Miracle-dz/UniFAR-FTUs huggingface dataset:
The analysis and measurements of spot focusing lens antenna resolution

The sizes of the focal area focused by spot focusing lens antenna and its landscape orientation spatial resolution are analyzed in virtue of wave theory in the paper. Relations between the characteristic of the spot focusing lens antenna at convergence region and the characteristic of the plane caliber antenna which has the same field distributing at infinite region are obtained. The method of the lens antenna spatial resolution measurement is brought forward. The results accord with the theory calculations.
Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at $3.67 \pm 0.05 \pm 0.15$ PeV. Below the knee, the spectral index is found to be -$2.7413 \pm 0.0004 \pm 0.0050$, while above the knee, it is -$3.128 \pm 0.005 \pm 0.027$, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -$0.1200 \pm 0.0003 \pm 0.0341$. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.
Features of conceptual blending in the context of visualisation

Computational creativity is a multi-disciplinary area of research that investigates what aspects of computing can be considered as an analogue to the human creative process. One premise is that humans come up with new concepts or creative ideas by combining two or more other concepts together. Conceptual blending is one of the creativity theories that has been modelled by computer programs attempting to emulate creativity. The current paper is part of a larger project that wishes to explore whether computer programs that automate the creation of data visualisations — such as pie charts, bar graphs and time series plots — can be enhanced by artificial intelligence methods that model human creative processes. One objective of the larger project is to explore and describe conceptual blending — and the techniques used to implement conceptual blending — in the context of applicability to visualisation. Metaphor emerges as a frequent emergent feature of conceptual blending with potential for creating useful visual features. Compression of information and iteration are shared and potentially exploitable features.
Thermal-NeRF: Neural Radiance Fields from an Infrared Camera

In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditions: a premise frequently unmet in robotic applications plagued by poor lighting or visual obstructions. This limitation overlooks the capabilities of infrared (IR) cameras, which excel in low-light detection and present a robust alternative under such adverse scenarios. To tackle these issues, we introduce Thermal-NeRF, the first method that estimates a volumetric scene representation in the form of a NeRF solely from IR imaging. By leveraging a thermal mapping and structural thermal constraint derived from the thermal characteristics of IR imaging, our method showcasing unparalleled proficiency in recovering NeRFs in visually degraded scenes where RGB-based methods fall short. We conduct extensive experiments to demonstrate that Thermal-NeRF can achieve superior quality compared to existing methods. Furthermore, we contribute a dataset for IR-based NeRF applications, paving the way for future research in IR NeRF reconstruction.
Measurement of groomed event shape observables in deep-inelastic electron-proton scattering at HERA

The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at $\sqrt{s}=319$ GeV, using data recorded between the years 2003 and 2007 with an integrated luminosity of $351$ pb$^{-1}$. Event shapes provide incisive probes of perturbative and non-perturbative QCD. Grooming techniques have been used for jet measurements in hadronic collisions; this paper presents the first application of grooming to DIS data. The analysis is carried out in the Breit frame, utilizing the novel Centauro jet clustering algorithm that is designed for DIS event topologies. Events are required to have squared momentum-transfer $Q^2>150$ GeV$^2$ and inelasticity $ 0.2<y<0.7$. We report measurements of the production cross section of groomed event 1-jettiness and groomed invariant mass for several choices of grooming parameter. Monte Carlo model calculations and analytic calculations based on Soft Collinear Effective Theory are compared to the measurements.
Diamond Micro-Chip for Quantum Microscopy

The nitrogen vacancy (NV) center in diamond is an increasingly popular quantum sensor for microscopy of electrical current, magnetization, and spins. However, efficient NV-sample integration with a robust, high-quality interface remains an outstanding challenge to realize scalable, high-throughput microscopy. In this work, we characterize a diamond micro-chip (DMC) containing a (111)-oriented NV ensemble; and demonstrate its utility for high-resolution quantum microscopy. We perform strain imaging of the DMC and find minimal detrimental strain variation across a field-of-view of tens of micrometer. We find good ensemble NV spin coherence and optical properties in the DMC, suitable for sensitive magnetometry. We then use the DMC to demonstrate wide-field microscopy of electrical current, and show that diffraction-limited quantum microscopy can be achieved. We also demonstrate the deterministic transfer of DMCs with multiple materials of interest for next-generation electronics and spintronics. Lastly, we develop a polymer-based technique for DMC placement. This work establishes the DMC's potential to expand the application of NV quantum microscopy in materials, device, geological, biomedical, and chemical sciences.
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection

Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets, and unlabeled target data to improve the accuracy and robustness of the detection model. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. This is challenging since the objects have unique modal information due to variations in object appearance across domains. A recent prototype-based approach proposed a class-wise alignment, yet it suffers from error accumulation due to noisy pseudo-labels which can negatively affect adaptation with imbalanced data. To overcome these limitations, we propose an attention-based class-conditioned alignment scheme for MSDA that aligns instances of each object category across domains. In particular, an attention module coupled with an adversarial domain classifier allows learning domain-invariant and class-specific instance representations. Experimental results on multiple benchmarking MSDA datasets indicate that our method outperforms the state-of-the-art methods and is robust to class imbalance. Our code is available at https://github.com/imatif17/ACIA.
Long-time behavior for discretization schemes of Fokker-Planck equations via couplings

Continuous-time Markov chains associated to finite-volume discretization schemes of Fokker-Planck equations are constructed. Sufficient conditions under which quantitative exponential decay in the $\phi$-entropy and Wasserstein distance are established, implying modified logarithmic Sobolev, Poincar\'e, and discrete Beckner inequalities. The results are not restricted to additive potentials and do not make use of discrete Bochner-type identities. The proof for the $\phi$-decay relies on a coupling technique due to Conforti, while the proof for the Wasserstein distance uses the path coupling method. Furthermore, exponential equilibration for discrete-time Markov chains is proved, based on an abstract discrete Bakry-Emery method and a path coupling.
Towards White Box Deep Learning

This paper introduces semantic features as a candidate conceptual framework for building inherently interpretable neural networks. A proof of concept model for informative subproblem of MNIST consists of 4 such layers with the total of 5K learnable parameters. The model is well-motivated, inherently interpretable, requires little hyperparameter tuning and achieves human-level adversarial test accuracy - with no form of adversarial training! These results and the general nature of the approach warrant further research on semantic features. The code is available at https://github.com/314-Foundation/white-box-nn
MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands

We introduce a large-scale dataset named MultiGripperGrasp for robotic grasping. Our dataset contains 30.4M grasps from 11 grippers for 345 objects. These grippers range from two-finger grippers to five-finger grippers, including a human hand. All grasps in the dataset are verified in Isaac Sim to classify them as successful and unsuccessful grasps. Additionally, the object fall-off time for each grasp is recorded as a grasp quality measurement. Furthermore, the grippers in our dataset are aligned according to the orientation and position of their palms, allowing us to transfer grasps from one gripper to another. The grasp transfer significantly increases the number of successful grasps for each gripper in the dataset. Our dataset is useful to study generalized grasp planning and grasp transfer across different grippers.
Efficient Detection of Exchangeable Factors in Factor Graphs

To allow for tractable probabilistic inference with respect to domain sizes, lifted probabilistic inference exploits symmetries in probabilistic graphical models. However, checking whether two factors encode equivalent semantics and hence are exchangeable is computationally expensive. In this paper, we efficiently solve the problem of detecting exchangeable factors in a factor graph. In particular, we introduce the detection of exchangeable factors (DEFT) algorithm, which allows us to drastically reduce the computational effort for checking whether two factors are exchangeable in practice. While previous approaches iterate all $O(n! )$ permutations of a factor's argument list in the worst case (where $n$ is the number of arguments of the factor), we prove that DEFT efficiently identifies restrictions to drastically reduce the number of permutations and validate the efficiency of DEFT in our empirical evaluation.
Online Policy Learning from Offline Preferences

