Search

Nanoscale silicate dust particles are the most abundant refractory component observed in the interstellar medium and thought to play a key role in catalysing the formation of complex organic molecules in the star forming regions of space. We present a method to synthesise a laboratory analogue of nanoscale silicate dust particles on highly oriented pyrolytic graphite (HOPG) substrates by co-deposition of the atomic constituents. The resulting nanoparticulate films are sufficiently thin and conducting to allow for surface science investigations, and are characterised here, in situ under UHV, using X-ray photoelectron spectroscopy, near-edge X-ray absorption atomic fine spectroscopy and scanning tunnelling microscopy, and, ex situ, using scanning electron microscopy. We compare SiO$_{x}$ film growth with and without the use of atomic O beams during synthesis and conclude that exposure of the sample to atomic O leads to homogeneous films of interconnected nanoparticle networks. The networks covers the graphite substrate and demonstrate superior thermal stability, up to 1073 K, when compared to oxides produced without exposure to atomic O. In addition, control over the flux of atomic O during growth allows for control of the average oxidation state of the film produced. Photoelectron spectroscopy measurements demonstrate that fully oxidised films have an SiO$_{2}$ stoichiometry very close to bulk SiO$_{2}$ and scanning tunnelling microscopy images show the basic cluster building unit to have a radius of approximately 2.5 nm. The synthesis of SiO$_{x}$ films with adjustable stoichiometry and suitable for surface science experiments that require conducting substrates will be of great interest to the astrochemistry community, and will allow for nanoscale-investigation of the chemical processes thought to be catalysed at the surface of dust grains in space.

Doppler-broadened $\gamma$-ray spectra for positron annihilation on molecules are calculated using many-body theory. By employing Gaussian bases for the electron and positron wavefunctions, a computable expression that involves a four-centre integral over the two-annihilation-photon momenta is derived for the $\gamma$ spectra in the independent particle model approximation to the annihilation vertex, and implemented in the open-source {\tt EXCITON+} code. The influence of electron-positron correlations on the $\gamma$ spectra is examined through \textit{ab initio} treatment of the positron wavefunction, whilst corrections to the annihilation vertex are treated approximately via enhancement factors previously calculated [D. G. Green and G. F. Gribakin, Phys.~Rev.~Lett.~{\bf 114}, 093201 (2015)] exactly for atoms. Calculated $\gamma$ spectra for furan and acetonitrile are presented for annihilation from the positron bound state with electrons of individual molecular orbitals. For such annihilation from the positron-molecule bound state, it is found that the magnitude of the partial contribution to the $\gamma$ spectra from individual molecular orbitals depends not just on the orbital energies, but also on the molecular symmetry, more precisely the relative localisation of the positron and electron densities.

TeV-emitting BL Lac type blazars represent the extreme end of the blazar population. They are characterized by relatively weak jets and radiatively inefficient accretion disks. Particles accelerated in these jets experience fewer radiative losses, allowing them to reach energies beyond the TeV scale and produce TeV gamma-ray emission. The study of TeV blazars is constrained by the limited number of known sources in this category. Currently, only 56 high synchrotron-peaked BL Lacs have been detected at energies above 0.1\,TeV. Searches for TeV emission from BL~Lacs typically target sources with bright X-ray emission and a synchrotron peak at or above 1\,keV. The recently released eRASS1 catalog by the eROSITA collaboration, which covers half of the sky, represents the deepest X-ray survey in the soft X-ray band to date. Utilizing the eROSITA survey, combined with infrared data from WISE and archival radio observations, we have identified 135 TeV-emitting blazar candidates. Our search introduces selection criteria based on the radio to infrared that remove quasar-like objects that have similar infrared spectra and X-ray fluxes as TeV-emitting BL~Lacs. In our search, we find 26 objects that had not been detected in the ROSAT X-ray survey and 20 that have not been previously associated with blazars. The candidates resulting from our search are suitable for follow-up observations with currently operating imaging atmospheric Cherenkov telescopes, as well as future facilities like the CTAO Observatory., Comment: Submitted to ApJ

