Miracle-dz/UniFAR-FTUs huggingface dataset:
A new ground-based FTIR spectrometer reference site at Shadnagar (India) and preliminary columnar retrievals of CH4 and N2O

ABSTRACT A Fourier Transform Infrared (FTIR) spectrometer has been operational since March 2014, at National Remote Sensing Centre (NRSC), Shadnagar, which is a suburban site of central India. Direct solar radiation spectra recorded by the FTIR during clear sky days at a high spectral resolution of 0.01 cm‒1 were analysed using the line-by-line radiative transfer algorithm (LBLRTA) to retrieve the initial results of column-averaged concentrations of methane (XCH4) and nitrous oxide (XN2O) in the atmosphere. Error residuals between the measured and estimated atmospheric transmissions are in the range of 0.40–0.80% for CH4 and 0.20–0.50% for N2O, respectively. The maximum (minimum) XCH4 and XN2O were, respectively, 1.88 ± 0.04 ppm (1.67 ± 0.02 ppm) and 316.15 ± 5.85 ppb (278.92 ± 4.02 ppb). Columnar concentrations of CH4 and N2O retrieved from the Infrared Atmospheric Sounding Interferometer (IASI) were compared against retrieved XCH4 and XN2O using the ground FTIR. The observed differences between the two data sets lie between −0.06 ppm and +0.10 ppm for CH4 and between +32 ppb and ‒10 ppb for N2O.
Tip dating and Bayes factors provide insight into the divergences of crown bird clades across the end-Cretaceous mass extinction

The origin of crown birds (Neornithes) remains contentious owing to conflicting divergence time hypotheses obtained from alternative sources of data. The fossil record suggests limited diversification of Neornithes in the Late Mesozoic and a substantial radiation in the aftermath of the Cretaceous–Palaeogene (K–Pg) mass extinction, approximately 66 Ma. Molecular clock studies, however, have yielded estimates for neornithine origins ranging from the Early Cretaceous (130 Ma) to less than 10 Myr before the K–Pg. We use Bayes factors to compare the fit of node ages from different molecular clock studies to an independent morphological dataset. Our results allow us to reject scenarios of crown bird origins deep in the Early Cretaceous, as well as an origin of crown birds within the last 10 Myr of the Cretaceous. The scenario best supported by our analyses is one where Neornithes originated between the Early and Late Cretaceous (ca 100 Ma), while numerous divergences within major neoavian clades either span or postdate the K–Pg. This study affirms the importance of the K–Pg on the diversification of modern birds, and the potential of combined-evidence tip-dating analyses to illuminate recalcitrant ‘rocks versus clocks’ debates.
Effects of psychedelic microdosing versus conventional ADHD medication use on emotion regulation, empathy, and ADHD symptoms in adults with severe ADHD symptoms: A naturalistic prospective comparison study

Adults with attention-deficit hyperactivity disorder (ADHD) often struggle with emotion regulation (ER), impacting their empathic skills and relationships. ADHD medication might not be as effective for ER issues as for ADHD symptoms. Microdosing (MD) psychedelics has shown promise for ADHD treatment and previous studies reported social-emotional benefits. Two online prospective studies investigated MD effects on ER and empathy in adults with severe ADHD symptoms across three assessments: baseline, two-, and four-week post-initiation. Study 1 examined adults initiating MD on their own (n = 233, n = 64, and n = 44) and found positive effects on ER (cognitive reappraisal and expressive suppression) and aspects of empathy (perspective-taking and personal distress). Study 2, including a control group and an ADHD symptom scale, compared individuals only MD (n = 180, n = 50, and n = 38) to individuals using conventional ADHD medication (n = 37, n = 27, and n = 28). After 4 weeks, ADHD symptoms were lower in the MD group. Only improvements in expressive suppression persisted after adding the control group. This study indicates the positive effects of MD psychedelics on ADHD symptoms and ER in adults with severe ADHD symptoms while lacking evidence for effects on empathy.
Projected Changes in Streamflow and Sea Level Rise in the Kilchis and Miami river basins: Summary of existing data and literature

