Search

RAND researchers conducted a study that aimed to fill the information gap on how to successfully implement a statewide financial education mandate by studying the implementation of Nevada's 2017 financial literacy education reform. The study focused on the reform enacted by the Nevada Legislature in 2017, Senate Bill 249, which required that financial literacy education be implemented in third through 12th grade starting in the 2017-2018 school year. RAND researchers examined the following research questions to better understand Nevada's financial literacy reform and potential implications for strengthening financial literacy reforms more broadly: (1) After the 2017 Nevada mandate, how was financial literacy education implemented in schools?; (2) What were the main barriers and facilitators to financial literacy implementation across Nevada's schools?; and (3) How do educators, leaders, and policymakers perceive financial literacy education in Nevada?

More than half of U.S. states have enacted policies to expand financial literacy education in schools with kindergarten through grade 12 (K-12) with the goal of improving students' financial literacy skills. Although financial literacy education can be key to building long-term financial capabilities and well-being, more information is needed on how to effectively design state-level financial literacy education requirements. In this report, we examine the implementation of Nevada's financial literacy requirements in grades 3 through 12 using data from a teacher survey, focus groups, interviews, and financial literacy programs. We describe several key takeaways from the implementation process and recommendations for strengthening financial literacy education in K-12 schools. This report should be of interest to state policymakers, education leaders, teachers, and those working to advance youth financial literacy.

This report looks at the provision of GCSE subjects in England in 2021. Provision in a GCSE subject is defined as the number or percentage of schools with at least one student taking the subject. This may not cover all of the actual 'provision' since schools might offer to provide a subject, but none of their students wants to study it. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The dataset consisted of Key Stage 4 (KS4) entry numbers for all GCSE subjects in each school in England. Other school information, such as schools' sex composition, school type, and admissions policy were also available. However, some data (available in previous years) was not available in the 2021 dataset, including GCSE grades achieved and the percentage of disadvantaged students in each school. The decision was made to use data (for these tables only) from the 2019 data set. The report presents only the provision of GCSE subjects in schools with students taking GCSEs in 2021. Only GCSEs (9-1) full course (both single and double award) were counted for this report. Schools with at least 10 students taking GCSEs were included in the analysis. In this report, the level of provision is presented by the following school classifications: school type, school attainment, school deprivation level, school sex composition, and school size. However, it should be noted that the analysis is at the simplest level and takes no account of interactions between these classifications.

This report is focused on the uptake of GCSE subjects in England in 2021. Uptake in a GCSE subject is defined as the number or percentage of students at the end of Key Stage 4 (KS4) taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The data set consisted of KS4 entry numbers in all GCSE subjects in England. Other school information, such as schools' sex composition, school type, and admissions policy, was also available. However, some data (available in previous years) was not available in the 2021 dataset, including GCSE grades achieved and the percentage of disadvantaged students in each school. This data was not available because the DfE did not want schools to be held to account for their students' performance in 2021 due to the disruption in learning caused by the COVID-19 pandemic. This meant that the authors were not able to produce the tables in this report with breakdowns by school level attainment or deprivation using 2021 data. Instead, the decision was made to use data (for these tables only) from the 2019 data set. It was assumed that for most schools, the average attainment and the percentage of disadvantaged students would not change significantly in two years and so the 2019 data would be a reasonable proxy for 2021. Only GCSEs (9-1) full course (both single and double award) were counted for this report. GCSE uptake is presented by the following school classifications: school type, school attainment, school deprivation level, and school sex composition.

This report is focused on the uptake of GCSE subjects in England in 2022. Uptake in a GCSE subject is defined as the number or percentage of students at the end of Key Stage 4 (KS4) taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service. The dataset consisted of KS4 results in all subjects provided by each school in England. Other school information, such as schools' sex composition, admissions policy, and the number of disadvantaged pupils at the end of KS4 were also available. Only GCSEs (9-1) full course (both single and double award) were counted for this report. In this report, GCSE uptake is presented by the following school classifications: school type, school attainment, school deprivation level, and school sex composition.