Language model (LM) post-training relies on two stages of human supervision: task demonstrations for supervised finetuning (SFT), followed by preference comparisons for reinforcement learning from human feedback (RLHF). As LMs become more capable, the tasks they are given become harder to supervise. Will post-training remain effective under unreliable supervision? To test this, we simulate unreliable demonstrations and comparison feedback using small LMs and time-constrained humans. We find that in the presence of unreliable supervision, SFT still retains some effectiveness, but DPO (a common RLHF algorithm) fails to improve the model beyond SFT. To address this, we propose iterative label refinement (ILR) as an alternative to RLHF. ILR improves the SFT data by using comparison feedback to decide whether human demonstrations should be replaced by model-generated alternatives, then retrains the model via SFT on the updated data. SFT+ILR outperforms SFT+DPO on several tasks with unreliable supervision (math, coding, and safe instruction-following). Our findings suggest that as LMs are used for complex tasks where human supervision is unreliable, RLHF may no longer be the best use of human comparison feedback; instead, it is better to direct feedback towards improving the training data rather than continually training the model. Our code and data are available at https://github.com/helloelwin/iterative-label-refinement., Comment: 22 pages, 10 figures

Lookup tables (LUTs) are frequently used to efficiently store arrays of precomputed values for complex mathematical computations. When used in the context of neural networks, these functions exhibit a lack of recognizable patterns which presents an unusual challenge for conventional logic synthesis techniques. Several approaches are known to break down a single large lookup table into multiple smaller ones that can be recombined. Traditional methods, such as plain tabulation, piecewise linear approximation, and multipartite table methods, often yield inefficient hardware solutions when applied to LUT-based NNs. This paper introduces ReducedLUT, a novel method to reduce the footprint of the LUTs by injecting don't cares into the compression process. This additional freedom introduces more self-similarities which can be exploited using known decomposition techniques. We then demonstrate a particular application to machine learning; by replacing unobserved patterns within the training data of neural network models with don't cares, we enable greater compression with minimal model accuracy degradation. In practice, we achieve up to $1.63\times$ reduction in Physical LUT utilization, with a test accuracy drop of no more than $0.01$ accuracy points.

We extend Friedman's theorem to show that, for any fixed $r$, the random $2r$--regular Schreier graphs depicting the action of $r$ random permutations of $[n]$ on $k_{n}$--tuples of distinct elements in $[n]$ are a.a.s. weakly Ramanujan, for any $k_{n}\leq n^{\frac{1}{12}-\epsilon}.$ Previously this was known only for $k$--tuples where $k$ is fixed. In fact, we prove the stronger result of strong convergence of random matrices in irreducible representations of $S_{n}$ of quasi--exponential dimension. We also give a new bound for the expected stable irreducible character of a random permutation obtained via a word map, showing that $\mathbb{E}\left[\chi^{\mu}\left(w(\sigma_{1},\dots,\sigma_{r})\right)\right]=O\left(n^{-k}\right)$, where $k$ is the number of boxes outside the first row of the Young diagram $\mu,$ solving one aspect of a conjecture of Hanany and Puder. We obtain this bound using an extension of Wise's $w$--cycle conjecture., Comment: Correction to an argument made in the original posting

GRB$\,$220831A is a gamma-ray burst (GRB) with a duration and spectral peak energy that places it at the interface between the distribution of long-soft and short-hard GRBs. In this paper, we present the multi-wavelength follow-up campaign to GRB$\,$220831A and its optical, near-infrared, X-ray and radio counterparts. Our deep optical and near-infrared observations do not reveal an underlying host galaxy, and establish that GRB$\,$220831A is observationally hostless to depth, $m_i\gtrsim26.6$ AB mag. Based on the Amati relation and the non-detection of an accompanying supernova, we find that this GRB is most likely to have originated from a collapsar at $z>2$, but it could also possibly be a compact object merger at $z<0.4$ with a large separation distance from its host galaxy. Regardless of its origin, we show that its optical and near-infrared counterpart departs from the evolution expected from a forward shock dominated synchrotron afterglow, exhibiting a steep post-break temporal powerlaw index of $-3.83^{+0.62}_{-0.79}$, too steep to be the jet-break. By analysing a range of models, we find that the observed steep departure from forward shock closure relations is likely due to an internal process producing either a flare or a plateau., Comment: 17 pages, 14 figures, 7 tables, accepted for publication in MNRAS

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered., Comment: 14 pages, 7 figures

