark out the parameter space where the branching fraction can have the maximum value. As a part of the necessary phenomenological and experimental cross-checks, we aim to fit the anomalous magnetic moment of the muon and $W$ boson mass anomaly through the supersymmetric contributions. We also find that the decays $Z\to H_1 \gamma, A_1 \gamma$ can serve as an excellent complementary test to $BR(h_{\rm SM}\to Z\gamma)$. In fact, to facilitate future searches, we unveil a few benchmark points that additionally satisfy the deviation of $BR(h_{\rm SM}\to Z\gamma)$ from the SM value based on the recent measurements of ATLAS and CMS. Future proposals such as ILC, CEPC, and FCC-ee are anticipated to operate for multiple years, focusing on center-of-mass energy near the $Z$ pole. Consequently, these projects will be capable of conducting experiments at the Giga-$Z$ ($10^{9}$ of $Z$ bosons) and Tera-$Z$ ($10^{12}$ of $Z$ bosons) phases, which may probe the aforesaid rare decay processes, thus the model as well. These unconventional yet complementary searches offer different routes to explore the supersymmetric models with extended Higgs sectors like NMSSM.
A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed, China

This study presented a simulation-based two-stage interval-stochastic programming (STIP) model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing (ITSP). The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were provided by the statistical analysis of simulation outcomes. The developed STIP model was applied to a real case of water resources management planning in Kaidu-Konqi watershed, where three scenarios with different water resources management policies were analyzed. The results indicated that water shortage mainly occurred in agriculture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stockbreeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.
The BINGO Project. IX. Search for fast radio bursts – A forecast for the BINGO interferometry system

The B aryon Acoustic Oscillations (BAO) from I ntegrated N eutral G as O bservations ( BINGO ) radio telescope will use the neutral hydrogen emission line to map the Universe in the redshift range $0.127 z 0.449$, with the main goal of probing BAO. In addition, the instrument's optical design and hardware configuration support the search for f ast r adio b ursts ( FRBs In this work, we propose the use of a B INGO I nterferometry S ystem ( BIS ) including new auxiliary, smaller radio telescopes (hereafter outriggers). The interferometric approach makes it possible to pinpoint the FRB sources in the sky. We present the results of several BIS configurations combining BINGO horns with and without mirrors ($4$ m, $5$ m, and $6$ m) and five, seven, nine, or ten for single horns. We developed a new Python package, the FRBlip which generates mock catalogs of synthetic FRB and computes, based on a telescope model, the observed signal-to-noise ratio, which we use to numerically compute the detection rates of the telescopes and how many interferometry pairs of telescopes (baselines) can observe an FRB. The FRBs observed by more than one baseline are the ones whose location can be determined. We thus evaluated the performance of BIS regarding FRB localization. We found that BIS would be able to localize 23 FRBs yearly with single horn outriggers in the best configuration (using ten outriggers of 6-m mirrors), with redshift $z 0.96$. The full localization capability depends on the number and type of the outriggers. Wider beams are best for pinpointing FRB sources because potential candidates will be observed by more baselines, while narrow beams search deep in redshift. The BIS can be a powerful extension of the BINGO telescope, dedicated to observe hundreds of FRBs during Phase 1. Many of FRBs will be well localized with a single horn and a 6-m dish as outriggers.
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods

Stochastic gradient descent (SGD) and adaptive gradient methods, such as Adam and RMSProp, have been widely used in training deep neural networks. We empirically show that while the difference between the standard generalization performance of models trained using these methods is small, those trained using SGD exhibit far greater robustness under input perturbations. Notably, our investigation demonstrates the presence of irrelevant frequencies in natural datasets, where alterations do not affect models' generalization performance. However, models trained with adaptive methods show sensitivity to these changes, suggesting that their use of irrelevant frequencies can lead to solutions sensitive to perturbations. To better understand this difference, we study the learning dynamics of gradient descent (GD) and sign gradient descent (signGD) on a synthetic dataset that mirrors natural signals. With a three-dimensional input space, the models optimized with GD and signGD have standard risks close to zero but vary in their adversarial risks. Our result shows that linear models' robustness to $\ell_2$-norm bounded changes is inversely proportional to the model parameters' weight norm: a smaller weight norm implies better robustness. In the context of deep learning, our experiments show that SGD-trained neural networks have smaller Lipschitz constants, explaining the better robustness to input perturbations than those trained with adaptive gradient methods.
StairNetV3: Depth-aware Stair Modeling using Deep Learning

