Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC

Motion compensation is one of the key technologies enabling the high compression efficiency of modern video coding standards. To allow compression of spherical video content, special mapping functions are required to project the video to the 2D image plane. Distortions inevitably occurring in these mappings impair the performance of classical motion models. In this paper, we propose a novel motion plane adaptive motion modeling technique (MPA) for spherical video that allows to perform motion compensation on different motion planes in 3D space instead of having to work on the - in theory arbitrarily mapped - 2D image representation directly. The integration of MPA into the state-of-the-art H.266/VVC video coding standard shows average Bjøntegaard Delta rate savings of 1.72% with a peak of 3.37% based on PSNR and 1.55% with a peak of 2.92% based on WS-PSNR compared to VTM-14.2.
Knowledge Graph-Augmented Korean Generative Commonsense Reasoning

Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding. By utilizing an existing dataset such as Korean CommonGen, language generation models can learn commonsense reasoning specific to the Korean language. However, language models often fail to consider the relationships between concepts and the deep knowledge inherent to concepts. To address these limitations, we propose a method to utilize the Korean knowledge graph data for text generation. Our experimental result shows that the proposed method can enhance the efficiency of Korean commonsense inference, thereby underlining the significance of employing supplementary data.
Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception

Uncertainty in control and perception poses challenges for autonomous vehicle navigation in unstructured environments, leading to navigation failures and potential vehicle damage. This paper introduces a framework that minimizes control and perception uncertainty to ensure safe and reliable navigation. The framework consists of two uncertainty-aware models: a learning-based vehicle dynamics model and a self-supervised traversability estimation model. We train a vehicle dynamics model that can quantify the epistemic uncertainty of the model to perform active exploration, resulting in the efficient collection of training data and effective avoidance of uncertain state-action spaces. In addition, we employ meta-learning to train a traversability cost prediction network. The model can be trained with driving data from a variety of types of terrain, and it can online-adapt based on interaction experiences to reduce the aleatoric uncertainty. Integrating the dynamics model and traversability cost prediction model with a sampling-based model predictive controller allows for optimizing trajectories that avoid uncertain terrains and state-action spaces. Experimental results demonstrate that the proposed method reduces uncertainty in prediction and improves stability in autonomous vehicle navigation in unstructured environments.
Adaptive Privacy Composition for Accuracy-first Mechanisms

In many practical applications of differential privacy, practitioners seek to provide the best privacy guarantees subject to a target level of accuracy. A recent line of work by Ligett et al. '17 and Whitehouse et al. '22 has developed such accuracy-first mechanisms by leveraging the idea of noise reduction that adds correlated noise to the sufficient statistic in a private computation and produces a sequence of increasingly accurate answers. A major advantage of noise reduction mechanisms is that the analysts only pay the privacy cost of the least noisy or most accurate answer released. Despite this appealing property in isolation, there has not been a systematic study on how to use them in conjunction with other differentially private mechanisms. A fundamental challenge is that the privacy guarantee for noise reduction mechanisms is (necessarily) formulated as ex-post privacy that bounds the privacy loss as a function of the released outcome. Furthermore, there has yet to be any study on how ex-post private mechanisms compose, which allows us to track the accumulated privacy over several mechanisms. We develop privacy filters [Rogers et al. '16, Feldman and Zrnic '21, and Whitehouse et al. '22'] that allow an analyst to adaptively switch between differentially private and ex-post private mechanisms subject to an overall differential privacy guarantee.
Localized Text-to-Image Generation for Free via Cross Attention Control

Despite the tremendous success in text-to-image generative models, localized text-to-image generation (that is, generating objects or features at specific locations in an image while maintaining a consistent overall generation) still requires either explicit training or substantial additional inference time. In this work, we show that localized generation can be achieved by simply controlling cross attention maps during inference. With no additional training, model architecture modification or inference time, our proposed cross attention control (CAC) provides new open-vocabulary localization abilities to standard text-to-image models. CAC also enhances models that are already trained for localized generation when deployed at inference time. Furthermore, to assess localized text-to-image generation performance automatically, we develop a standardized suite of evaluations using large pretrained recognition models. Our experiments show that CAC improves localized generation performance with various types of location information ranging from bounding boxes to semantic segmentation maps, and enhances the compositional capability of state-of-the-art text-to-image generative models.
Estimating Network Dimension When the Spectrum Struggles

What is the dimension of a network? Here, we view it as the smallest dimension of Euclidean space into which nodes can be embedded so that pairwise distances accurately reflect the connectivity structure. We show that a recently proposed and extremely efficient algorithm for data clouds, based on computing first and second nearest neighbour distances, can be used as the basis of an approach for estimating the dimension of a network with weighted edges. We also show how the algorithm can be extended to unweighted networks when combined with spectral embedding. We illustrate the advantages of this technique over the widely-used approach of characterising dimension by visually searching for a suitable gap in the spectrum of the Laplacian.
Potential Benefits of Employing Large Language Models in Research in Moral Education and Development

Recently, computer scientists have developed large language models (LLMs) by training prediction models with large-scale language corpora and human reinforcements. The LLMs have become one promising way to implement artificial intelligence with accuracy in various fields. Interestingly, recent LLMs possess emergent functional features that emulate sophisticated human cognition, especially in-context learning and the chain of thought, which were unavailable in previous prediction models. In this paper, I will examine how LLMs might contribute to moral education and development research. To achieve this goal, I will review the most recently published conference papers and ArXiv preprints to overview the novel functional features implemented in LLMs. I also intend to conduct brief experiments with ChatGPT to investigate how LLMs behave while addressing ethical dilemmas and external feedback. The results suggest that LLMs might be capable of solving dilemmas based on reasoning and revising their reasoning process with external input. Furthermore, a preliminary experimental result from the moral exemplar test may demonstrate that exemplary stories can elicit moral elevation in LLMs as do they among human participants. I will discuss the potential implications of LLMs on research on moral education and development with the results.
The exact bound for the reverse isodiametric problem in 3-space

Let $K$ be a convex body in $\mathbb{R}^{3}$. We denote the volume of $K$ by $Vol(K)$ and the diameter of $K$ by $Diam(K). $ In this paper we prove that there exists a linear bijection $T:\mathbb{R}^{3}\to \mathbb{R}^{3}$ such that $Vol(TK)\geq \frac{\sqrt{2}}{12}Diam(TK)^3$ with equality if $K$ is a simplex, which was conjectured by Endre Makai Jr. As a corollary, we prove that any non-separable lattice of translates in $\mathbb{R}^{3}$ has density of at least $\frac{1}{12}$, which is a dual analog of Minkowski's fundamental theorem. Also we prove that $Vol(K)\geq \frac{1}{12}\omega(K)^3$, where $K\subset \mathbb{R}^{3}$ is a convex body and $\omega(K)$ is the lattice width of $K$. In addition, there exists a three-dimensional simplex $\Delta\subset \mathbb{R}^3$ such that $Vol(\Delta) = \frac{1}{12}\omega(\Delta)^3. $
Selenide sputtered films development for MIR environmental sensor

A micro-sensor based on selenide glasses for evanescent wave detection in mid-infrared spectral range was designed and fabricated. Ge-Sb-Se thin films were successfully deposited by radio-frequency magnetron sputtering. In order to characterize them spectroscopic ellipsometry, atomic force microscopy and contact angle measurements were employed to study near and middle infrared refractive index, surface roughness and the wettability, respectively. Selenide sputtered films were micro-patterned by means of reactive ion etching with inductively coupled plasma process enabling single-mode propagation at a wavelength of 7.7 µm for a waveguide width between 8 and 12 µm. Finally, optical waveguide surface was functionalized by deposition of a hydrophobic polymer, which will permit detection of organic molecules in water. Thus, the optical transducer is a ridge waveguide composed by cladding and guiding Ge-Sb-Se sputtered layers exhibiting a tailored refractive index contrast and a polymer layer onto its surface ready for environmental detections in middle infrared.
Practitioner Motives to Select Hyperparameter Optimization Methods

Advanced programmatic hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sample efficiency in reproducibly finding optimal hyperparameter values of machine learning (ML) models. Yet, ML practitioners often apply less sample-efficient HPO methods, such as grid search, which often results in under-optimized ML models. As a reason for this behavior, we suspect practitioners choose HPO methods based on individual motives, consisting of contextual factors and individual goals. However, practitioners' motives still need to be clarified, hindering the evaluation of HPO methods for achieving specific goals and the user-centered development of HPO tools. To understand practitioners' motives for using specific HPO methods, we used a mixed-methods approach involving 20 semi-structured interviews and a survey study with 71 ML experts to gather evidence of the external validity of the interview results. By presenting six main goals (e.g., improving model understanding) and 14 contextual factors affecting practitioners' selection of HPO methods (e.g., available computer resources), our study explains why practitioners use HPO methods that seem inappropriate at first glance. This study lays a foundation for designing user-centered and context-adaptive HPO tools and, thus, linking social and technical research on HPO.
Novel technologies to micro and nanostructure highly nonplanar optical fibers: from the micro to the sub-50 nm regime

