Miracle-dz/UniFAR-FTUs huggingface dataset:
GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization

Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. This task has considerable challenges due to immense variation in geographic landscapes. The image-to-image retrieval-based approaches fail to solve this problem on a global scale as it is not feasible to construct a large gallery of images covering the entire world. Instead, existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. However, their performance is limited by the predefined classes and often results in inaccurate localizations when an image's location significantly deviates from its class center. To overcome these limitations, we propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations. GeoCLIP's location encoder models the Earth as a continuous function by employing positional encoding through random Fourier features and constructing a hierarchical representation that captures information at varying resolutions to yield a semantically rich high-dimensional feature suitable to use even beyond geo-localization. To the best of our knowledge, this is the first work employing GPS encoding for geo-localization. We demonstrate the efficacy of our method via extensive experiments and ablations on benchmark datasets. We achieve competitive performance with just 20% of training data, highlighting its effectiveness even in limited-data settings. Furthermore, we qualitatively demonstrate geo-localization using a text query by leveraging CLIP backbone of our image encoder. The project webpage is available at: https://vicentevivan.github. io/GeoCLIP
Psychosocial factors associated with the risk of developing psychosis in a Mexican general population sample

Epidemiological evidence has linked an array of sociodemographic and psychosocial factors with an increased risk of developing psychosis. However, research in samples from low- and middle-income countries is still scarce. This study used a Mexican sample to explore (i) sociodemographic and psychosocial differences between individuals with and without a positive screen for Clinical High-Risk for psychosis (CHR), and (ii) sociodemographic and psychosocial factors associated with screening positive for CHR. The sample consisted of 822 individuals from the general population who completed an online survey. Of the participants, 17.3% (n = 142) met the CHR screening criteria. Comparisons between those who screened positive (CHR-positive group) and those who did not (Non-CHR group) showed that participants in the CHR-positive group were younger, had a lower educational level, and reported more mental health problems than the Non-CHR group. Furthermore, relative to the Non-CHR group, the CHR-positive group had a greater prevalence of medium/high risk associated with cannabis use, a higher prevalence of adverse experiences (bullying, intimate partner violence, and experiencing a violent or unexpected death of a relative or friend), as well as higher levels of childhood maltreatment, poorer family functioning, and more distress associated with the COVID-19 pandemic. Groups did not differ in sex, marital/relationship status, occupation, and socio-economic status. Finally, when examined in multivariate analyses, the variables associated with screening positive for CHR were: having an unhealthy family functioning (OR = 2.75, 95%CI 1.69–4.46), a higher risk associated with cannabis use (OR = 2.75, 95%CI 1.63–4.64), a lower level of education (OR = 1.55, 95%CI 1.003–2.54), having experienced a major natural disaster (OR = 1.94, 95%CI 1.18–3.16), having experienced a violent or unexpected death of a relative or friend (OR = 1.85, 95%CI 1.22–2.81), higher levels of childhood emotional abuse (OR = 1.88, 95%CI 1.09–3.25), physical neglect (OR = 1.68, 95%CI 1.08–2.61), and physical abuse (OR = 1.66, 95%CI 1.05–2.61), and higher COVID-related distress (OR = 1.10, 95%CI 1.01–1.20). An older age was a protective factor for screening positive for CHR (OR = 0.96, 95%CI 0.92–0.99). Overall, the findings highlight the importance of examining potential psychosocial contributors to psychosis vulnerability across different sociocultural contexts to delineate risk and protective processes relevant to specific populations and better target preventive intervention efforts.
Pressure-induced one-dimensional oxygen ion diffusion channel in lead apatite

Recently, Lee et al. claimed that the experimental observation of room-temperature ambient-pressure superconductivity in a Cu-doped lead-apatite (Pb10-xCux(PO4)6O). The study revealed the Cu doping induces a chemical pressure, resulting in a structural contraction of one-dimensional Cu-O-Cu atomic column. This unique structure promotes a one-dimensional electronic conduction channel along the c-axis mediated by the O atoms, which may be related to superconductivity. These O atoms occupy 1/4 of the equivalent positions along the c-axis and exhibit a low diffusion activation energy of 0.8 eV, indicating the possibility of diffusion between these equivalent positions. Here, using machine-learning based deep potential, we predict the pressure-induced fast diffusion of 1/4-occupied O atoms along the one-dimensional channel in Pb10(PO4)6O at 500 K, while the frameworks of Pb triangles and PO4 tetrahedrons remain stable. The calculation results also indicate Cu doping can provide appropriate effective chemical pressure, indicating the one-dimensional ion diffusion channel may appear in Pb9Cu(PO4)6O, even at ambient pressure. Our finding shows that the one-dimensional ions diffusion channel may be an important factor to affects the fabrication and electrical measurement of doped lead-apatite.
Stackelberg Batch Policy Learning