In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners. A particular challenge for establishing common ground arises in remote dialogue (occurring in disaster relief or search-and-rescue tasks), where a human and robot are engaged in a joint navigation and exploration task of an unfamiliar environment, but where the robot cannot immediately share high quality visual information due to limited communication constraints. Engaging in a dialogue provides an effective way to communicate, while on-demand or lower-quality visual information can be supplemented for establishing common ground. Within this paradigm, we capture propositional semantics and the illocutionary force of a single utterance within the dialogue through our Dialogue-AMR annotation, an augmentation of Abstract Meaning Representation. We then capture patterns in how different utterances within and across speaker floors relate to one another in our development of a multi-floor Dialogue Structure annotation schema. Finally, we begin to annotate and analyze the ways in which the visual modalities provide contextual information to the dialogue for overcoming disparities in the collaborators' understanding of the environment. We conclude by discussing the use-cases, architectures, and systems we have implemented from our annotations that enable physical robots to autonomously engage with humans in bi-directional dialogue and navigation., Comment: 52 pages, 14 figures

Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three days to system construction and testing. Seventy young researchers from all parts of the world formed six teams focusing on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days. The different steps, encountered challenges, and their solutions are reported., Comment: 49 pages, 23 figures, comments welcome (this paper is meant to be useful to people constructing their own MRI systems)

As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002 to 2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development.

Given a source image of a clothed person (an image subject), AI-based nudification applications can produce nude (undressed) images of that person. Moreover, not only do such applications exist, but there is ample evidence of the use of such applications in the real world and without the consent of an image subject. Still, despite the growing awareness of the existence of such applications and their potential to violate the rights of image subjects and cause downstream harms, there has been no systematic study of the nudification application ecosystem across multiple applications. We conduct such a study here, focusing on 20 popular and easy-to-find nudification websites. We study the positioning of these web applications (e.g., finding that most sites explicitly target the nudification of women, not all people), the features that they advertise (e.g., ranging from undressing-in-place to the rendering of image subjects in sexual positions, as well as differing user-privacy options), and their underlying monetization infrastructure (e.g., credit cards and cryptocurrencies). We believe this work will empower future, data-informed conversations -- within the scientific, technical, and policy communities -- on how to better protect individuals' rights and minimize harm in the face of modern (and future) AI-based nudification applications. Content warning: This paper includes descriptions of web applications that can be used to create synthetic non-consensual explicit AI-created imagery (SNEACI). This paper also includes an artistic rendering of a user interface for such an application., Comment: 22 pages, 5 figures, 2 tables

Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery. Our BioNet, utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module, processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery, we integrate a neural feature field (NeFF) module, enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality, BioNet demonstrates superior performance on two public datasets, with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality, the combination of BioNet and NeFF achieves a 7.9% RI. Additionally, the NeFF-based approach utilizes inexpensive, portable drone-mounted cameras, providing a scalable solution for large field applications.

Many printed electronic applications require strain-independent electrical properties to ensure deformation-independent performance. Thus, developing printed, flexible devices using 2D and other nanomaterials will require an understanding of the effect of strain on the electrical properties of nano-networks. Here we introduce novel AC electrical techniques to fully characterise the effect of strain on the resistance of high mobility printed networks, fabricated from of electrochemically exfoliated MoS2 nanosheets. These devices were initially characterised using DC piezoresistance measurements and showed good cyclability and a linear strain response, consistent with a low gauge factor of G~3. However, AC impedance spectroscopy measurements, performed as a function of strain, allowed the measurement of the effects of strain on both the nanosheets and the inter-nanosheet junctions separately. The junction resistance was found to increase linearly with strain, while the nanosheet resistance remained constant. This response is consistent with strain-induced sliding of the highly-aligned nanosheets past one another, without any strain being transferred to the sheets themselves. Our approach allows us to individually estimate the contributions of dimensional factors (G~1.4) and intrinsic factors (G~1.9) to the total gauge factor. This novel technique may provide insight into other piezoresistive systems., Comment: 3 Figures

