Miracle-dz/UniFAR-FTUs huggingface dataset:
Doppler Constraints on Planetary Companions to Nearby Sun-like Stars: An Archival Radial Velocity Survey of Southern Targets for Proposed NASA Direct Imaging Missions

Directly imaging temperate rocky planets orbiting nearby, Sun-like stars with a 6 m class IR/O/UV space telescope, recently dubbed the Habitable Worlds Observatory, is a high-priority goal of the Astro2020 Decadal Survey. To prepare for future direct imaging (DI) surveys, the list of potential targets should be thoroughly vetted to maximize efficiency and scientific yield. We present an analysis of archival radial velocity data for southern stars from the NASA/NSF Extreme Precision Radial Velocity (EPRV) Working Group’s list of high-priority target stars for future DI missions (drawn from the HabEx, LUVOIR, and Starshade Rendezvous studies). For each star, we constrain the region of companion mass and period parameter space we are already sensitive to based on the observational baseline, sampling, and precision of the archival radial velocity (RV) data. Additionally, for some of the targets, we report new estimates of magnetic activity cycle periods, rotation periods, improved orbital parameters for previously known exoplanets, and new candidate planet signals that require further vetting or observations to confirm. Our results show that for many of these stars we are not yet sensitive to even Saturn-mass planets in the habitable zone, let alone smaller planets, highlighting the need for future EPRV vetting efforts before the launch of a DI mission. We present evidence that the candidate temperate super-Earth exoplanet HD 85512b is most likely due to the star’s rotation, and report an RV acceleration for δ Pav that supports the existence of a distant giant planet previously inferred from astrometry.
Predicting Privacy Preferences for Smart Devices as Norms

Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect them to adhere to their privacy preferences. However, the current approach of gathering these preferences consists in asking the users directly, which usually triggers automatic responses failing to capture their true preferences. In response, in this paper we present a collaborative filtering approach to predict user preferences as norms. These preference predictions can be readily adopted or can serve to assist users in determining their own preferences. Using a dataset of privacy preferences of smart assistant users, we test the accuracy of our predictions.
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay significantly improves the training efficiency and stability of RL algorithms. Many decision-making problems in practice naturally involve multiple agents and require multi-agent reinforcement learning (MARL) under centralized training decentralized execution paradigm. Nevertheless, existing MARL algorithms often adopt standard experience replay where the transitions are uniformly sampled regardless of their importance. Finding prioritized sampling weights that are optimized for MARL experience replay has yet to be explored. To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions. Such optimization is relaxed and solved using the Lagrangian multiplier approach to obtain the close-form optimal sampling weights. By minimizing the resulting policy regret, we can narrow the gap between the current policy and a nominal optimal policy, thus acquiring an improved prioritization scheme for multi-agent tasks. Our experimental results on Predator-Prey and StarCraft Multi-Agent Challenge environments demonstrate the effectiveness of our method, having a better ability to replay important transitions and outperforming other state-of-the-art baselines.
Nano-Crystals of c-Diamond, n-Diamond and i-Carbon Grown in Carbon-Ion Implanted Fused Quartz

Combined high-resolution transmission electron microscopy, selected area electron diffraction and parallel electron energy loss spectroscopy are used to characterise carbon nano-phases found embedded in fused quartz. These appear after implantation of 1 MeV carbon ions, followed by annealing in argon, oxygen and forming gas for 1 hour at 1100°C. For Ar, virtually all of the carbon diffuses out of the substrate with no observable carbon clusters for all doses studied. After annealing in oxygen, a crystalline COx phase is identified at the end of range, following a dose of 5×1017C/cm2. Three nano-crystalline carbon phases, including diamond, appear after annealing in forming gas: these form a layer 170 nm beneath the fused quartz surface for all ion doses. The average size of these clusters and the corresponding phases depend on the ion dose; the smallest size of 5–7 nm diameter crystallise as fcc diamond following a dose of 0.5× 1017C/cm2, whereas clusters of 8–13 nm diameter, for a higher dose of 2× 1017C/cm2, have a modified phase of diamond known as n-diamond. The largest clusters, diameter 15–40 nm, for a dose of 5× 1017C/cm2, have the cubic P213 (or P4232) structure known as i-carbon. These buried layered diamond and diamond-related materials may have applications for field emission and optical waveguide type devices.
A unified approach for morphometrics and functional data analysis with machine learning for craniodental shape quantification in shrew species

This work proposes a functional data analysis approach for morphometrics with applications in classifying three shrew species (S. murinus, C. monticola and C. malayana) based on the images. The discrete landmark data of craniodental views (dorsal, jaw and lateral) are converted into continuous curves where the curves are represented as linear combinations of basis functions. A comparative study based on four machine learning algorithms such as naive Bayes, support vector machine, random forest, and generalized linear models was conducted on the predicted principal component scores obtained from the FDA approach and classical approach (combination of all three craniodental views and individual views). The FDA approach produced better results in separating the three clusters of shrew species compared to the classical method and the dorsal view gave the best representation in classifying the three shrew species. Overall, based on the FDA approach, GLM of the predicted PCA scores was the most accurate (95.4% accuracy) among the four classification models.
Mode interference study of bulk acoustic wave liquid sensors

The impedance of thickness shear mode (TSM) quartz resonators, which are very sensitive to the liquid electrical property changes, has been investigated. Resonators with a ring shape electrode were exposed to a NaCl solution of varying concentrations and it was found that mode interference between the primary and spurious mode occurred. The results show that as the inner diameter of the ring electrode increases, a spurious mode appears with increasing mode strength and the frequency spacing between the spurious mode and the primary TSM decreases. For a resonator with the same sensing electrode, the strength and center frequency of the spurious mode change as the NaCl concentration varies. Theoretical analysis of the mode interference based on a liquid lossy electrode is presented.
Phases of cold holographic QCD: Baryons, pions and rho mesons

We improve the holographic description of isospin-asymmetric baryonic matter within the Witten-Sakai-Sugimoto model by accounting for a realistic pion mass, computing the pion condensate dynamically, and including rho meson condensation by allowing the gauge field in the bulk to be anisotropic. This description takes into account the coexistence of baryonic matter with pion and rho meson condensates. Our main result is the zero-temperature phase diagram in the plane of baryon and isospin chemical potentials. We find that the effective pion mass in the baryonic medium increases with baryon density and that, as a consequence, there is no pion condensation in neutron-star matter. Our improved description also predicts that baryons are disfavored at low baryon chemical potentials even for arbitrarily large isospin chemical potential. Instead, rho meson condensation sets in on top of the pion condensate at an isospin chemical potential of about 9.4\, m_\pi9.4mπ. We further observe a highly non-monotonic phase boundary regarding the disappearance of pion condensation.
Heterogeneous Social Event Detection via Hyperbolic Graph Representations

Social events reflect the dynamics of society and, here, natural disasters and emergencies receive significant attention. The timely detection of these events can provide organisations and individuals with valuable information to reduce or avoid losses. However, due to the complex heterogeneities of the content and structure of social media, existing models can only learn limited information; large amounts of semantic and structural information are ignored. In addition, due to high labour costs, it is rare for social media datasets to include high-quality labels, which also makes it challenging for models to learn information from social media. In this study, we propose two hyperbolic graph representation-based methods for detecting social events from heterogeneous social media environments. For cases where a dataset has labels, we designed a Hyperbolic Social Event Detection (HSED) model that converts complex social information into a unified social message graph. This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space. For cases where the dataset is unlabelled, we designed an Unsupervised Hyperbolic Social Event Detection (UHSED). This model is based on the HSED model but includes graph contrastive learning to make it work in unlabelled scenarios. Extensive experiments demonstrate the superiority of the proposed approaches.
Contrastive Learning and the Emergence of Attributes Associations

In response to an object presentation, supervised learning schemes generally respond with a parsimonious label. Upon a similar presentation we humans respond again with a label, but are flooded, in addition, by a myriad of associations. A significant portion of these consist of the presented object attributes. Contrastive learning is a semi-supervised learning scheme based on the application of identity preserving transformations on the object input representations. It is conjectured in this work that these same applied transformations preserve, in addition to the identity of the presented object, also the identity of its semantically meaningful attributes. The corollary of this is that the output representations of such a contrastive learning scheme contain valuable information not only for the classification of the presented object, but also for the presence or absence decision of any attribute of interest. Simulation results which demonstrate this idea and the feasibility of this conjecture are presented.
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design provably efficient quantum RL algorithms that can address the exploration-exploitation trade-off. To this end, we propose a novel UCRL-style algorithm that takes advantage of quantum computing for tabular Markov decision processes (MDPs) with $S$ states, $A$ actions, and horizon $H$, and establish an $\mathcal{O}(\mathrm{poly}(S, A, H, \log T))$ worst-case regret for it, where $T$ is the number of episodes. Furthermore, we extend our results to quantum RL with linear function approximation, which is capable of handling problems with large state spaces. Specifically, we develop a quantum algorithm based on value target regression (VTR) for linear mixture MDPs with $d$-dimensional linear representation and prove that it enjoys $\mathcal{O}(\mathrm{poly}(d, H, \log T))$ regret. Our algorithms are variants of UCRL/UCRL-VTR algorithms in classical RL, which also leverage a novel combination of lazy updating mechanisms and quantum estimation subroutines. This is the key to breaking the $\Omega(\sqrt{T})$-regret barrier in classical RL. To the best of our knowledge, this is the first work studying the online exploration in quantum RL with provable logarithmic worst-case regret.
Deep Generative Neural Embeddings for High Dimensional Data Visualization

We propose a visualization technique that utilizes neural network embeddings and a generative network to reconstruct original data. This method allows for independent manipulation of individual image embeddings through its non-parametric structure, providing more flexibility than traditional autoencoder approaches. We have evaluated the effectiveness of this technique in data visualization and compared it to t-SNE and VAE methods. Furthermore, we have demonstrated the scalability of our method through visualizations on the ImageNet dataset. Our technique has potential applications in human-in-the-loop training, as it allows for independent editing of embedding locations without affecting the optimization process.
Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which computes a reward maximizing policy from a given start to a far away goal state while satisfying cost constraints) with a low-level goal conditioned RL agent (which estimates cost and reward values to move between nearby states). A major advantage of CoSHRL is that it can handle constraints on the cost value distribution (e.g., on Conditional Value at Risk, CVaR) and can adjust to flexible constraint thresholds without retraining. We perform extensive experiments with different types of safety constraints to demonstrate the utility of our approach over leading approaches in constrained and hierarchical RL.
The Benefit of Uncertainty Coupling in Robust and Adaptive Robust Optimization

Despite the modeling power for problems under uncertainty, robust optimization (RO) and adaptive robust optimization (ARO) can exhibit too conservative solutions in terms of objective value degradation compared to the nominal case. One of the main reasons behind this conservatism is that, in many practical applications, uncertain constraints are directly designed as constraint-wise without taking into account couplings over multiple constraints. In this paper, we define a coupled uncertainty set as the intersection between a constraint-wise uncertainty set and a coupling set. We study the benefit of coupling in alleviating conservatism in RO and ARO. We provide theoretical tight and computable upper and lower bounds on the objective value improvement of RO and ARO problems under coupled uncertainty over constraint-wise uncertainty. In addition, we relate the power of adaptability over static solutions with the coupling of uncertainty set. Computational results demonstrate the benefit of coupling in applications.
Unfolding the Hong–Ou–Mandel interference between heralded photons from narrowband twin beams

The Hong–Ou–Mandel (HOM) interference is one of the most intriguing quantum optical phenomena and crucial in performing quantum optical communication and computation tasks. Lately, twin beam emitters such as those relying on the process of parametric down-conversion (PDC) have become confident sources of heralded single photons. However, if the pump power is high enough, the pairs produced via PDC—often called signal and idler—incorporate multiphoton contributions that usually distort the investigated quantum features. Here, we derive the temporal characteristics of the HOM interference between heralded states from two independent narrowband PDC sources. Apart from the PDC multiphoton content, our treatment also takes into account effects arriving from an unbalanced beam splitter ratio and optical losses. We perform a simulation in the telecommunication wavelength range and provide a useful tool for finding the optimal choice for PDC process parameters. Our results offer insight in the properties of narrowband PDC sources and turn useful when driving quantum optical applications with them.
Harms from Increasingly Agentic Algorithmic Systems