Recent advancements in optogenetics and brain interfacing technologies have significantly improved neuroscience research. However, developing user-friendly and efficient probes with high spatial and temporal resolution and specificity remains a challenge. Tapered Optical Fibers (TOFs) have emerged as an intriguing solution due to their unique properties. This work reviews the strategies developed to enable precise definitions of light delivery and collection sites along the TOF axis, incorporating additional functionalities such as electrical recording sites or exploiting alternative light-matter interactions for label-free applications. The latest progresses in TOF micro and nanostructuring are categorized based on these objectives, highlighting the benefits and limitations of each approach. This manuscript aims at providing a comprehensive overview of recent advancements in TOF micro and nanostructuring.
Semi-analytical computation method for propagation loss of hollow-core anti-resonant fiber

Hollow core anti-resonant fibers (HCAR) exploit the resonant structure of the cladding to decrease the propagation loss at specific transmission windows located in between resonance wavelengths. HCAR loss is usually computed by numerical methods such as finite elements or estimated by analytical formulas. Here, the semi-analytical method based on solution of the nonlinear dispersion equation is presented and discussed. The method employs the theory of propagation of electromagnetic waves in cylindrically-layered media and it is fully vectorial. Its reliability and efficiency is tested by comparison with results of numerical simulations of the HCAR structure using modal analysis in COMSOL.
Investigation of NbRe for superconducting microstrips single photon detectors

Superconducting Microstrips Single Photon Detectors (SMSPDs) are currently of crucial interest in numerous applications thanks to their excellent performance in terms of high detection efficiency, short jitter time, and low dark count rate, meeting the necessities of covering a large active area using low-cost technology. In this scenario, the research of new materials to detect from telecom (1550 nm) to mid-infrared wavelengths plays an important role. In this work we fabricated SMSPDs made of NbRe, an innovative material recently proposed in this field. We realized devices with different microstrip configurations based on single microstrips and pairs of parallel microstrips to investigate the role of the geometry. Single photon detection sensitivity at 1550 nm wavelength at a temperature of 1.79 K was demonstrated. The results obtained are encouraging for the photon detection with NbRe-based devices covering large areas.
Optimization of erbium and ytterbium concentration in nanostructured core fiber for dual-wavelength fiber lasers

Recent evolution in nanoscience and nanotechnologies has brought novel possibilities in the development of optical fibers. Dual-wavelength fiber lasers have attracted scientific attention due to their prospective applications in fields including next-generation optical fiber communication, ranging systems, and spectroscopy. Nanostructurization has shown itself as a suitable method for preparing fiber lasers operating simultaneously at dual wavelengths. We report on the design of nanostructured or “pixelated” core fabricated by assembling erbium- and ytterbium elements, as well as on the optimization of the average concentration of rare earth elements using numerical modeling. Preliminary experimental results of erbium- and ytterbium-doped nanostructured-core fiber will be presented.
Research on transverse vibration characteristics of beam-strut composite system based on analytical method

In this paper, the beam-strut composite system is taken as a simplified theoretical model, and the transverse vibration of the model is modeled by analytical method, and the reliability of the model is verified by numerical substitution. Based on this model, the influence of each mode of the system on the final displacement, the lateral vibration response of the system at different excitation frequencies and the influence of excitation displacement velocity on the lateral vibration of the system are analyzed. The results show that the higher the mode order, the smaller the influence of the mode on the final displacement of the system; the system will not resonate at the actual working frequency; the lateral vibration displacement of the system will increase with the increase of excitation displacement speed.
Development of light-field motion tracking technology for use in laboratory studies of planet formation

Modelling of planet formation requires empirical data on the collisions involved in the earliest stages of the process. Laboratory-based studies are required to gain this data by colliding dusty, icy particles in conditions analogous to those found in protoplanetary disks. Having technology to capture experimental footage and extract the three-dimensional motions of ensembles of particles is crucial to generating accurate collisional data within a practical timeframe. The cost of microgravity-based experiments drives a need to minimize the form-factor of such an imaging system leading this work to use light-field techniques to provide the depth element of tracking from a single camera. This work focused on the development of software to be used to perform light-field based, three-dimensional tracking and its application to real-time analysis of mm-scale particle collisions.
Luminescence lifetime of Er-doped silica optical fibers: the role of composition and fabrication processing

