Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflationary models constrained by reheating

The study of reheating in inflationary models is crucial for understanding the early universe and gaining insights into inflationary dynamics and parameters. The reheating temperature $T_{re}$ and the duration of the reheating phase, quantified by the number of $e$-folds $N_{re}$, have significant implications for particle production, thermalization, and the primordial power spectrum. The duration of reheating affects particle abundance, including dark matter, and shapes the primordial power spectrum and cosmic microwave background anisotropies. By combining cosmological observations and theoretical considerations, we can constrain both $T_{re}$ and $N_{re}$, which in turn constrain the spectral index $n_s$, tensor-to-scalar ratio $r$, and inflation model parameters. Utilizing consistency relations among observables, such as $n_s$ and $r$, provides additional constraints on inflationary models and determines bounds for other observables like the running of the scalar spectral index. These bounds are valuable for assessing the viability of models and can serve to specify priors in Bayesian analyses of specific models. As an example of how to proceed, we study in detail a particular case of a generalized $\alpha$-attractor model that accurately reproduces observed quantities. We present equations for the conditions of instantaneous reheating, establish consistency relations, and explore the generalized $\alpha$-attractor model using cosmological data.
Finite groups of symplectic birational transformations of IHS manifolds of $OG10$ type

We classify finite groups that act faithfully by symplectic birational transformations on an irreducible holomorphic symplectic (IHS) manifold of $OG10$ type. In particular, up to deformation there are 367 birational conjugacy classes of pairs $(X,G)$, consisting of an IHS manifold $X$ of $OG10$ type and a saturated, finite subgroup $G\leq \mathrm{Bir}_s(X)$. We determine the action of $G$ on $H^2(X,\mathbb{Z})$ for each case. We prove a criterion for when such a group is determined by a group of automorphisms acting on a cubic fourfold, and apply it to our classification. Our proof is computer aided.
Photon-number resolution with microwave Josephson photomultipliers

We study counting photons confined in a mode of a microwave resonator via repeated measurements by a Josephson photomultiplier (JPM). The considered JPM is essentially a flux-biased phase qubit operating as a single-photon detector. We identify optimal operational regimes that maximize photon-number resolution within a predetermined range. Two counting techniques are studied. The first is to count the total number of clicks in the measurement sequence. The second involves counting the number of clicks until the occurrence of either the first no-click event or the end of the measurement sequence. Our theoretical methods employ the derived positive operator-valued measures for the considered photocounting techniques and the introduced measure of the photon-number resolution. The results reveal that the resolution decrease in both cases is mainly caused by the JPM relaxation. As an example, we show how the obtained results can be used for practical testing nonclassical properties of electromagnetic radiation in a microwave resonator.
Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond

We propose task-adaptive tokenization as a way to adapt the generation pipeline to the specifics of a downstream task and enhance long-form generation in mental health. Inspired by insights from cognitive science, our task-adaptive tokenizer samples variable segmentations from multiple outcomes, with sampling probabilities optimized based on task-specific data. We introduce a strategy for building a specialized vocabulary and introduce a vocabulary merging protocol that allows for the integration of task-specific tokens into the pre-trained model's tokenization step. Through extensive experiments on psychological question-answering tasks in both Chinese and English, we find that our task-adaptive tokenization approach brings a significant improvement in generation performance while using up to 60% fewer tokens. Preliminary experiments point to promising results when using our tokenization approach with very large language models.
Predictable Artificial Intelligence

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance. While distinctive from other areas of technical and non-technical AI research, the questions, hypotheses and challenges relevant to Predictable AI were yet to be clearly described. This paper aims to elucidate them, calls for identifying paths towards AI predictability and outlines the potential impact of this emergent field.
Autoregressive Neural Quantum States with Quantum Number Symmetries

Neural quantum states have established themselves as a powerful and versatile family of ansatzes for variational Monte Carlo simulations of quantum many-body systems. Of particular prominence are autoregressive neural quantum states (ANQS), which enjoy the expressibility of deep neural networks, and are equipped with a procedure for fast and unbiased sampling. Yet, the non-selective nature of autoregressive sampling makes incorporating quantum number symmetries challenging. In this work, we develop a general framework to make the autoregressive sampling compliant with an arbitrary number of quantum number symmetries. We showcase its advantages by running electronic structure calculations for a range of molecules with multiple symmetries of this kind. We reach the level of accuracy reported in previous works with more than an order of magnitude speedup and achieve chemical accuracy for all studied molecules, which is a milestone unreported so far. Combined with the existing effort to incorporate space symmetries, our approach expands the symmetry toolbox essential for any variational ansatz and brings the ANQS closer to being a competitive choice for studying challenging quantum many-body systems.
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory

Since their inception, Variational Autoencoders (VAEs) have become central in machine learning. Despite their widespread use, numerous questions regarding their theoretical properties remain open. Using PAC-Bayesian theory, this work develops statistical guarantees for VAEs. First, we derive the first PAC-Bayesian bound for posterior distributions conditioned on individual samples from the data-generating distribution. Then, we utilize this result to develop generalization guarantees for the VAE's reconstruction loss, as well as upper bounds on the distance between the input and the regenerated distributions. More importantly, we provide upper bounds on the Wasserstein distance between the input distribution and the distribution defined by the VAE's generative model.
Energy Management in a Cooperative Energy Harvesting Wireless Sensor Network

In this letter, we consider the problem of finding an optimal energy management policy for a network of sensor nodes capable of harvesting their own energy and sharing it with other nodes in the network. We formulate this problem in the discounted cost Markov decision process framework and obtain good energy-sharing policies using the Deep Deterministic Policy Gradient (DDPG) algorithm. Earlier works have attempted to obtain the optimal energy allocation policy for a single sensor and for multiple sensors arranged on a mote with a single centralized energy buffer. Our algorithms, on the other hand, provide optimal policies for a distributed network of sensors individually harvesting energy and capable of sharing energy amongst themselves. Through simulations, we illustrate that the policies obtained by our DDPG algorithm using this enhanced network model outperform algorithms that do not share energy or use a centralized energy buffer in the distributed multi-nodal case.
Generating spectral dental panoramic images from single energy computed tomography volumes

Purpose: To implement a framework generating synthetic spectral panoramic images from single energy CT volumes. Using the framework output to compare the synthetic images against experimental spectral panoramic images for cross-verification. Methods: A simulation framework for generating synthetic spectral panoramic images from CT volumes is described. A cone beam CT scan of an anthropomorphic head phantom is used as input. An experimental spectral panoramic image of the same phantom is acquired. Results: The output of the framework of an anthropomorphic head phantom is compared against an experimental spectral panoramic image of the same phantom. The synthetic and experimental spectral panoramic images resemble each other considerably, especially the bone features. In the soft tissue images, there are some deviations, which are a result of the differences between the experimental and synthetic processing pipelines. Conclusions: It is demonstrated that generating synthetic spectral panoramic images from single energy CT volumes is possible. The synthetic images have many similarities with the experimental results, increasing the confidence in the correctness of the information contained within experimental spectral panoramic images and indicating that the synthetic images could be useful in further research.
The Pleistocene Epoch in England

Summary A review of the evidence on the position of the boundary line between the Pliocene and Pleistocene is given. This leads to the acceptance of a boundary in England berween the Coralline Crag and the Waltonian division of the Red Crag. A plea is made for basing the boundary on stratigraphical rather than climatographical considerations. Since the evidence from the Midlands appears to be much more clear-cut than that in East Anglia, the former is taken as a starting point. The stratigraphy in the Thames basin is then considered in the light of the Midland deposits; the question of the succession and age of the oldest drifts raises problems clearly needing elucidation. These are further considered in connexion with the East Anglian drifts, where the importance of recent work is emphasized. The problems of the Newer Drift in the northern parts of England are reviewed. It is suggested that in the Midlands and northern England the Corbicula-Hippopotamus fauna probably occurs at only one horizon and that this lies between the Older and the Newer Drifts. It is suggested that more objective thinking would be possible if the Alpine nomenclature of Penck & Brückner were not used in Britain, and that it might help if a committee were set up, not to try to formulate correlations, but to propound an agreed nomenclature for the English divisions of the Pleistocene deposits.
T-Rep: Representation Learning for Time Series using Time-Embeddings

Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this, we propose T-Rep, a self-supervised method to learn time series representations at a timestep granularity. T-Rep learns vector embeddings of time alongside its feature extractor, to extract temporal features such as trend, periodicity, or distribution shifts from the signal. These time-embeddings are leveraged in pretext tasks, to incorporate smooth and fine-grained temporal dependencies in the representations, as well as reinforce robustness to missing data. We evaluate T-Rep on downstream classification, forecasting, and anomaly detection tasks. It is compared to existing self-supervised algorithms for time series, which it outperforms in all three tasks. We test T-Rep in missing data regimes, where it proves more resilient than its counterparts. Finally, we provide latent space visualisation experiments, highlighting the interpretability of the learned representations.
Dinoflagellate cysts from the Szlachtowa Formation (Jurassic) and adjacent deposits (Jurassic–Cretaceous) of the Grajcarek Unit at Szczawnica-Zabaniszcze (Pieniny Klippen Belt, Carpathians, Poland)

The Jurassic–Cretaceous fine-clastic dark deposits of the Grajcarek Unit (Pieniny Klippen Belt, Carpathians, Poland) show lithological similarities, which may lead to erroneous age correlations. Their well-exposed outcrop at Szczawnica-Zabaniszcze allows detailed sampling for their precise age determination. For this purpose, dinoflagellate cysts have been studied from a set of samples collected from the Szlachtowa, Opaleniec, Pieniny Limestone, Kapuśnica and Wronine formations exposed at that site (additional sample from the Pieniny Limestone Formation was studied). The Szlachtowa Formation, a very characteristic lithostratigraphic unit, which consists of black shale and sandstones extremely rich in mica flakes, occasionally referred to literature as the so-called “black flysch”, yielded rich and well-preserved assemblages. Their age is Middle Jurassic, spanning the Aalenian to Lower Bajocian. The exposures of the Szlachtowa Formation at Szczawnica-Zabaniszcze are located close to the outcrops of the Opaleniec Formation. The calcareous spotted shale from this lithostratigraphic unit yielded rich dinoflagellate cyst assemblage characteristic for Late Bathonian (Early Callovian? ). The Opaleniec Formation tectonically contacts with very similar non-calcareous spotted shale of the Wronine Formation. Its age, based on infrequent dinoflagellate cysts is Aptian–Albian. Similar, Lower Aptian age was concluded for the Kapuśnica Formation. Late Barremian–earliest Aptian dinoflagellate cysts have been found in the underlying topmost part of the Pieniny Limestone Formation.
Wet scavenging of sulfur compounds and other constituents during the Indian Ocean Experiment (INDOEX)

[1] Rainwater samples for chemical analysis were collected over the Indian Ocean during the Indian Ocean Experiment (INDOEX) campaign January–March 1999 on board the research vessels Ronald H. Brown and Sagar Kanya. Samples were analyzed for major ions and some trace metals. The rainwater data are interpreted in terms of transport from potential source regions in Asia using air mass trajectories covering 10 days. A comparison is also made between the rainwater data and the concentration of aerosol components measured simultaneously on the ships. The concentrations of nonsea-salt (nss)-SO42−, NO3−, NH4+, nss-K+, and nss-Ca2+ in rainwater over the Indian Ocean, while a factor of 2 to 3 lower than over the Indian continent, were still clearly influenced by pollution and soil sources in Asia. The concentration of nss-Ca2+ decreased more rapidly as the air moved southward from the continent out over the ocean, whereas the concentration of nss-SO42− became relatively more abundant. This was consistent with the observed higher acidity of the rainwater over the ocean (pH in the range 4.8 to 5.4) than over the Indian subcontinent, with NH4+ as the main cation (rather than Ca2+, as over land). Variations in the concentration of Al and Fe correlated well with those of nss-Ca2+, indicating a crustal source for these elements. The relation between Na+, Cl−, and Br− in the rainwater was close to that of seawater, implying no excess or deficit of the two halogen ions. The ratio between the concentration in rainwater and the concentration in surface air was systematically larger for aerosol components that exist in the coarse mode of sea-salt origin (Na+, Mg2+, and Cl−) than those in the fine mode (NH4+, nss-K+, and nss-SO42−), indicating that fine-mode particles are scavenged mainly by in-cloud processes whereas coarse-mode sea-salt particles are scavenged also by falling raindrops under the clouds. Nss-Ca2+ and NO3− fall in a category in between, indicating that these compounds are not as effectively removed by below-cloud scavenging as sea-salt aerosols.
Low-Resolution Self-Attention for Semantic Segmentation

Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction. While existing vision transformers demonstrate promising performance, they often utilize high resolution context modeling, resulting in a computational bottleneck. In this work, we challenge conventional wisdom and introduce the Low-Resolution Self-Attention (LRSA) mechanism to capture global context at a significantly reduced computational cost. Our approach involves computing self-attention in a fixed low-resolution space regardless of the input image's resolution, with additional 3x3 depth-wise convolutions to capture fine details in the high-resolution space. We demonstrate the effectiveness of our LRSA approach by building the LRFormer, a vision transformer with an encoder-decoder structure. Extensive experiments on the ADE20K, COCO-Stuff, and Cityscapes datasets demonstrate that LRFormer outperforms state-of-the-art models. The code will be made available at https://github.com/yuhuan-wu/LRFormer.
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention. Nevertheless, previous studies in this domain exhibit certain limitations, as they often lack theoretical insights or their methodologies are tied to specific network architectures or modalities. We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective and illustrate that the performance ceiling in such scenarios can be approached by efficiently utilizing the information inherent in non-missing modalities. In practice, there are two key aspects: (1) The encoder should be able to extract sufficiently good features from the non-missing modality; (2) The extracted features should be robust enough not to be influenced by noise during the fusion process across modalities. To this end, we introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA). UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities. Apart from that, UME-MMA, built on a late-fusion learning framework, allows for the plug-and-play use of various encoders, making it suitable for a wide range of modalities and enabling seamless integration of large-scale pre-trained encoders to further enhance performance. And we demonstrate UME-MMA's effectiveness in audio-visual datasets~(e.g., AV-MNIST, Kinetics-Sound, AVE) and vision-language datasets~(e.g., MM-IMDB, UPMC Food101).
Zero-Level-Set Encoder for Neural Distance Fields

Neural shape representation generally refers to representing 3D geometry using neural networks, e.g., to compute a signed distance or occupancy value at a specific spatial position. In this paper, we present a novel encoder-decoder neural network for embedding 3D shapes in a single forward pass. Our architecture is based on a multi-scale hybrid system incorporating graph-based and voxel-based components, as well as a continuously differentiable decoder. Furthermore, the network is trained to solve the Eikonal equation and only requires knowledge of the zero-level set for training and inference. This means that in contrast to most previous work, our network is able to output valid signed distance fields without explicit prior knowledge of non-zero distance values or shape occupancy. We further propose a modification of the loss function in case that surface normals are not well defined, e.g., in the context of non-watertight surfaces and non-manifold geometry. Overall, this can help reduce the computational overhead of training and evaluating neural distance fields, as well as enabling the application to difficult shapes. We finally demonstrate the efficacy, generalizability and scalability of our method on datasets consisting of deforming shapes, both based on simulated data and raw 3D scans. We further show single-class and multi-class encoding, on both fixed and variable vertex-count inputs, showcasing a wide range of possible applications.
CATTLE FEEDING MANAGEMENT OF PASTORALISTS AND CONFLICT RESOLUTION STRATEGIES IN THE TROPICAL HUMID RAIN FOREST ZONE OF SOUTH EASTERN NIGERIA

This study investigated feeding management of pastoralist cattle and conflict frequencies an resolution strategies in the rainforest zone of Nigeria using primary data generated through questionnaires, interviews and observations. About 77% of cattle holdings were White Fulani breed produced for dual purposes. The pastoralists allowed calves to start suckling from 0 8 weeks and for six to nine months. Mature animals depended only on natural pastures for nutrient needs, salt lick was supplemented and water obtained directly streams, rivers and stagnant ponds. The diversity of ever green forages made all year round feed supply possible. About, 81% of pastoralists had conflicts with indigenous crop farmers because of animals straying to destroy crops during herding. Another 13.60% had cases of animal theft, with conflicts being resolved through village heads, town authorities and the army. Conflicts threaten pastoral access to resources, thus, impacting negatively on sustainability of pastoralism in this forest zones.
Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques

Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amount of data. Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques. This is surprising as the learning behaviour of MAML mimics that of finetuning: both rely on re-using learned features. We investigate the observed performance differences between finetuning, MAML, and another meta-learning technique called Reptile, and show that MAML and Reptile specialize for fast adaptation in low-data regimes of similar data distribution as the one used for training. Our findings show that both the output layer and the noisy training conditions induced by data scarcity play important roles in facilitating this specialization for MAML. Lastly, we show that the pre-trained features as obtained by the finetuning baseline are more diverse and discriminative than those learned by MAML and Reptile. Due to this lack of diversity and distribution specialization, MAML and Reptile may fail to generalize to out-of-distribution tasks whereas finetuning can fall back on the diversity of the learned features.