Vision-based stair perception can help autonomous mobile robots deal with the challenge of climbing stairs, especially in unfamiliar environments. To address the problem that current monocular vision methods are difficult to model stairs accurately without depth information, this paper proposes a depth-aware stair modeling method for monocular vision. Specifically, we take the extraction of stair geometric features and the prediction of depth images as joint tasks in a convolutional neural network (CNN), with the designed information propagation architecture, we can achieve effective supervision for stair geometric feature learning by depth information. In addition, to complete the stair modeling, we take the convex lines, concave lines, tread surfaces and riser surfaces as stair geometric features and apply Gaussian kernels to enable the network to predict contextual information within the stair lines. Combined with the depth information obtained by depth sensors, we propose a stair point cloud reconstruction method that can quickly get point clouds belonging to the stair step surfaces. Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3.4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications. Our dataset is available at https://data.mendeley. com/datasets/6kffmjt7g2/1.
Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds

Rotational invariance is a popular inductive bias used by many fields in machine learning, such as computer vision and machine learning for quantum chemistry. Rotation-invariant machine learning methods set the state of the art for many tasks, including molecular property prediction and 3D shape classification. These methods generally either rely on task-specific rotation-invariant features, or they use general-purpose deep neural networks which are complicated to design and train. However, it is unclear whether the success of these methods is primarily due to the rotation invariance or the deep neural networks. To address this question, we suggest a simple and general-purpose method for learning rotation-invariant functions of three-dimensional point cloud data using a random features approach. Specifically, we extend the random features method of Rahimi&Recht 2007 by deriving a version that is invariant to three-dimensional rotations and showing that it is fast to evaluate on point cloud data. We show through experiments that our method matches or outperforms the performance of general-purpose rotation-invariant neural networks on standard molecular property prediction benchmark datasets QM7 and QM9. We also show that our method is general-purpose and provides a rotation-invariant baseline on the ModelNet40 shape classification task. Finally, we show that our method has an order of magnitude smaller prediction latency than competing kernel methods.
Why Not? Explaining Missing Entailments with Evee (Technical Report)

Understanding logical entailments derived by a description logic reasoner is not always straight-forward for ontology users. For this reason, various methods for explaining entailments using justifications and proofs have been developed and implemented as plug-ins for the ontology editor Prot\'eg\'e. However, when the user expects a missing consequence to hold, it is equally important to explain why it does not follow from the ontology. In this paper, we describe a new version of $\rm E{\scriptsize VEE}$, a Prot\'eg\'e plugin that now also provides explanations for missing consequences, via existing and new techniques based on abduction and counterexamples.
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition

Brain-Computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI not only to enhance the quality of life for individuals with disabilities but also to develop mainstream applications for healthy users. One commonly used BCI signal pattern is the Steady-state Visually-evoked Potential (SSVEP), which captures the brain's response to flickering visual stimuli. SSVEP-based BCI-AR applications enable users to express their needs/wants by simply looking at corresponding command options. However, individuals are different in brain signals and thus require per-subject SSVEP recognition. Moreover, muscle movements and eye blinks interfere with brain signals, and thus subjects are required to remain still during BCI experiments, which limits AR engagement. In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference. Our testing on multiple subjects achieved a mean accuracy of 80% on a PC and 77% using the HoloLens AR headset, both of which surpass previous studies that incorporate individual classifiers and head movements. In addition, our visual stimulation time is 5 seconds which is relatively short. The statistically significant results show that our ensemble classification approach outperforms individual classifiers in SSVEP-based BCIs.
Helion: Enabling Natural Testing of Smart Homes

Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices.
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adversarial prompts can bypass their safety measures. We propose LLM Self Defense, a simple approach to defend against these attacks by having an LLM screen the induced responses. Our method does not require any fine-tuning, input preprocessing, or iterative output generation. Instead, we incorporate the generated content into a pre-defined prompt and employ another instance of an LLM to analyze the text and predict whether it is harmful. We test LLM Self Defense on GPT 3.5 and Llama 2, two of the current most prominent LLMs against various types of attacks, such as forcefully inducing affirmative responses to prompts and prompt engineering attacks. Notably, LLM Self Defense succeeds in reducing the attack success rate to virtually 0 using both GPT 3.5 and Llama 2.
On Cooperative Coevolution and Global Crossover

Cooperative coevolutionary algorithms (CCEAs) divide a given problem in to a number of subproblems and use an evolutionary algorithm to solve each subproblem. This short paper is concerned with the scenario under which only a single, global fitness measure exists. By removing the typically used subproblem partnering mechanism, it is suggested that such CCEAs can be viewed as making use of a generalised version of the global crossover operator introduced in early Evolution Strategies. Using the well-known NK model of fitness landscapes, the effects of varying aspects of global crossover with respect to the ruggedness of the underlying fitness landscape are explored. Results suggest improvements over the most widely used form of CCEAs, something further demonstrated using other well-known test functions.
MM-GEF: Multi-modal representation meet collaborative filtering

In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods.