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In this paper, we derive the primordial black hole (PBH) mass function using the Press-Schechter formalism, particularly using the excursion set approach, which is a powerful statistical method for obtaining the mass functions and modeling the formation of PBHs in the early universe by invoking the concepts like random walks. The excursion set formalism provides a framework for calculating the mass function by tracking the evolution of density fluctuations in the primordial universe and their subsequent collapse into PBHs uponcrossing critical barriers. By applying this theory, we compute the PBH mass function and explore its dependence on key parameters such as the peak mass,standard deviation, and initial PBH fraction. We also incorporate the effects of observational constraints, including microlensing and Cosmic Microwave Background (CMB) measurements, to place limits on the PBH population across different mass ranges. Our results offer important insights into the role of PBHs as potential dark matter candidates and their contribution to the overall matter content in the universe. The derived mass function provides a basis for further exploration of PBH formation, growth, and their possible detection through gravitational waves and other cosmological signatures.

Modern affective computing systems rely heavily on datasets with human-annotated emotion labels, for training and evaluation. However, human annotations are expensive to obtain, sensitive to study design, and difficult to quality control, because of the subjective nature of emotions. Meanwhile, Large Language Models (LLMs) have shown remarkable performance on many Natural Language Understanding tasks, emerging as a promising tool for text annotation. In this work, we analyze the complexities of emotion annotation in the context of LLMs, focusing on GPT-4 as a leading model. In our experiments, GPT-4 achieves high ratings in a human evaluation study, painting a more positive picture than previous work, in which human labels served as the only ground truth. On the other hand, we observe differences between human and GPT-4 emotion perception, underscoring the importance of human input in annotation studies. To harness GPT-4's strength while preserving human perspective, we explore two ways of integrating GPT-4 into emotion annotation pipelines, showing its potential to flag low-quality labels, reduce the workload of human annotators, and improve downstream model learning performance and efficiency. Together, our findings highlight opportunities for new emotion labeling practices and suggest the use of LLMs as a promising tool to aid human annotation.

This paper presents a new construction of non-Anosov Partially Hyperbolic Geodesic flows. Our construction is closely related to the construction made by Carneiro and Pujals, the novelty is the use of conformal deformations to produce the examples. Some of the necessary conditions appear more naturally and are easier to check. Besides that, we could enumerate the conditions required to produce partially hyperbolic geodesic flow examples. We show how to produce examples with metrics that are non-positively curved and with a finner analysis we can prove ergodicity for the Liouville measure and uniqueness of the measure of maximal entropy. These examples lie on the boundary of Anosov metrics, allowing us to also produce metrics with partially hyperbolic geodesic flows and conjugate points., Comment: 34 pages

RMs (reverse micelles) provide a controlled system for studying the unique structural and dynamic behaviors of confined water, thus gaining a general insight into the behavior or water under confinement and at interfaces. This study employs ESR (Electron Spin Resonance) to study the dynamics inside RMs and capitalizes on the fact that different preparations of RMs can precisely manipulate both the size of water pools as well as whether or not different pools can come into contact. A small spin label moiety, less than a half nanometer in size tumbles in solution, offering insight into the rotational diffusion from room temperature down to deeply super-cooled temperatures. Two different spin probes can separately probe the dynamics of the interfacial layer vs. those at the core of the reverse micelle. The results provide critical insights into how thermodynamic transitions affect the stability and behavior of water in confined spaces, enhancing the understanding of nucleation and ice crystal growth, as well as the broader understanding of confined water's role in various chemical and biological systems. The ability to more precisely understand and control reaction dynamics and thermodynamics inside confined aqueous systems across a broad range of temperatures will prove useful to fields ranging from biophysics to the development of new nano-materials.

After the discovery of a cross-luminescence (CL) in BaF2 in 1982, a large number of CL scintillators were investigated. However, no CL scintillator superior to BaF2 has been discovered, and the research of CL scintillators has subsided. Recent technological development in medical imaging and high-energy physics created a new demand for ultra-fast scintillators further supported by the development of UV-sensitive semiconductor photodetectors. As a consequence, renewed interest in CL scintillators appeared. To satisfy the requirements of fast timing applications high photo-detection efficiency, e. i. a good spectral match between the scintillator and photodetector must be achieved. Cesium-based ternary chlorides could provide a red-shift (~1.5 eV) of CL towards the sensitive region of the photodetector (PMT or SiPM) while keeping light output and timing characteristics comparable to BaF2., Comment: 10 pages, 13 figures, 56 references

For the first time Lu$_2$S$_3$ (undoped and Pr-doped) single crystals were successfully grown from melt using micro-pulling-down (mPD) technique. Customization of halide mPD apparatus allowed us to grow rod-shaped (\O 2 mm and length around 20 mm) crystals of Lu$_2$S$_3$ with high melting temperature (~1750 $^\circ$C). X-ray powder diffraction revealed that the grown crystals exhibit the {\epsilon}-Lu$_2$S$_3$ crystal structure ($\alpha$-Al$_2$O$_3$ type, space group R-3c). Optical and scintillation properties of both the undoped and Pr$^{3+}$ doped Lu$_2$S$_3$ were investigated. Fast 5d-4f Pr$^{3+}$ luminescence was observed in both photoluminescence and radioluminescence spectra. The presented technology is an effective tool for the exploration of a large family of high-melting sulfides. Such materials show promise for application as scintillators, active laser media, and optoelectronic components., Comment: 7 pages, 9 figures, 34 references

