Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution.
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks

Botnet attacks are a major threat to networked systems because of their ability to turn the network nodes that they compromise into additional attackers, leading to the spread of high volume attacks over long periods. The detection of such Botnets is complicated by the fact that multiple network IP addresses will be simultaneously compromised, so that Collective Classification of compromised nodes, in addition to the already available traditional methods that focus on individual nodes, can be useful. Thus this work introduces a collective Botnet attack classification technique that operates on traffic from an n-node IP network with a novel Associated Random Neural Network (ARNN) that identifies the nodes which are compromised. The ARNN is a recurrent architecture that incorporates two mutually associated, interconnected and architecturally identical n-neuron random neural networks, that act simultneously as mutual critics to reach the decision regarding which of n nodes have been compromised. A novel gradient learning descent algorithm is presented for the ARNN, and is shown to operate effectively both with conventional off-line training from prior data, and with on-line incremental training without prior off-line learning. Real data from a 107 node packet network is used with over 700,000 packets to evaluate the ARNN, showing that it provides accurate predictions. Comparisons with other well-known state of the art methods using the same learning and testing datasets, show that the ARNN offers significantly better performance.
SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions

Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In order to effectively complete tasks in these spaces, robots must be able to perceive, reason, and execute over a diversity of affordances, well beyond simple pick-and-place. We posit the notion of semantic frames provides a compelling representation for robot actions that is amenable to action-focused perception, task-level reasoning, action-level execution, and integration with language. Semantic frames, a product of the linguistics community, define the necessary elements, pre- and post- conditions, and a set of sequential robot actions necessary to successfully execute an action evoked by a verb phrase. In this work, we extend the semantic frame representation for robot manipulation actions and introduce the problem of Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions (SEAL) as a graphical model. For the SEAL problem, we describe our nonparametric Semantic Frame Mapping (SeFM) algorithm for maintaining belief over a finite set of semantic frames as the locations of actions afforded to the robot. We show that language models such as GPT-3 are insufficient to address generalized task execution covered by the SEAL formulation and SeFM provides robots with efficient search strategies and long term memory needed when operating in building-scale environments.
One Protocol to Rule Them All? On Securing Interoperable Messaging

European lawmakers have ruled that users on different platforms should be able to exchange messages with each other. Yet messaging interoperability opens up a Pandora's box of security and privacy challenges. While championed not just as an anti-trust measure but as a means of providing a better experience for the end user, interoperability runs the risk of making the user experience worse if poorly executed. There are two fundamental questions: how to enable the actual message exchange, and how to handle the numerous residual challenges arising from encrypted messages passing from one service provider to another -- including but certainly not limited to content moderation, user authentication, key management, and metadata sharing between providers. In this work, we identify specific open questions and challenges around interoperable communication in end-to-end encrypted messaging, and present high-level suggestions for tackling these challenges.
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Survival analysis is an essential tool for the study of health data. An inherent component of such data is the presence of missing values. In recent years, researchers proposed new learning algorithms for survival tasks based on neural networks. Here, we studied the predictive performance of such algorithms coupled with different methods for handling missing values on simulated data that reflect a realistic situation, i.e., when individuals belong to unobserved clusters. We investigated different patterns of missing data. The results show that, without further feature engineering, no single imputation method is better than the others in all cases. The proposed methodology can be used to compare other missing data patterns and/or survival models. The Python code is accessible via the package survivalsim. -- L'analyse de survie est un outil essentiel pour l'\'etude des donn\'ees de sant\'e. Une composante inh\'erente \`a ces donn\'ees est la pr\'esence de valeurs manquantes. Ces derni\`eres ann\'ees, de nouveaux algorithmes d'apprentissage pour la survie, bas\'es sur les r\'eseaux de neurones, ont \'et\'e con\c{c}us. L'objectif de ce travail est d'\'etudier la performance en pr\'ediction de ces algorithmes coupl\'es \`a diff\'erentes m\'ethodes pour g\'erer les valeurs manquantes, sur des donn\'ees simul\'ees qui refl\`etent une situation rencontr\'ee en pratique, c'est-\`a dire lorsque les individus peuvent \^etre group\'es selon leurs covariables. Diff\'erents sch\'emas de donn\'ees manquantes sont \'etudi\'es. Les r\'esultats montrent que, sans l'ajout de variables suppl\'ementaires, aucune m\'ethode d'imputation n'est meilleure que les autres dans tous les cas. La m\'ethodologie propos\'ee peut \^etre utilis\'ee pour comparer d'autres mod\`eles de survie. Le code en Python est accessible via le package survivalsim.
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration

Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the large number of multiply-and-accumulate (MAC) operations. To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations. In addition, the timing characteristics of the selected weights together with all activation transitions are evaluated. The weights and activations that lead to small delays are further selected. Consequently, the maximum delay of the sensitized circuit paths in the MAC units is reduced even without modifying MAC units, which thus allows a flexible scaling of supply voltage to reduce power consumption further. Together with retraining, the proposed method can reduce power consumption of DNNs on hardware by up to 73.9% with only a slight accuracy loss.
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives

With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have much higher prediction performance than traditional machine learning models, the specific prediction process is still difficult to interpret and/or explain. This is known as the black-boxing of machine learning models and is recognized as a particularly important problem in a wide range of research fields, including manufacturing, commerce, robotics, and other industries where the use of such technology has become commonplace, as well as the medical field, where mistakes are not tolerated. This bulletin is based on the summary of the author's dissertation. The research summarized in the dissertation focuses on the attention mechanism, which has been the focus of much attention in recent years, and discusses its potential for both basic research in terms of improving prediction performance and interpretability, and applied research in terms of evaluating it for real-world applications using large data sets beyond the laboratory environment. The dissertation also concludes with a summary of the implications of these findings for subsequent research and future prospects in the field.
Evaluating a large language model’s ability to solve programming exercises from an introductory bioinformatics course

Computer programming is a fundamental tool for life scientists, allowing them to carry out essential research tasks. However, despite various educational efforts, learning to write code can be a challenging endeavor for students and researchers in life-sciences disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists’ efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such tool—OpenAI’s ChatGPT—could successfully complete programming tasks. ChatGPT solved 139 (75.5%) of the exercises on its first attempt. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have implications for life-sciences education and research. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public. For some programming tasks, researchers may be able to work in collaboration with machine-learning models to produce functional code.
Return of the RNN: Residual Recurrent Networks for Invertible Sentence Embeddings

This study presents a novel model for invertible sentence embeddings using a residual recurrent network trained on an unsupervised encoding task. Rather than the probabilistic outputs common to neural machine translation models, our approach employs a regression-based output layer to reconstruct the input sequence's word vectors. The model achieves high accuracy and fast training with the ADAM optimizer, a significant finding given that RNNs typically require memory units, such as LSTMs, or second-order optimization methods. We incorporate residual connections and introduce a"match drop"technique, where gradients are calculated only for incorrect words. Our approach demonstrates potential for various natural language processing applications, particularly in neural network-based systems that require high-quality sentence embeddings.
Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification

Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to changes in individual users and in the external environment. To this end, we propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks. Various external factors such as viewpoint and distance to the camera, are investigated in this study. Extensive experiments have shown superior performanceof the proposed model compared with the state-of-the-art model.
A genetic algorithm-based fuzzy goal programming approach for solving fractional bilevel programming problems

This paper presents a genetic algorithm (GA) based fuzzy goal programming procedure for modelling and solving bilevel programming problems having fractional objectives in a hierarchical decision system. In the proposed approach, the concept of tolerance membership functions in fuzzy sets for measuring the degree of satisfactions of the decision-makers (DMs) regarding achievements of fuzzily described objective goals as well as the degree of optimality of the decision vector controlled by the upper-level DM are considered in the decision-making context. The proposed approach leads to achieve the highest membership value (unity) of each of the defined fuzzy goals to the extent possible in the decision-making situation. In the GA search process, the fitter codon selection scheme, two-point crossover and random mutation are adopted to reach a satisfactory solution in the decision-making environment. To illustrate the potential use of the approach, a numerical example is solved.
SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features

Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environ-ment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation of the contact points between the two bodies. We experimentally evaluated the proposed approach for a dish cleaning task both in simulation and on a real Franka Emika Panda with real-world objects. The experimental results demonstrate that in both scenarios the proposed planning pipeline is capable of generating high-quality trajectories that can accomplish the task by achieving more than 90% area coverage on different objects of varying sizes and curvatures while minimizing travel distance. Code and video are available at: https://irobotics.aalto. fi/sponge/.
AN ALTERNATIVE DERIVATION OF THE SHALLOW WATER EQUATIONS

The one dimensional shallow water equations consist of the equation of mass conservation and that of momentum conservation. In this paper, those equations are derived. We utilize a constant approximation of integration in the derivation. We call our derivation an alternative derivation since it is different from the classical derivation. The classical derivation of the shallow water equations involves a velocity-potential function, while our derivation does not.
An Electronic Publication Dedicated to Early Stellar Evolution and Molecular Clouds Abstracts of Recently Accepted Papers Hcn in Bok Globules: a Good Tracer of Collapsing Cores Mass Segregation in Young Stellar Clusters Injection of Short-lived Isotopes into the Presolar Cloud

We have conducted a HCN (J=1-0) survey of dense cores in a sample of 24 star-forming and quiescent Bok globules. HCN emission was detected towards 11 of 13 globules containing embedded point sources with no HCN detection among 11 starless globules. As in other dark clouds, the J=1-0 hyperfine line intensity ratios vary from globule to globule and also with position toward the same globule, suggesting the presence of either a scattering envelope surrounding the core or a complex density structure. We find that the J=1-0 transition of the HCN molecule can be used effectively to search for early stages of star formation in small molecular clouds: in the sample of star-forming cores, for 3 (of 5) sources mapped, there is good spatial coincidence (better than 6 arc sec) between the position of the peak integrated HCN emission and the location of the associated embedded source. Furthermore, this transition is well correlated with the YSO class, detecting preferentially Class I YSOs. Our results indicate that detecting strong (> 1 K km s −1) HCN emission from a molecular cloud core seems to imply the presence of an embedded protostar and thus, indirectly, that of a collapsing core. We investigate the evolutionary effect of dynamical mass segregation in young stellar clusters. Dynamical mass segregation acts on a timescale of order the relaxation time of a cluster. Although some degree of mass segregation occurs earlier, the position of massive stars in rich young clusters generally reflect the cluster's initial conditions. In particular, the positions of the massive stars in the Trapezium Cluster in Orion cannot be due to dynamical mass segregation but indicate that they formed in, or near, the cluster's centre. Implications of this for cluster formation and for the formation of high-mass stars are discussed. The evidence for short-lived isotopes such as 26 Al and 41 Ca in meteorites requires their production either by irradiation in the solar nebula, or by nucleosynthesis in a supernova or other evolved star. In the latter case, nucleosynthesis 1
Online Learning for the Random Feature Model in the Student-Teacher Framework

Deep neural networks are widely used prediction algorithms whose performance often improves as the number of weights increases, leading to over-parametrization. We consider a two-layered neural network whose first layer is frozen while the last layer is trainable, known as the random feature model. We study over-parametrization in the context of a student-teacher framework by deriving a set of differential equations for the learning dynamics. For any finite ratio of hidden layer size and input dimension, the student cannot generalize perfectly, and we compute the non-zero asymptotic generalization error. Only when the student's hidden layer size is exponentially larger than the input dimension, an approach to perfect generalization is possible.
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images

Recent work in Neural Fields (NFs) learn 3D representations from class-specific single view image collections. However, they are unable to reconstruct the input data preserving high-frequency details. Further, these methods do not disentangle appearance from geometry and hence are not suitable for tasks such as texture transfer and editing. In this work, we propose TEGLO (Textured EG3D-GLO) for learning 3D representations from single view in-the-wild image collections for a given class of objects. We accomplish this by training a conditional Neural Radiance Field (NeRF) without any explicit 3D supervision. We equip our method with editing capabilities by creating a dense correspondence mapping to a 2D canonical space. We demonstrate that such mapping enables texture transfer and texture editing without requiring meshes with shared topology. Our key insight is that by mapping the input image pixels onto the texture space we can achieve near perfect reconstruction (>= 74 dB PSNR at 1024^2 resolution). Our formulation allows for high quality 3D consistent novel view synthesis with high-frequency details at megapixel image resolution.
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor

Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lower spatial resolution and non-Bayer CFA artifacts. To address these challenges, we propose a dual-head joint remosaicing and denoising network (DJRD), which enables the conversion of noisy Quad Bayer and standard noise-free Bayer pattern without any resolution loss. DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated denoising modules based on Swin-transformer and multi-scale wavelet transform. The QB-Re block constructs the convolution kernel based on the CFA pattern to achieve a periodic color distribution in the perceptual field, which is used to extract exact spectral information and reduce color misalignment. The integrated Swin-Transformer and multi-scale wavelet transform capture non-local dependencies, frequency and location information to effectively reduce practical noise. By identifying challenging patches utilizing Moire and zipper detection metrics, we enable our model to concentrate on difficult patches during the post-training phase, which enhances the model's performance in hard cases. Our proposed model outperforms competing models by approximately 3dB, without additional complexity in hardware or software.
Crime Mapping and the CrimeStat Program

CrimeStat is a spatial statistics program used in crime mapping. The program inputs incident or point locations and outputs statistics that can be displayed graphically in a geographic information systems (GIS) program. Among the routines are those for summary spatial description, hot spot analysis, interpolation, space–time analysis, and journey-to-crime modeling. Version 3.0 has a crime travel demand module for analyzing travel patterns over a metropolitan area. The program and documentation are distributed by the National Institute of Justice.
Feature saliency affects delayed matching of an attended feature

We examined how top-down attentional modulation and bottom-up stimulus saliency interact with feature memory. Experiment 1 used a delayed-matching-to-sample (DMS) task to examine the relative saliency between features by observing the relative accuracy of recognition at different stimulus durations. Feature salience decreased according to the following order: colour, form, and texture. In a modified DMS task (Experiments 2 and 3), participants were required to attend to one of three features and ignore the others. After a delay, they were required to choose which of the two test stimuli matched the reference stimulus on the attended feature, disregarding other task-irrelevant features. The target was either identical to the reference stimulus or mismatched the reference stimulus on one of the irrelevant features. The results showed that colour matching was affected neither by a form change nor by a texture change. Form matching was affected by a colour change, and texture matching was affected by a colour or form change. These results are consistent with the relative saliency hypothesis. Even when all features of an attended object are maintained, a relatively more salient task-irrelevant feature can interfere with the delayed recognition of a less salient feature.