In preference-based reinforcement learning (PbRL), a reward function is learned from a type of human feedback called preference. To expedite preference collection, recent works have leveraged \emph{offline preferences}, which are preferences collected for some offline data. In this scenario, the learned reward function is fitted on the offline data. If a learning agent exhibits behaviors that do not overlap with the offline data, the learned reward function may encounter generalizability issues. To address this problem, the present study introduces a framework that consolidates offline preferences and \emph{virtual preferences} for PbRL, which are comparisons between the agent's behaviors and the offline data. Critically, the reward function can track the agent's behaviors using the virtual preferences, thereby offering well-aligned guidance to the agent. Through experiments on continuous control tasks, this study demonstrates the effectiveness of incorporating the virtual preferences in PbRL.
Knowledge and perceptions of national and provincial tuberculosis control programme managers in Pakistan about the WHO Stop TB strategy: a qualitative study

Objective To understand how national and provincial tuberculosis programme managers in Pakistan perceive and engage with the Stop TB strategy, its strengths, weaknesses and their experience in its implementation. National and provincial tuberculosis programme managers play an important role in effective implementation of the Stop TB strategy. Design A qualitative interview study was conducted with 10 national and provincial tuberculosis programme managers to understand how they perceive and engage with the Stop TB strategy, its strengths, weaknesses and their experience in its implementation. Managers were selected purposively; 10 managers were interviewed (six national staff and four from provincial level). Participants National and provincial tuberculosis programme managers in Pakistan. Managers were selected purposively; 10 managers were interviewed (six national staff and four from provincial level). Setting National and provincial tuberculosis programmes in Pakistan Main outcome measures 1. Knowledge and perceptions of national and provincial tuberculosis programme managers about the Stop TB strategy 2. Progress in implementing the strategy in Pakistan 3. Significant success factors 4. Significant implementation challenges 5. Lessons learnt to scale up successful implementation. Results The managers reported that most progress had been made in extending DOTS, health systems strengthening, public -private mixed interventions, MDR-TB care and TB/HIV care. The four factors that contributed significantly to progress were the availability of DOTS services, the public-private partnership approach, comprehensive guidance for TB control and government and donor commitment to TB control. Conclusion This study identified three main challenges as perceived by national and provincial tuberculosis programme managers in terms of implementing the Stop TB strategy: 1. Inadequate political commitment, 2. Issue pertaining to prioritisation of certain components in the TB strategy over others due to external influences and 3. Limitations in the overall health system. To improve the tuberculosis control programme in the country political commitment needs to be enhanced and public -private partnerships increased. This can be done through government prioritisation of TB control at both national and provincial levels; donor-funded components should not receive undue attention; and partnerships with the private health sector, health institutions not yet covered by DOTS services, non-governmental organisations and patient coalitions should be increased.
Enabling Precision/Recall Preferences for Semi-supervised SVM Training

Semi-supervised learning is an essential approach to classification when the available labeled data is insufficient and we need to also make use of unlabeled data in the learning process. Numerous research efforts have focused on designing algorithms to improve the F1 score, but have any mechanism to control precision or recall individually. However, many applications have precision/recall preferences. For instance, an email spam classifier requires a precision of 0.9 to mitigate the false dismissal of useful emails. In this paper, we propose a method that allows to specify a precision/recall preference while maximising the F1 score. Our key idea is that we divide the semi-supervised learning process into multiple rounds of supervised learning, and the classifier learned at each round is calibrated using a sub-set of the labeled dataset before we use it on the unlabeled dataset for enlarging the training dataset. Our idea is applicable to a number of learning models such as Support Vector Machines (SVMs), Bayesian networks and neural networks. We focus our research and the implementation of our idea on SVMs. We conduct extensive experiments to validate the effectiveness of our method. The experimental results show that our method can train classifiers with a precision/recall preference, while the popular semi-supervised SVM training algorithm (which we use as the baseline) cannot. When we specify the precision preference and the recall preference to be the same, which indicates to maximise the F1 score only as the baseline does, our method achieves better or similar F1 scores to the baseline. An additional advantage of our method is that it converges much faster than the baseline.
Using an LLM to Turn Sign Spottings into Spoken Language Sentences

Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a hybrid SLT approach, Spotter+GPT, that utilizes a sign spotter and a pretrained large language model to improve SLT performance. Our method builds upon the strengths of both components. The videos are first processed by the spotter, which is trained on a linguistic sign language dataset, to identify individual signs. These spotted signs are then passed to the powerful language model, which transforms them into coherent and contextually appropriate spoken language sentences.
Multilevel functional distributional models with application to continuous glucose monitoring in diabetes clinical trials