The Nature Conservancy (TNC) is working to restore two tidal wetland properties along the Kilchis and Miami Rivers, located within the Tillamook bay in Oregon. Ultimately, the goal of these restoration efforts is to reestablish key habitats and foster a resilient ecosystem that is robust to future changes in climate. The two properties had previously been diked off from river and estuarine flow and converted to farmland – planned restoration efforts include removal of dikes and levees, restoration of river channels and floodplains, and creating suitable habitat for native species.
Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots

Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process -- fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.
ScamSpot: Fighting Financial Fraud in Instagram Comments

The long-standing problem of spam and fraudulent messages in the comment sections of Instagram pages in the financial sector claims new victims every day. Instagram’s current spam filter proves inadequate, and existing research approaches are primarily confined to theoretical concepts. Practical implementations with evaluated results are missing. To solve this problem, we propose ScamSpot, a comprehensive system that includes a browser extension, a fine-tuned BERT model and a REST API. This approach ensures public accessibility of our results for Instagram users using the Chrome browser. Furthermore, we conduct a data annotation study, shedding light on the reasons and causes of the problem and evaluate the system through user feedback and comparison with existing models. ScamSpot is an open-source project and is publicly available at https://scamspot.github. io/.
Bolometric detection of Josephson radiation

A Josephson junction (JJ) has been under intensive study ever since 1960's. Yet even in the present era of building quantum information processing devices based on many JJs, open questions regarding a single junction remain unsolved, such as quantum phase transitions, coupling of the JJ to an environment and improving coherence of a superconducting qubit. Here we design and build an engineered on-chip reservoir that acts as an efficient bolometer for detecting the Josephson radiation under non-equilibrium (biased) conditions. The bolometer converts ac Josephson current at microwave frequencies, up to about $100\,$GHz, into a measurable dc temperature rise. The present experiment demonstrates an efficient, wide-band, thermal detection scheme of microwave photons and provides a sensitive detector of Josephson dynamics beyond the standard conductance measurements. Using a circuit model, we capture both the current-voltage characteristics and the measured power quantitatively.
BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

In the process of evaluating competencies for job or student recruitment through material screening, decision-makers can be influenced by inherent cognitive biases, such as the screening order or anchoring information, leading to inconsistent outcomes. To tackle this challenge, we conducted interviews with seven experts to understand their challenges and needs for support in the screening process. Building on their insights, we introduce BiasEye, a bias-aware real-time interactive material screening visualization system. BiasEye enhances awareness of cognitive biases by improving information accessibility and transparency. It also aids users in identifying and mitigating biases through a machine learning (ML) approach that models individual screening preferences. Findings from a mixed-design user study with 20 participants demonstrate that, compared to a baseline system lacking our bias-aware features, BiasEye increases participants' bias awareness and boosts their confidence in making final decisions. At last, we discuss the potential of ML and visualization in mitigating biases during human decision-making tasks.
Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning

Deep reinforcement learning has demonstrated remarkable achievements across diverse domains such as video games, robotic control, autonomous driving, and drug discovery. Common methodologies in partially-observable domains largely lean on end-to-end learning from high-dimensional observations, such as images, without explicitly reasoning about true state. We suggest an alternative direction, introducing the Partially Supervised Reinforcement Learning (PSRL) framework. At the heart of PSRL is the fusion of both supervised and unsupervised learning. The approach leverages a state estimator to distill supervised semantic state information from high-dimensional observations which are often fully observable at training time. This yields more interpretable policies that compose state predictions with control. In parallel, it captures an unsupervised latent representation. These two-the semantic state and the latent state-are then fused and utilized as inputs to a policy network. This juxtaposition offers practitioners a flexible and dynamic spectrum: from emphasizing supervised state information to integrating richer, latent insights. Extensive experimental results indicate that by merging these dual representations, PSRL offers a potent balance, enhancing model interpretability while preserving, and often significantly outperforming, the performance benchmarks set by traditional methods in terms of reward and convergence speed.
Approximate Sequential Optimization for Informative Path Planning