This report looks at the provision of A level subjects in England in 2021. Provision in a subject is defined as the number or percentage of schools with at least one student taking the subject. This report was produced using publicly available data from the Department for Education's (DfE) "Find and compare schools in England" service and the dataset consisted of Key Stage 5 (KS5) entry numbers for all subjects provided in each school and college in England. Provision of other qualifications (e.g., Pre-U, International Baccalaureate, Cambridge Technicals, BTECs, etc.) was not investigated. The level of provision is presented by the following school classifications: school type, school attainment, school deprivation level, school sex composition, and school size.

Semi-implicit variational inference (SIVI) enriches the expressiveness of variational families by utilizing a kernel and a mixing distribution to hierarchically define the variational distribution. Existing SIVI methods parameterize the mixing distribution using implicit distributions, leading to intractable variational densities. As a result, directly maximizing the evidence lower bound (ELBO) is not possible and so, they resort to either: optimizing bounds on the ELBO, employing costly inner-loop Markov chain Monte Carlo runs, or solving minimax objectives. In this paper, we propose a novel method for SIVI called Particle Variational Inference (PVI) which employs empirical measures to approximate the optimal mixing distributions characterized as the minimizer of a natural free energy functional via a particle approximation of an Euclidean--Wasserstein gradient flow. This approach means that, unlike prior works, PVI can directly optimize the ELBO; furthermore, it makes no parametric assumption about the mixing distribution. Our empirical results demonstrate that PVI performs favourably against other SIVI methods across various tasks. Moreover, we provide a theoretical analysis of the behaviour of the gradient flow of a related free energy functional: establishing the existence and uniqueness of solutions as well as propagation of chaos results.

The physical mechanisms regulating the power sharing at the outer targets of L-mode double-null (DN) configurations are investigated using nonlinear, flux-driven, three-dimensional two-fluid simulations. Scans of parameters that regulate the turbulent level, such as the plasma resistivity and the magnetic imbalance, reveal that the power asymmetry in DN configurations is determined by the combined effects of diamagnetic drift, turbulence, and geometrical factor. Leveraging these observations, an analytical theory-based scaling law for the power-sharing asymmetry is derived and compared with nonlinear simulations. These comparisons indicate that the scaling law effectively captures the trends observed in simulations. Validation with experimental data from TCV DN discharges demonstrates agreement of the scaling law with the experimental results., Comment: 30 pages, 9 figures

We show that unconstrained quadratic optimization over a Grassmannian $\operatorname{Gr}(k,n)$ is NP-hard. Our results cover all scenarios: (i) when $k$ and $n$ are both allowed to grow; (ii) when $k$ is arbitrary but fixed; (iii) when $k$ is fixed at its lowest possible value $1$. We then deduce the NP-hardness of unconstrained cubic optimization over the Stiefel manifold $\operatorname{V}(k,n)$ and the orthogonal group $\operatorname{O}(n)$. As an addendum we demonstrate the NP-hardness of unconstrained quadratic optimization over the Cartan manifold, i.e., the positive definite cone $\mathbb{S}^n_{\scriptscriptstyle++}$ regarded as a Riemannian manifold, another popular example in manifold optimization. We will also establish the nonexistence of $\mathrm{FPTAS}$ in all cases., Comment: 19 pages

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been limited emphasis on capturing the vital long-term contextual information associated with emotional cognitive processes. In order to address this discrepancy, we introduce a novel transformer model called emotion transformer (EmT). EmT is designed to excel in both generalized cross-subject EEG emotion classification and regression tasks. In EmT, EEG signals are transformed into a temporal graph format, creating a sequence of EEG feature graphs using a temporal graph construction module (TGC). A novel residual multi-view pyramid GCN module (RMPG) is then proposed to learn dynamic graph representations for each EEG feature graph within the series, and the learned representations of each graph are fused into one token. Furthermore, we design a temporal contextual transformer module (TCT) with two types of token mixers to learn the temporal contextual information. Finally, the task-specific output module (TSO) generates the desired outputs. Experiments on four publicly available datasets show that EmT achieves higher results than the baseline methods for both EEG emotion classification and regression tasks. The code is available at https://github.com/yi-ding-cs/EmT., Comment: 11 pages, 5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Recognizing traversable terrain from 3D point cloud data is critical, as it directly impacts the performance of autonomous navigation in off-road environments. However, existing segmentation algorithms often struggle with challenges related to changes in data distribution, environmental specificity, and sensor variations. Moreover, when encountering sunken areas, their performance is frequently compromised, and they may even fail to recognize them. To address these challenges, we introduce B-TMS, a novel approach that performs map-wise terrain modeling and segmentation by utilizing Bayesian generalized kernel (BGK) within the graph structure known as the tri-grid field (TGF). Our experiments encompass various data distributions, ranging from single scans to partial maps, utilizing both public datasets representing urban scenes and off-road environments, and our own dataset acquired from extremely bumpy terrains. Our results demonstrate notable contributions, particularly in terms of robustness to data distribution variations, adaptability to diverse environmental conditions, and resilience against the challenges associated with parameter changes., Comment: Accepted by IEEE IV'24 workshop on Off-road autonomy

Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. Instead, RL agents that can act over useful, temporally extended skills rather than low-level actions can learn new tasks more easily. Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL. Our approach, EXTRACT, instead utilizes pre-trained vision language models to extract a discrete set of semantically meaningful skills from offline data, each of which is parameterized by continuous arguments, without human supervision. This skill parameterization allows robots to learn new tasks by only needing to learn when to select a specific skill and how to modify its arguments for the specific task. We demonstrate through experiments in sparse-reward, image-based, robot manipulation environments that EXTRACT can more quickly learn new tasks than prior works, with major gains in sample efficiency and performance over prior skill-based RL. Website at https://www.jessezhang.net/projects/extract/., Comment: 25 pages, 16 figures

Experiments to explore the predicted consequences on the I-V characteristics of a Langmuir probe based on the fluid approach for electrons and the electron Bohm criterion are conducted and their results are presented. The predictions on the I-V characteristics of a Langmuir probe when electrons are highly collisional are shown in terms of the estimation of plasma potentials and electron saturation currents, primarily represented by a rounded knee of the curve. An edge-effect reduced and guard-ringed Langmuir probe is employed to minimize geometrical effects on the measured data. To characterize the rounded knee of the curve, various methods on defining critical points around the plasma potential are presented. Additionally, emissive and cutoff probes were utilized to obtain bulk plasma properties, providing accurate reference plasma parameters. The physically valid condition for electrons to exhibit a shifted Maxwellian velocity distribution near the wall, wherein the fluid approach is applicable, is established from theory of the ion-acoustic instability enhanced electron-electron collisions. In this work, we conducted experiments scanning from collisionless to collisional regimes for electrons with theoretical considerations. Our observation indicate that the Langmuir probe can be understood with the conventional wisdom represented by the model of truncated electron velocity distribution. This result suggests the absence of the enhancement of electron-electron collisions and/or incompleteness of electron fluid approach near the wall.

In portfolio risk minimization, the inverse covariance matrix of returns is often unknown and has to be estimated in practice. This inverse covariance matrix also prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity gives rise to considerable estimation errors, making the hedge trades too unstable and unreliable for use. By adopting ideas from current methodologies in the existing literature, we propose 2 new estimators of the inverse covariance matrix, one which relies only on the l2 norm while the other utilizes both the l1 and l2 norms. These 2 new estimators are classified as shrinkage estimators in the literature. Comparing favorably with other methods (sample-based estimation, equal-weighting, estimation based on Principal Component Analysis), a portfolio formed on the proposed estimators achieves substantial out-of-sample risk reduction and improves the out-of-sample risk-adjusted returns of the portfolio, particularly in high-dimensional settings. Furthermore, the proposed estimators can still be computed even in instances where the sample covariance matrix is ill-conditioned or singular, Comment: B.Sc. Dissertation

Controlling and channelling light emissions from unpolarized quantum dots into specific directions with chiral polarization remains a key challenge in modern photonics. Stacked metasurface designs offer a potential compact solution for chirality and directionality engineering. However, experimental observations of directional chiral radiation from resonant metasurfaces with quantum emitters remain obscure. In this paper, we present experimental observations of unidirectional chiral emission from a twisted bi-layer metasurface via multi-dimensional control, including twist angle, interlayer distance, and lateral displacement between the top and bottom layers, as enabled by doublet alignment lithography (DAL). First, maintaining alignment, the metasurface demonstrates a resonant intrinsic optical chirality with near-unity circular dichroism of 0.94 and reflectance difference of 74%, where a high circular dichroism greater than 0.9 persists across a wide range of angles from -11 to 11 degrees. Second, engineered lateral displacement induces a unidirectional chiral resonance, resulting in unidirectional chiral emission from the quantum dots deposited onto the metasurface. Our bi-layer metasurfaces offer a universal compact platform for efficient radiation manipulation over a wide angular range, promising potential applications in miniaturized lasers, grating couplers, and chiral nanoantennas., Comment: 16 pages, 4 figures