Batch reinforcement learning (RL) defines the task of learning from a fixed batch of data lacking exhaustive exploration. Worst-case optimality algorithms, which calibrate a value-function model class from logged experience and perform some type of pessimistic evaluation under the learned model, have emerged as a promising paradigm for batch RL. However, contemporary works on this stream have commonly overlooked the hierarchical decision-making structure hidden in the optimization landscape. In this paper, we adopt a game-theoretical viewpoint and model the policy learning diagram as a two-player general-sum game with a leader-follower structure. We propose a novel stochastic gradient-based learning algorithm: StackelbergLearner, in which the leader player updates according to the total derivative of its objective instead of the usual individual gradient, and the follower player makes individual updates and ensures transition-consistent pessimistic reasoning. The derived learning dynamic naturally lends StackelbergLearner to a game-theoretic interpretation and provides a convergence guarantee to differentiable Stackelberg equilibria. From a theoretical standpoint, we provide instance-dependent regret bounds with general function approximation, which shows that our algorithm can learn a best-effort policy that is able to compete against any comparator policy that is covered by batch data. Notably, our theoretical regret guarantees only require realizability without any data coverage and strong function approximation conditions, e.g., Bellman closedness, which is in contrast to prior works lacking such guarantees. Through comprehensive experiments, we find that our algorithm consistently performs as well or better as compared to state-of-the-art methods in batch RL benchmark and real-world datasets.
Analytical asymptotic solutions to determine interactions between the planetary boundary layer and the Earth's surface

[1] This study derives an asymptotic analytical solution to calculate land skin temperature, planetary boundary layer (PBL) temperature, and turbulent heat fluxes over arid and wet ground surfaces. Applying the analytical solution to field data, the turbulent heat fluxes and the daytime canopy resistance (which are difficult to measure directly) can be easily determined on the basis of solar radiation and atmospheric radiation measurements, as well as other commonly available meteorological data. The results can be compared with a Lagrangian soil-plant-atmosphere model. Using the derived canopy resistance as input for the Lagrangian model, the simulated PBL temperature has a higher correlation coefficient with the real data than two other canopy schemes. The analytical solution proves that the mean skin temperature is slightly higher than the mean PBL temperature, and its amplitude is always larger than that of PBL temperature in general. An equation for determining the half-life of either PBL temperature or skin temperature is derived. The half-life is the time needed for the initial temperatures of the PBL or a land surface to decay to 50%. Note that the temperatures will alter in response to variations in the meteorological conditions such as changes in solar radiation. Over bare ground, vegetation, snow or ice, the half-lives are ∼8 hours. For a body of water, it is 1.4 months with a thermocline depth of 100 m. Although the algorithm of determining canopy resistance is verified in an urban area in Taiwan, it is likely to be valid worldwide.
Analysis of the Usability of Automatically Enriched Cultural Heritage Data

This chapter presents the potential of interoperability and standardised data publication for cultural heritage resources, with a focus on community-driven approaches and web standards for usability. The Linked Open Usable Data (LOUD) design principles, which rely on JSON-LD as lingua franca, serve as the foundation. We begin by exploring the significant advances made by the International Image Interoperability Framework (IIIF) in promoting interoperability for image-based resources. The principles and practices of IIIF have paved the way for Linked Art, which expands the use of linked data by demonstrating how it can easily facilitate the integration and sharing of semantic cultural heritage data across portals and institutions. To provide a practical demonstration of the concepts discussed, the chapter highlights the implementation of LUX, the Yale Collections Discovery platform. LUX serves as a compelling case study for the use of linked data at scale, demonstrating the real-world application of automated enrichment in the cultural heritage domain. Rooted in empirical study, the analysis presented in this chapter delves into the broader context of community practices and semantic interoperability. By examining the collaborative efforts and integration of diverse cultural heritage resources, the research sheds light on the potential benefits and challenges associated with LOUD.
Towards Poisoning Fair Representations

Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained by deep neural networks has demonstrated superior performance, whereby representations containing no demographic information are inferred from the data and then used as the input to classification or other downstream tasks. Despite the development of FRL methods, their vulnerability under data poisoning attack, a popular protocol to benchmark model robustness under adversarial scenarios, is under-explored. Data poisoning attacks have been developed for classical fair machine learning methods which incorporate fairness constraints into shallow-model classifiers. Nonetheless, these attacks fall short in FRL due to notably different fairness goals and model architectures. This work proposes the first data poisoning framework attacking FRL. We induce the model to output unfair representations that contain as much demographic information as possible by injecting carefully crafted poisoning samples into the training data. This attack entails a prohibitive bilevel optimization, wherefore an effective approximated solution is proposed. A theoretical analysis on the needed number of poisoning samples is derived and sheds light on defending against the attack. Experiments on benchmark fairness datasets and state-of-the-art fair representation learning models demonstrate the superiority of our attack.
Scale-invariant critical dynamics at eigenstate transitions

The notion of scale-invariant dynamics is well established at late times in quantum chaotic systems, as illustrated by the emergence of a ramp in the spectral form factor (SFF). Building on the results of the preceding Letter [Phys. Rev. Lett. 131, 060404 (2023)], we explore features of scale-invariant dynamics of survival probability and SFF at criticality, i.e., at eigenstate transitions from quantum chaos to localization. We show that, in contrast to the quantum chaotic regime, the quantum dynamics at criticality do not only exhibit scale invariance at late times, but also at much shorter times that we refer to as mid-time dynamics. Our results apply to both quadratic and interacting models. Specifically, we study Anderson models in dimensions three to five and power-law random banded matrices for the former, and the quantum sun model and the ultrametric model for the latter, as well as the Rosenzweig-Porter model.
High frequency stream bed mobility of a low‐gradient agricultural stream with implications on the hyporheic zone

Little Kickapoo Creek (LKC), a low‐gradient stream, mobilizes its streambed–fundamentally altering its near‐surface hyporheic zone–more frequently than do higher‐gradient mountain and karst streams. LKC streambed mobility was assessed through streambed surveys, sediment sampling, and theoretical calculations comparing basal shear stress (τb) with critical shear stress (τc). Baseflow τb is capable of entraining a d50 particle; bankfull flow could entrain a 51·2 mm particle. No particle that large occurs in the top 30 cm of the substrate, suggesting that the top 30 cm of the substrate is mobilized and redistributed during bankfull events. Bankfull events occur on average every 7·6 months; flows capable of entraining d50 and d85 particles occur on average every 0·85 and 2·1 months, respectively. Streambed surveys verify streambed mobility at conditions below bankfull. While higher gradient streams have higher potential energy than LKC, they achieve streambed‐mobilization thresholds less frequently. Heterogeneous sediment redistribution creates an environment where substrate hydraulic conductivity (K) varies over four orders of magnitude. The frequency and magnitude of the substrate entrainment has implications on hyporheic zone function in fluid, solute and thermal transport models, interpretations of hyporheic zone stability, and understanding of LKC's aquatic ecosystem. Copyright © 2008 John Wiley & Sons, Ltd.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction
Chapter 2 Self-Interference-Cancellation in Full-Duplex Systems

This chapter provides a brief overview of several important concepts related to SI-cancellation techniques to form a solid background for the following chapters. We first discuss the nature of the self-interference (SI) channel which leads to the use of the analog RF cancellation stage and the digital cancellation stage. We describe both stages and state their advantages and limitations. The next part presents a quick survey on self-interference channel estimation. We then discuss the transmitter impairments that can degrade the SI-cancellation performance and the existing methods to mitigate them. The last part presents recent advances in precoding for SI-cancellation. 2.1 SI Channel Modelling Various measurements have been done to characterise the SI channel. Consider the simple and popular architecture using the same antenna to transmit and receive via a 3-port circulator, the dominant paths of the SI channel come from the leakage through the circulator and the internal antenna reflections due to the impedance mismatch between the isolator and the antenna. On the other hand, external reflections from closely-located objects may occur with much larger delays and weaker levels compared to the dominant paths since they travel longer distances. It was reported in [1] that the external reflections are about 30 dB lower than the leakage and antenna reflections paths. When using two different antennas to transmit and receive, the line-of-sight (LoS) components and the path coming from the electromagnetic waves reflected from the transceiver structure represent the most significant paths [2, 3]. Figure 1.4 represents the different reflections that constitute the SI channel for the two antenna configurations. In both cases, the internal reflections are static since they depend on the structure of the transceiver while the external reflections vary according to the surrounding environment. In general, the power delay profile (PDP) of the SI channel is written as [1]: PDP.t; / D m1ı. t m1 / C m2ı. t m2 / C L X
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption

We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated independently from the distribution of the arm chosen at each time. However, these rewards are not directly observed. Instead, with a fixed $\varepsilon\in (0,\frac{1}{2})$, the agent observes a sample from the chosen arm's distribution with probability $1-\varepsilon$, or from an arbitrary corruption distribution with probability $\varepsilon$. Importantly, we impose no assumptions on these corruption distributions, which can be unbounded. In this setting, accommodating potentially unbounded corruptions, we establish a problem-dependent lower bound on regret for a given family of arm distributions. We introduce CRIMED, an asymptotically-optimal algorithm that achieves the exact lower bound on regret for bandits with Gaussian distributions with known variance. Additionally, we provide a finite-sample analysis of CRIMED's regret performance. Notably, CRIMED can effectively handle corruptions with $\varepsilon$ values as high as $\frac{1}{2}$. Furthermore, we develop a tight concentration result for medians in the presence of arbitrary corruptions, even with $\varepsilon$ values up to $\frac{1}{2}$, which may be of independent interest. We also discuss an extension of the algorithm for handling misspecification in Gaussian model.
Hybrid Volitional Control of a Robotic Transtibial Prosthesis using a Phase Variable Impedance Controller

For robotic transtibial prosthesis control, the global kinematics of the tibia can be used to monitor the progression of the gait cycle and command smooth and continuous actuation. In this work, these global tibia kinematics are used to define a phase variable impedance controller (PVIC), which is then implemented as the nonvolitional base controller within a hybrid volitional control framework (PVI-HVC). The gait progression estimation and biomechanic performance of one able-bodied individual walking on a robotic ankle prosthesis via a bypass adapter are compared for three control schemes: a passive benchmark controller, PVIC, and PVI-HVC. The different actuation of each controller had a direct effect on the global tibia kinematics, but the average deviation between the estimated and ground truth gait percentage were 1.6%, 1.8%, and 2.1%, respectively, for each controller. Both PVIC and PVI-HVC produced good agreement with able-bodied kinematic and kinetic references. As designed, PVI-HVC results were similar to those of PVIC when the user used low volitional intent, but yielded higher peak plantarflexion, peak torque, and peak power when the user commanded high volitional input in late stance. This additional torque and power also allowed the user to volitionally and continuously achieve activities beyond level walking, such as ascending ramps, avoiding obstacles, standing on tip-toes, and tapping the foot. In this way, PVI-HVC offers the kinetic and kinematic performance of the PVIC during level ground walking, along with the freedom to volitionally pursue alternative activities.
GAMMA: Generalizable Articulation Modeling and Manipulation for Articulated Objects

Articulated objects like cabinets and doors are widespread in daily life. However, directly manipulating 3D articulated objects is challenging because they have diverse geometrical shapes, semantic categories, and kinetic constraints. Prior works mostly focused on recognizing and manipulating articulated objects with specific joint types. They can either estimate the joint parameters or distinguish suitable grasp poses to facilitate trajectory planning. Although these approaches have succeeded in certain types of articulated objects, they lack generalizability to unseen objects, which significantly impedes their application in broader scenarios. In this paper, we propose a novel framework of Generalizable Articulation Modeling and Manipulating for Articulated Objects (GAMMA), which learns both articulation modeling and grasp pose affordance from diverse articulated objects with different categories. In addition, GAMMA adopts adaptive manipulation to iteratively reduce the modeling errors and enhance manipulation performance. We train GAMMA with the PartNet-Mobility dataset and evaluate with comprehensive experiments in SAPIEN simulation and real-world Franka robot. Results show that GAMMA significantly outperforms SOTA articulation modeling and manipulation algorithms in unseen and cross-category articulated objects. We will open-source all codes and datasets in both simulation and real robots for reproduction in the final version. Images and videos are published on the project website at: http://sites.google. com/view/gamma-articulation