We consider the problem of finding an informative path through a graph, given initial and terminal nodes and a given maximum path length. We assume that a linear noise corrupted measurement is taken at each node of an underlying unknown vector that we wish to estimate. The informativeness is measured by the reduction in uncertainty in our estimate, evaluated using several metrics. We present a convex relaxation for this informative path planning problem, which we can readily solve to obtain a bound on the possible performance. We develop an approximate sequential method where the path is constructed segment by segment through dynamic programming. This involves solving an orienteering problem, with the node reward acting as a surrogate for informativeness, taking the first step, and then repeating the process. The method scales to very large problem instances and achieves performance not too far from the bound produced by the convex relaxation. We also demonstrate our method's ability to handle adaptive objectives, multimodal sensing, and multi-agent variations of the informative path planning problem.
Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method

This paper proposes a stochastic proximal point method to solve a stochastic convex composite optimization problem. High probability results in stochastic optimization typically hinge on restrictive assumptions on the stochastic gradient noise, for example, sub-Gaussian distributions. Assuming only weak conditions such as bounded variance of the stochastic gradient, this paper establishes a low sample complexity to obtain a high probability guarantee on the convergence of the proposed method. Additionally, a notable aspect of this work is the development of a subroutine to solve the proximal subproblem, which also serves as a novel technique for variance reduction.
Multi-Label Zero-Shot Product Attribute-Value Extraction

E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values, large quantities of labeled training data are needed to train a traditional supervised learning model. Typically, it is difficult, time-consuming, and costly to manually label large quantities of new product profiles. In this paper, we propose a novel method to efficiently and effectively extract unseen attribute values from new products in the absence of labeled data (zero-shot setting). We propose HyperPAVE, a multi-label zero-shot attribute value extraction model that leverages inductive inference in heterogeneous hypergraphs. In particular, our proposed technique constructs heterogeneous hypergraphs to capture complex higher-order relations (i.e. user behavior information) to learn more accurate feature representations for graph nodes. Furthermore, our proposed HyperPAVE model uses an inductive link prediction mechanism to infer future connections between unseen nodes. This enables HyperPAVE to identify new attribute values without the need for labeled training data. We conduct extensive experiments with ablation studies on different categories of the MAVE dataset. The results demonstrate that our proposed HyperPAVE model significantly outperforms existing classification-based, generation-based large language models for attribute value extraction in the zero-shot setting.
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models

Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However, in the vision domain, existing RL-based reward finetuning methods are limited by their instability in large-scale training, rendering them incapable of generalizing to complex, unseen prompts. In this paper, we propose Proximal Reward Difference Prediction (PRDP), enabling stable black-box reward finetuning for diffusion models for the first time on large-scale prompt datasets with over 100K prompts. Our key innovation is the Reward Difference Prediction (RDP) objective that has the same optimal solution as the RL objective while enjoying better training stability. Specifically, the RDP objective is a supervised regression objective that tasks the diffusion model with predicting the reward difference of generated image pairs from their denoising trajectories. We theoretically prove that the diffusion model that obtains perfect reward difference prediction is exactly the maximizer of the RL objective. We further develop an online algorithm with proximal updates to stably optimize the RDP objective. In experiments, we demonstrate that PRDP can match the reward maximization ability of well-established RL-based methods in small-scale training. Furthermore, through large-scale training on text prompts from the Human Preference Dataset v2 and the Pick-a-Pic v1 dataset, PRDP achieves superior generation quality on a diverse set of complex, unseen prompts whereas RL-based methods completely fail.
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments

Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing the reconstructed and synthetic scenes at unlimited resolution. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the problem of large-scale 3D scene reconstruction and novel view synthesis using sparse LiDAR frames remains unexplored. To bridge this gap, we propose a 3D scene reconstruction and novel view synthesis framework called parent-child neural radiance field (PC-NeRF). Based on its two modules, parent NeRF and child NeRF, the framework implements hierarchical spatial partitioning and multi-level scene representation, including scene, segment, and point levels. The multi-level scene representation enhances the efficient utilization of sparse LiDAR point cloud data and enables the rapid acquisition of an approximate volumetric scene representation. With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively handle situations with sparse LiDAR frames and demonstrate high deployment efficiency with limited training epochs. Our approach implementation and the pre-trained models are available at https://github.com/biter0088/pc-nerf.
Computational Complexity of Preferred Subset Repairs on Data-Graphs