Evaluating LLMs and text-to-image models is a computationally intensive task often overlooked. Efficient evaluation is crucial for understanding the diverse capabilities of these models and enabling comparisons across a growing number of new models and benchmarks. To address this, we introduce SubLIME, a data-efficient evaluation framework that employs adaptive sampling techniques, such as clustering and quality-based methods, to create representative subsets of benchmarks. Our approach ensures statistically aligned model rankings compared to full datasets, evidenced by high Pearson correlation coefficients. Empirical analysis across six NLP benchmarks reveals that: (1) quality-based sampling consistently achieves strong correlations (0.85 to 0.95) with full datasets at a 10\% sampling rate such as Quality SE and Quality CPD (2) clustering methods excel in specific benchmarks such as MMLU (3) no single method universally outperforms others across all metrics. Extending this framework, we leverage the HEIM leaderboard to cover 25 text-to-image models on 17 different benchmarks. SubLIME dynamically selects the optimal technique for each benchmark, significantly reducing evaluation costs while preserving ranking integrity and score distribution. Notably, a minimal sampling rate of 1% proves effective for benchmarks like MMLU. Additionally, we demonstrate that employing difficulty-based sampling to target more challenging benchmark segments enhances model differentiation with broader score distributions. We also combine semantic search, tool use, and GPT-4 review to identify redundancy across benchmarks within specific LLM categories, such as coding benchmarks. This allows us to further reduce the number of samples needed to maintain targeted rank preservation. Overall, SubLIME offers a versatile and cost-effective solution for the robust evaluation of LLMs and text-to-image models.

The large-scale pre-trained neural network has achieved notable success in enhancing performance for downstream tasks. Another promising approach for generalization is Bayesian Neural Network (BNN), which integrates Bayesian methods into neural network architectures, offering advantages such as Bayesian Model averaging (BMA) and uncertainty quantification. Despite these benefits, transfer learning for BNNs has not been widely investigated and shows limited improvement. We hypothesize that this issue arises from the inability to find flat minima, which is crucial for generalization performance. To address this, we evaluate the sharpness of BNNs in various settings, revealing their insufficiency in seeking flat minima and the influence of flatness on BMA performance. Therefore, we propose Sharpness-aware Bayesian Model Averaging (SA-BMA), a Bayesian-fitting flat posterior seeking optimizer integrated with Bayesian transfer learning. SA-BMA calculates the divergence between posteriors in the parameter space, aligning with the nature of BNNs, and serves as a generalized version of existing sharpness-aware optimizers. We validate that SA-BMA improves generalization performance in few-shot classification and distribution shift scenarios by ensuring flatness.

LHS 1140 b is the second-closest temperate transiting planet to the Earth with an equilibrium temperature low enough to support surface liquid water. At 1.730$\pm$0.025 R$_\oplus$, LHS 1140 b falls within the radius valley separating H$_2$-rich mini-Neptunes from rocky super-Earths. Recent mass and radius revisions indicate a bulk density significantly lower than expected for an Earth-like rocky interior, suggesting that LHS 1140 b could either be a mini-Neptune with a small envelope of hydrogen ($\sim$0.1% by mass) or a water world (9--19% water by mass). Atmospheric characterization through transmission spectroscopy can readily discern between these two scenarios. Here, we present two JWST/NIRISS transit observations of LHS 1140 b, one of which captures a serendipitous transit of LHS 1140 c. The combined transmission spectrum of LHS 1140 b shows a telltale spectral signature of unocculted faculae (5.8 $\sigma$), covering $\sim$20% of the visible stellar surface. Besides faculae, our spectral retrieval analysis reveals tentative evidence of residual spectral features, best-fit by Rayleigh scattering from an N$_2$-dominated atmosphere (2.3 $\sigma$), irrespective of the consideration of atmospheric hazes. We also show through Global Climate Models (GCM) that H$_2$-rich atmospheres of various compositions (100$\times$, 300$\times$, 1000$\times$solar metallicity) are ruled out to $>$10 $\sigma$. The GCM calculations predict that water clouds form below the transit photosphere, limiting their impact on transmission data. Our observations suggest that LHS 1140 b is either airless or, more likely, surrounded by an atmosphere with a high mean molecular weight. Our tentative evidence of an N$_2$-rich atmosphere provides strong motivation for future transmission spectroscopy observations of LHS 1140 b., Comment: 26 pages, 14 figures, 3 tables, accepted for publication in ApJL