Research in Fairness, Accountability, Transparency, and Ethics (FATE)1 has established many sources and forms of algorithmic harm, in domains as diverse as health care, finance, policing, and recommendations. Much work remains to be done to mitigate the serious harms of these systems, particularly those disproportionately affecting marginalized communities. Despite these ongoing harms, new systems are being developed and deployed, typically without strong regulatory barriers, threatening the perpetuation of the same harms and the creation of novel ones. In response, the FATE community has emphasized the importance of anticipating harms, rather than just responding to them. Anticipation of harms is especially important given the rapid pace of developments in machine learning (ML). Our work focuses on the anticipation of harms from increasingly agentic systems. Rather than providing a definition of agency as a binary property, we identify 4 key characteristics which, particularly in combination, tend to increase the agency of a given algorithmic system: underspecification, directness of impact, goal-directedness, and long-term planning. We also discuss important harms which arise from increasing agency – notably, these include systemic and/or long-range impacts, often on marginalized or unconsidered stakeholders. We emphasize that recognizing agency of algorithmic systems does not absolve or shift the human responsibility for algorithmic harms. Rather, we use the term agency to highlight the increasingly evident fact that ML systems are not fully under human control. Our work explores increasingly agentic algorithmic systems in three parts. First, we explain the notion of an increase in agency for algorithmic systems in the context of diverse perspectives on agency across disciplines. Second, we argue for the need to anticipate harms from increasingly agentic systems. Third, we discuss important harms from increasingly agentic systems and ways forward for addressing them. We conclude by reflecting on implications of our work for anticipating algorithmic harms from emerging systems.
A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)

We propose a new Tipping Point Generative Adversarial Network (TIP-GAN) for better characterizing potential climate tipping points in Earth system models. We describe an adversarial game to explore the parameter space of these models, detect upcoming tipping points, and discover the drivers of tipping points. In this setup, a set of generators learn to construct model configurations that will invoke a climate tipping point. The discriminator learns to identify which generators are generating each model configuration and whether a given configuration will lead to a tipping point. The discriminator is trained using an oracle (a surrogate climate model) to test if a generated model configuration leads to a tipping point or not. We demonstrate the application of this GAN to invoke the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We share experimental results of modifying the loss functions and the number of generators to exploit the area of uncertainty in model state space near a climate tipping point. In addition, we show that our trained discriminator can predict AMOC collapse with a high degree of accuracy without the use of the oracle. This approach could generalize to other tipping points, and could augment climate modeling research by directing users interested in studying tipping points to parameter sets likely to induce said tipping points in their computationally intensive climate models.
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-variate time series data, which are of major significance for today's industrial applications. However, establishing an anomaly detection system that can be rapidly and accurately located is a challenging problem due to the lack of outlier tags, the high dimensional complexity of the data, memory bottlenecks in the actual hardware, and the need for fast reasoning. We have proposed an anomaly detection and diagnosis model -- DTAAD in this paper, based on Transformer, and Dual Temporal Convolutional Network(TCN). Our overall model will be an integrated design in which autoregressive model(AR) combines autoencoder(AE) structures, and scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Constructed by us, the Dual TCN-Attention Network (DTA) only uses a single layer of Transformer encoder in our baseline experiment, that belongs to an ultra-lightweight model. Our extensive experiments on six publicly datasets validate that DTAAD exceeds current most advanced baseline methods in both detection and diagnostic performance. Specifically, DTAAD improved F1 scores by $8.38\%$, and reduced training time by $99\%$ compared to baseline. The code and training scripts are publicly on GitHub at https://github.com/Yu-Lingrui/DTAAD.
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts

We present the TherapyView, a demonstration system to help therapists visualize the dynamic contents of past treatment sessions, enabled by the state-of-the-art neural topic modeling techniques to analyze the topical tendencies of various psychiatric conditions and deep learning-based image generation engine to provide a visual summary. The system incorporates temporal modeling to provide a time-series representation of topic similarities at a turn-level resolution and AI-generated artworks given the dialogue segments to provide a concise representations of the contents covered in the session, offering interpretable insights for therapists to optimize their strategies and enhance the effectiveness of psychotherapy. This system provides a proof of concept of AI-augmented therapy tools with e in-depth understanding of the patient's mental state and enabling more effective treatment.