The problem of repairing inconsistent knowledge bases has a long history within the communities of database theory and knowledge representation and reasoning, especially from the perspective of structured data. However, as the data available in real-world domains becomes more complex and interconnected, the need naturally arises for developing new types of repositories, representation languages, and semantics, to allow for more suitable ways to query and reason about it. Graph databases provide an effective way to represent relationships among semi-structured data, and allow processing and querying these connections efficiently. In this work, we focus on the problem of computing prioritized repairs over graph databases with data values, using a notion of consistency based on Reg-GXPath expressions as integrity constraints. We present several preference criteria based on the standard subset repair semantics, incorporating weights, multisets, and set-based priority levels. We study the most common repairing tasks, showing that it is possible to maintain the same computational complexity as in the case where no preference criterion is available for exploitation. To complete the picture, we explore the complexity of consistent query answering in this setting and obtain tight lower and upper bounds for all the preference criteria introduced.
Textural properties of dense granular pastes produced by kneading

The efficiency of supported catalysts depends on the porous microstructure of their solid support, which regulates mass transfer, exposure to the active phase, and mechanical strength. Here, we focus on the manufacturing of a specific type of catalytic support, $\gamma$-alumina extrudates, by a kneading-extrusion process. In this process, a paste is initially formed by blending and subsequently kneading a boehmite powder, an aluminum oxide hydroxide precursor of $\gamma$-alumina, with various liquids before undergoing extrusion. The crucial step in this process is the kneading step, which allows control over the textural and mechanical properties of the extrudate. The present experimental study aims to measure the impact of the kneading step on boehmite pastes prepared using a pilot kneader. The pastes are obtained by mixing boehmite powder with an acid and a basic solution in a two-step process known as peptization and neutralization. In this study, kneading is conducted at various mixing speeds and durations while monitoring the torque exerted by the boehmite paste on the kneader blades. Moreover, samples are extracted from the pilot kneader at various stages of the kneading process, and their textural properties are determined by both nitrogen sorption and mercury intrusion porosimetry. Our findings show that, at fixed composition, the textural properties of boehmite pastes are controlled by the overall deformation accumulated during kneading. Conversely, for a fixed accumulated deformation, the pH sets the textural properties. Finally, we identify an empirical control parameter that captures the combined effects of pH and accumulated deformation on the key textural attributes of boehmite pastes. These results set the stage for a systematic design approach of boehmite-based catalyst supports.
Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancer

Prostate cancer is the most common noncutaneous cancer in the world. Recently, multi-modality transrectal ultrasound (TRUS) has increasingly become an effective tool for the guidance of prostate biopsies. With the aim of effectively identifying prostate cancer, we propose a framework for the classification of clinically significant prostate cancer (csPCa) from multi-modality TRUS videos. The framework utilizes two 3D ResNet-50 models to extract features from B-mode images and shear wave elastography images, respectively. An adaptive spatial fusion module is introduced to aggregate two modalities' features. An orthogonal regularized loss is further used to mitigate feature redundancy. The proposed framework is evaluated on an in-house dataset containing 512 TRUS videos, and achieves favorable performance in identifying csPCa with an area under curve (AUC) of 0.84. Furthermore, the visualized class activation mapping (CAM) images generated from the proposed framework may provide valuable guidance for the localization of csPCa, thus facilitating the TRUS-guided targeted biopsy. Our code is publicly available at https://github.com/2313595986/ProstateTRUS.
The impact of CP-violating phases on DM observables in the cpMSSM

In this paper we examine the effect of adding CP-violating phases to the pMSSM on the Dark-Matter (DM) direct-detection cross sections, the velocity-weighted annihilation cross section, and the DM relic density. We show that $\varphi_{M_2}$ and $\varphi_{\mu}$, the phases of the wino and higgsino mass parameters, are sufficiently constrained by electron electric-dipole-moment (EDM) measurements such that the allowed values of these phases are too small to influence these DM observables. Conversely, the phase of the bino-mass parameter $\varphi_{M_1}$ and the phases associated with the trilinear couplings, $\varphi_{A^t}$, $\varphi_{A^b}$, and $\varphi_{A^\tau}$, are barely constrained by EDM experiments. We find that these cpMSSM phases can have an important impact on the mentioned DM observables. This especially concerns pMSSM points that lie on the boundary of exclusion, as observables can be affected by cpMSSM phases to the extent that they are either pushed into the observable region, or outside the excluded region.