The examination of the musculoskeletal system in dogs is a challenging task in veterinary practice. In this work, a novel method has been developed that enables efficient documentation of a dog's condition through a visual representation. However, since the visual documentation is new, there is no existing training data. The objective of this work is therefore to mitigate the impact of data scarcity in order to develop an AI-based diagnostic support system. To this end, the potential of synthetic data that mimics realistic visual documentations of diseases for pre-training AI models is investigated. We propose a method for generating synthetic image data that mimics realistic visual documentations. Initially, a basic dataset containing three distinct classes is generated, followed by the creation of a more sophisticated dataset containing 36 different classes. Both datasets are used for the pre-training of an AI model. Subsequently, an evaluation dataset is created, consisting of 250 manually created visual documentations for five different diseases. This dataset, along with a subset containing 25 examples. The obtained results on the evaluation dataset containing 25 examples demonstrate a significant enhancement of approximately 10% in diagnosis accuracy when utilizing generated synthetic images that mimic real-world visual documentations. However, these results do not hold true for the larger evaluation dataset containing 250 examples, indicating that the advantages of using synthetic data for pre-training an AI model emerge primarily when dealing with few examples of visual documentations for a given disease. Overall, this work provides valuable insights into mitigating the limitations imposed by limited training data through the strategic use of generated synthetic data, presenting an approach applicable beyond the canine musculoskeletal assessment domain.

Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval and processing times not only potentially enhances swift response and decision-making in critical scenarios but also supports interactive surgical planning and navigation. Recent methods attempt to solve the medical shape reconstruction problem by utilizing implicit neural functions. However, their performance suffers in terms of generalization and computation time, a critical metric for real-time applications. To address these challenges, we propose to leverage meta-learning to improve the network parameters initialization, reducing inference time by an order of magnitude while maintaining high accuracy. We evaluate our approach on three public datasets covering different anatomical shapes and modalities, namely CT and MRI. Our experimental results show that our model can handle various input configurations, such as sparse slices with different orientations and spacings. Additionally, we demonstrate that our method exhibits strong transferable capabilities in generalizing to shape domains unobserved at training time.

In this paper, we prove Ruelle's inequality for the geodesic flow in non-compact manifolds with Anosov geodesic flow and some assumptions on the curvature. In the same way, we obtain Pesin's formula for Anosov geodesic flow in non-compact manifolds with finite volume.

Synthesizing 3D whole bodies that realistically grasp objects is useful for animation, mixed reality, and robotics. This is challenging, because the hands and body need to look natural w.r.t. each other, the grasped object, as well as the local scene (i.e., a receptacle supporting the object). Moreover, training data for this task is really scarce, while capturing new data is expensive. Recent work goes beyond finite datasets via a divide-and-conquer approach; it first generates a "guiding" right-hand grasp, and then searches for bodies that match this. However, the guiding-hand synthesis lacks controllability and receptacle awareness, so it likely has an implausible direction (i.e., a body can't match this without penetrating the receptacle) and needs corrections through major post-processing. Moreover, the body search needs exhaustive sampling and is expensive. These are strong limitations. We tackle these with a novel method called CWGrasp. Our key idea is that performing geometry-based reasoning "early on," instead of "too late," provides rich "control" signals for inference. To this end, CWGrasp first samples a plausible reaching-direction vector (used later for both the arm and hand) from a probabilistic model built via ray-casting from the object and collision checking. Moreover, CWGrasp uniquely tackles both right and left-hand grasps. We evaluate on the GRAB and ReplicaGrasp datasets. CWGrasp outperforms baselines, at lower runtime and budget, while all components help performance. Code and models are available at https://gpaschalidis.github.io/cwgrasp., Comment: 3DV 2025

Novel red-emitting scintillator Li$_2$MnCl$_4$ is proposed as a candidate for thermal neutron detection. It features high Li content, low density, low effective atomic number, and emission in red-NIR region. These characteristics make it an interesting candidate for long distance neutron detection in harsh enviroments e. g. decomisioning of nuclear powerplants. The absorption spectrum is thoroughly investigated in the scope of Tanabe-Sugano diagram. Luminescence mechanism in the undoped Li$_2$MnCl$_4$ is studied in depth. Doping by Eu$^{2+}$ and Ce$^{3+}$ is introduced as a trial to improve the scintillation efficiency. We show in the Eu$^{2+}$ and Ce$^{3+}$ doped Li$_2$MnCl$_4$ that luminescence mechanism involves energy transfer from the dopants to Mn$^{2+}$, and propose the local lattice distortion around the dopant and a possible charge compensation mechanisms., Comment: 8 pages, 10 figures, 2 tables, 26 references