For applications in fiber lasers and amplifiers, silica glass remains a perspective host for rare-earth ions thanks to favorable material properties. However, the luminescence of RE ions is hindered by the high phonon energy of silica lattice and low solubility of RE ions, which cause luminescence quenching. Pure silica thus needs to be co-doped with suitable additives such as Al2O3, which form a beneficial low-phonon environment and increase the solubility of RE ions. Luminescence lifetime is one of the most important parameters to determine the suitability of RE-doped silica fibers for laser operation. Optical fibers with higher luminescence lifetime typically exhibit higher values of slope efficiency and lower laser threshold. It was previously shown that the environment of Tm3+, Ho3+ or Yb3+ ions and luminescence lifetime may be significantly affected by fabrication processing at high temperatures, probably due to the chemical changes occurring in the matrix. However, the effect of fabrication processing on the spectroscopic properties of another important RE ion, Er3+, is different to the other ions and remains unclear. In this contribution, we present a study on the fluorescence lifetime of a highly-doped optical fiber prepared by the MCVD method combined with nanoparticle-doping. The fluorescence lifetime of Er3+ was studied in several stages of fabrication processing. The influence of fabrication processing on the fluorescence lifetime of Er3+ ions was analyzed and discussed.
Influence of abiotic and biotic factors on benthic marine community composition, structure and stability: a multidisciplinary approach to molluscan assemblages from the Miocene of northern Germany

The Miocene mica‐clay deposits of Groß Pampau (northern Germany) are well known for their diverse assemblages of marine mammals. Despite numerous systematic and biostratigraphic studies, an in‐depth palaeoecological analysis of its molluscan assemblages and a comprehensive palaeoenvironmental reconstruction are lacking. Here, we integrate new faunal, sedimentological and geochemical data to reconstruct the marine palaeoecosystem of the Upper Miocene sedimentary succession of Groß Pampau, and to identify the drivers controlling the composition, ecological structure and temporal dynamics of its macrobenthic molluscan assemblages. Fossil evidence, coupled with analyses of clay mineral composition, grain size distribution and geochemical data (total organic carbon, total nitrogen, δ13C, δ18O, δ15N of sediment and shells), suggests a warm–temperate, mesotrophic, low‐energy, offshore marine setting mostly below storm wave base and a pronounced surface‐to‐bottom water temperature gradient. Low variability in sedimentological and geochemical signals indicates generally stable physicochemical conditions, whereas the occurrence of the opportunistic species Varicorbula gibba suggests occasionally unfavourable bottom conditions, possibly related to transient hypoxia. Canonical correspondence analysis indicates that the distribution of molluscan assemblages correlates with total organic carbon and nitrogen content, suggesting organic matter availability at the sea floor as a controlling factor. A pattern of repetitive punctuated stasis of molluscan assemblages is defined by the temporal persistence in taxonomic and ecological composition, occasionally interrupted by shifts to a different faunal configuration. We suggest that both stable environmental conditions and biotic interactions (i.e. the top‐down control exerted by carnivorous gastropods and environmental modification by ubiquitous burrowing deposit feeders) probably contributed to the observed temporal stability.
A Machine Learning Approach for Real-time Cortical State Estimation

Objective Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as “cortical state”. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach We use unsupervised Gaussian Mixture Models (GMMs) to identify discrete, emergent clusters in spontaneous local field potential (LFP) signals in cortex. We then extend our approach to a temporally-informed Hidden semi-Markov Model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.
A Risk-reward Examination of Sample Multiplexing Reagents for Single Cell RNA-Seq

Single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for understanding cellular heterogeneity and function. However the choice of sample multiplexing reagents can impact data quality and experimental outcomes. In this study, we compared various multiplexing reagents, including MULTI-Seq, Hashtag antibody, and CellPlex, across diverse sample types such as human peripheral blood mononuclear cells (PBMCs), mouse embryonic brain and patient-derived xenografts (PDXs). We found that all multiplexing reagents worked well in cell types robust to ex vivo manipulation but suffered from signal-to-noise issues in more delicate sample types. We compared multiple demultiplexing algorithms which differed in performance depending on data quality. We find that minor improvements to laboratory workflows such as titration and rapid processing are critical to optimal performance. We also compared the performance of fixed scRNA-Seq kits and highlight the advantages of the Parse Biosciences kit for fragile samples. Highly multiplexed scRNA-Seq experiments require more sequencing resources, therefore we evaluated CRISPR-based destruction of non-informative genes to enhance sequencing value. Our comprehensive analysis provides insights into the selection of appropriate sample multiplexing reagents and protocols for scRNASeq experiments, facilitating more accurate and cost-effective studies.