Continuous glucose monitoring (CGM) is a minimally invasive technology that allows continuous monitoring of an individual's blood glucose. We focus on a large clinical trial that collected CGM data every few minutes for 26 weeks and assumes that the basic observation unit is the distribution of CGM observations in a four-week interval. The resulting data structure is multilevel (because each individual has multiple months of data) and distributional (because the data for each four-week interval is represented as a distribution). The scientific goals are to: (1) identify and quantify the effects of factors that affect glycemic control in type 1 diabetes (T1D) patients; and (2) identify and characterize the patients who respond to treatment. To address these goals, we propose a new multilevel functional model that treats the CGM distributions as a response. Methods are motivated by and applied to data collected by The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Group. Reproducible code for the methods introduced here is available on GitHub.
iBRF: Improved Balanced Random Forest Classifier

Class imbalance poses a major challenge in different classification tasks, which is a frequently occurring scenario in many real-world applications. Data resampling is considered to be the standard approach to address this issue. The goal of the technique is to balance the class distribution by generating new samples or eliminating samples from the data. A wide variety of sampling techniques have been proposed over the years to tackle this challenging problem. Sampling techniques can also be incorporated into the ensemble learning framework to obtain more generalized prediction performance. Balanced Random Forest (BRF) and SMOTE-Bagging are some of the popular ensemble approaches. In this study, we propose a modification to the BRF classifier to enhance the prediction performance. In the original algorithm, the Random Undersampling (RUS) technique was utilized to balance the bootstrap samples. However, randomly eliminating too many samples from the data leads to significant data loss, resulting in a major decline in performance. We propose to alleviate the scenario by incorporating a novel hybrid sampling approach to balance the uneven class distribution in each bootstrap sub-sample. Our proposed hybrid sampling technique, when incorporated into the framework of the Random Forest classifier, termed as iBRF: improved Balanced Random Forest classifier, achieves better prediction performance than other sampling techniques used in imbalanced classification tasks. Experiments were carried out on 44 imbalanced datasets on which the original BRF classifier produced an average MCC score of 47.03% and an F1 score of 49.09%. Our proposed algorithm outperformed the approach by producing a far better MCC score of 53.04% and an F1 score of 55%. The results obtained signify the superiority of the iBRF algorithm and its potential to be an effective sampling technique in imbalanced learning.
Design and Control Co-Optimization for Automated Design Iteration of Dexterous Anthropomorphic Soft Robotic Hands

We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous manipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nearly 400 hand designs, testing grasp quality under external force disturbances. We validate the optimized designs in the real world through teleoperation of pickup and reorient manipulation tasks. Our real world evaluation, from over 900 teleoperated tasks, shows that the trend in design performance in simulation resembles that of the real world. Furthermore, we show that optimized hand designs from our approach outperform existing soft robot hands from prior work in the real world. The results highlight the usefulness of simulation in guiding parameter choices for anthropomorphic soft robotic hand systems, and the effectiveness of our automated design iteration approach, despite the sim-to-real gap.
A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery

Intraoperative brain deformations decrease accuracy in image-guided neurosurgery. Approaches to quantify these deformations based on 3-D reconstruction of cortectomy surfaces have been described and have shown promising results regarding the extrapolation to the whole brain volume using additional prior knowledge or sparse volume modalities. Quantification of brain deformations from surface measurement requires the registration of surfaces at different times along the surgical procedure, with different challenges according to the patient and surgical step. In this paper, we propose a new flexible surface registration approach for any textured point cloud computed by stereoscopic or laser range approach. This method includes three terms: the first term is related to image intensities, the second to Euclidean distance, and the third to anatomical landmarks automatically extracted and continuously tracked in the 2-D video flow. Performance evaluation was performed on both phantom and clinical cases. The global method, including textured point cloud reconstruction, had accuracy within 2 mm, which is the usual rigid registration error of neuronavigation systems before deformations. Its main advantage is to consider all the available data, including the microscope video flow with higher temporal resolution than previously published methods.