Fast emitting polymeric scintillators are requested in advanced applications where high-speed detectors with large signal-to-noise ratio are needed. However, their low density implies a weak stopping power of high energy radiations, thus a limited light output and sensitivity. To enhance their performances, polymeric scintillators can be loaded with dense nanoparticles (NPs). We investigate the properties of a series of polymeric scintillators by means of photoluminescence and scintillation spectroscopy, comparing standard scintillators with a composite system loaded with dense hafnium dioxide (HfO2) NPs. The nanocomposite shows a scintillation yield enhancement of +100% with unchanged time response. We provide for the first time an interpretation of this effect, pointing out the local effect of NPs in the generation of emissive states upon interaction with the ionizing radiation. The obtained results indicate that coupling of fast conjugated emitters with optically inert dense NPs could allow to surpass the actual limits of pure polymeric scintillators., Comment: 14 pages, 4 figures

This is the third in a series of three papers in which we study a lattice gas subject to Kawasaki dynamics at inverse temperature $\beta>0$ in a large finite box $\Lambda_\beta \subset \mathbb{Z}^2$ whose volume depends on $\beta$. Each pair of neighbouring particles has a negative binding energy $-U<0$, while each particle has a positive activation energy $\Delta>0$. The initial configuration is drawn from the grand-canonical ensemble restricted to the set of configurations where all the droplets are subcritical. Our goal is to describe, in the metastable regime $\Delta \in (U,2U)$ and in the limit as $\beta\to\infty$, how and when the system nucleates, i.e., creates a critical droplet somewhere in $\Lambda_\beta$ that subsequently grows by absorbing particles from the surrounding gas. In the first paper we showed that subcritical droplets behave as quasi-random walks. In the second paper we used the results in the first paper to analyse how subcritical droplets form and dissolve on multiple space-time scales when the volume is moderately large, namely, $|\Lambda_\beta| = \mathrm{e}^{\theta\beta}$ with $\Delta < \theta < 2\Delta-U$. In the present paper we consider the setting where the volume is very large, namely, $|\Lambda_\beta| = \mathrm{e}^{\Theta\beta}$ with $\Delta < \Theta < \Gamma-(2\Delta-U)$, where $\Gamma$ is the energy of the critical droplet in the local model with fixed volume, and use the results in the first two papers to identify the nucleation time and the tube of typical trajectories towards nucleation. We will see that in a very large volume critical droplets appear more or less independently in boxes of moderate volume, a phenomenon referred to as homogeneous nucleation. One of the key ingredients in the proof is an estimate showing that no information can travel between these boxes on relevant time scales., Comment: 21 pages, 5 figures. arXiv admin note: text overlap with arXiv:2304.14099

Diamond is increasingly popular because of its unique material properties. Diamond defects called nitrogen vacancy (NV) centers allow measurements with unprecedented sensitivity. However, to achieve ideal sensing performance NV centers need to be within nanometers from the surface and are thus strongly dependent on the local surface chemistry. Several attempts have been made to compare diamond surfaces. However, due to the high price of diamond crystals with shallow NV centers, a limited number of chemical modifications have been studied. Here, we developed a systematic method to investigate a continuity of different local environments with a varying density and nature of surface groups in a single experiment on a single diamond plate. To achieve this goal, we used diamonds with a shallow ensemble of NV centers and introduced a chemical gradient across the surface. More specifically we used air and hydrogen plasma. The gradients were formed by low pressure plasma treatment after masking with a right-angled triangular prism shield. As a result, the surface contained gradually more oxygen/hydrogen towards the open end of the shield. We then performed widefield relaxometry to determine the effect of surface chemistry on the sensing performance. As expected, relaxation times and thus sensing performance indeed varies along the gradient., Comment: Main text: 18 pages and 9 figures Supplementary materials: 6 pages and 6 figures

A bit catastrophe, loosely defined, is when a change in just one character of a string causes a significant change in the size of the compressed string. We study this phenomenon for the Burrows-Wheeler Transform (BWT), a string transform at the heart of several of the most popular compressors and aligners today. The parameter determining the size of the compressed data is the number of equal-letter runs of the BWT, commonly denoted $r$. We exhibit infinite families of strings in which insertion, deletion, resp. substitution of one character increases $r$ from constant to $\Theta(\log n)$, where $n$ is the length of the string. These strings can be interpreted both as examples for an increase by a multiplicative or an additive $\Theta(\log n)$-factor. As regards multiplicative factor, they attain the upper bound given by Akagi, Funakoshi, and Inenaga [Inf & Comput. 2023] of $O(\log n \log r)$, since here $r=O(1)$. We then give examples of strings in which insertion, deletion, resp. substitution of a character increases $r$ by a $\Theta(\sqrt{n})$ additive factor. These strings significantly improve the best known lower bound for an additive factor of $\Omega(\log n)$ [Giuliani et al., SOFSEM 2021]., Comment: This work is an extended version of our conference article with the same title, published in the proceedings of DLT 2023