Solution-processable 2D materials are promising candidates for a range of printed electronics applications. Yet maximising their potential requires solution-phase processing of nanosheets into high-quality networks with carrier mobility ({\mu}Net) as close as possible to that of individual nanosheets ({\mu}NS). In practise, the presence of inter-nanosheet junctions generally limits electronic conduction, such that the ratio of junction resistance (RJ) to nanosheet resistance (RNS), determines the network mobility via . Hence, achieving RJ/RNS<1 is a crucial step for implementation of 2D materials in printed electronics applications. In this work, we utilise an advanced liquid-interface deposition process to maximise nanosheet alignment and network uniformity, thus reducing RJ. We demonstrate the approach using graphene and MoS2 as model materials, achieving low RJ/RNS values of 0.5 and 0.2, respectively. The resultant graphene networks show a high conductivity of {\sigma}Net = 5 \times 104 S/m while our semiconducting MoS2 networks demonstrate record mobility of {\mu}Net = 30 cm2/Vs, both at extremely low network thickness (tNet <10 nm). Finally, we show that the deposition process is compatible with non-layered quasi-2D materials such as silver nanosheets (AgNS), achieving network conductivity close to bulk silver for networks <100 nm thick. We believe this work is the first to report nanosheet networks with RJ/RNS<1 and serves to guide future work in 2D materials-based printed electronics.

Science is increasingly global, with international collaboration playing a crucial role in advancing scientific development and knowledge exchange across borders. However, the processes that regulate how scientific labor is distributed among countries remain underexplored, leading to challenges in ensuring both effective collaboration and equitable participation across diverse scientific communities. Here, we leverage three million internationally coauthored publications produced by countries worldwide to examine the division of scientific labor in international collaboration, identify the factors that shape this distribution, and assess its broader consequences. Our findings uncover a persistent hierarchical structure in international collaboration, with researchers from scientifically advanced countries tend to occupy leading roles, while those from less-developed countries are often relegated to supportive roles, even after controlling for various influential factors. This hierarchy is also reflected in the research content, as countries with lower scientific capacity tend to participate in international collaborations that deviate from their domestic science. By analyzing the labor division within international collaborations, we demonstrate that researchers from less-developed countries face systematic disadvantages, which not only limit their contributions to the global scientific community but also prevent them from fully benefiting from international collaborations., Comment: 35 pages, 7 figures, 3 tables

Delayed processes are ubiquitous throughout biology. These delays may arise through maturation processes or as the result of complex multi-step networks, and mathematical models with distributed delays are increasingly used to capture the heterogeneity present in these delayed processes. Typically, these distributed delay differential equations are simulated by discretizing the distributed delay and using existing tools for the resulting multi-delay delay differential equations or by using an equivalent representation under additional assumptions on the delayed process. Here, we use the existing framework of functional continuous Runge-Kutta methods to confirm the convergence of this common approach. Our analysis formalizes the intuition that the least accurate numerical method dominates the error. We give a number of examples to illustrate the predicted convergence, derive a new class of equivalences between distributed delay and discrete delay differential equations, and give conditions for the existence of breaking points in the distributed delay differential equation. Finally, our work shows how recently reported multi-delay complexity collapse arises naturally from the convergence of equations with multiple discrete delays to equations with distributed delays, offering insight into the dynamics of the Mackey-Glass equation., Comment: 18 pages, 2 figures

Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light-skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker-skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark-skin tone test set with ground truth, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker-skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation.

Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world settings. In recommender systems, the use of synthetic data is somewhat limited; some work has concentrated on building user-item interaction data at large scale. We believe that fairness-aware recommendation research can benefit from simulated data as it allows the study of protected groups and their interactions without depending on sensitive data that needs privacy protection. In this paper, we propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs that can be used to study re-ranking algorithms.

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot's actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://sites.google.com/illinois.edu/comp-teleop., Comment: Accepted to IEEE ICRA 2025; 8 pages, 4 figures, 3 tables

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Social technology research today largely focuses on mitigating the negative impacts of technology and, therefore, often misses the potential of technology to enhance human connections and well-being. However, we see a potential to shift towards a holistic view of social technology's impact on human flourishing. We introduce Positive Social Technology (Positech), a framework that shifts emphasis toward leveraging social technologies to support and augment human flourishing. This workshop is organized around three themes relevant to Positech: 1) "Exploring Relevant and Adjacent Research" to define and widen the Positech scope with insights from related fields, 2) "Projecting the Landscape of Positech" for participants to outline the domain's key aspects and 3) "Envisioning the Future of Positech," anchored around strategic planning towards a sustainable research community. Ultimately, this workshop will serve as a platform to shift the narrative of social technology research towards a more positive, human-centric approach. It will foster research that goes beyond fixing technologies to protect humans from harm, to also pursue enriching human experiences and connections through technology.