In this letter, we propose a robust and fast navigation system in a narrow indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR and odometry. We used behavior cloning with Transformer neural network to learn the optimization-based baseline algorithm. We inject Gaussian noise during expert demonstration to increase the robustness of learned policy. We evaluate the performance of LiCS using both simulation and hardware experiments. It outperforms all other baselines in terms of navigation performance and can maintain its robust performance even on highly cluttered environments. During the hardware experiments, LiCS can maintain safe navigation at maximum speed of $1.5\ m/s$., Comment: 6 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discovery methods that leverage basic visual cues to identify salient objects, consistently achieving a high recall rate remains difficult due to the diversity of object types and their contextual complexity. In this work, we investigate using vision-language models (VLMs) to enhance object detection via a self-supervised prompt learning strategy. Our initial findings indicate that manually crafted text queries often result in undetected objects, primarily because detection confidence diminishes when the query words exhibit semantic overlap. To address this, we propose a Dispersing Prompt Expansion (DiPEx) approach. DiPEx progressively learns to expand a set of distinct, non-overlapping hyperspherical prompts to enhance recall rates, thereby improving performance in downstream tasks such as out-of-distribution OD. Specifically, DiPEx initiates the process by self-training generic parent prompts and selecting the one with the highest semantic uncertainty for further expansion. The resulting child prompts are expected to inherit semantics from their parent prompts while capturing more fine-grained semantics. We apply dispersion losses to ensure high inter-class discrepancy among child prompts while preserving semantic consistency between parent-child prompt pairs. To prevent excessive growth of the prompt sets, we utilize the maximum angular coverage (MAC) of the semantic space as a criterion for early termination. We demonstrate the effectiveness of DiPEx through extensive class-agnostic OD and OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by up to 20.1% in AR and achieving a 21.3% AP improvement over SAM. The code is available at https://github.com/jason-lim26/DiPEx., Comment: 19 pages

The idea of personality in descriptive psychology, traditionally defined through observable behavior, has now been extended to Large Language Models (LLMs) to better understand their behavior. This raises a question: do LLMs exhibit distinct and consistent personality traits, similar to humans? Existing self-assessment personality tests, while applicable, lack the necessary validity and reliability for precise personality measurements. To address this, we introduce TRAIT, a new tool consisting of 8K multi-choice questions designed to assess the personality of LLMs with validity and reliability. TRAIT is built on the psychometrically validated human questionnaire, Big Five Inventory (BFI) and Short Dark Triad (SD-3), enhanced with the ATOMIC10X knowledge graph for testing personality in a variety of real scenarios. TRAIT overcomes the reliability and validity issues when measuring personality of LLM with self-assessment, showing the highest scores across three metrics: refusal rate, prompt sensitivity, and option order sensitivity. It reveals notable insights into personality of LLM: 1) LLMs exhibit distinct and consistent personality, which is highly influenced by their training data (i.e., data used for alignment tuning), and 2) current prompting techniques have limited effectiveness in eliciting certain traits, such as high psychopathy or low conscientiousness, suggesting the need for further research in this direction., Comment: Preprint; Under review

Mobile robots operating in crowded environments require the ability to navigate among humans and surrounding obstacles efficiently while adhering to safety standards and socially compliant mannerisms. This scale of the robot navigation problem may be classified as both a local path planning and trajectory optimization problem. This work presents an array of force sensors that act as a tactile layer to complement the use of a LiDAR for the purpose of inducing awareness of contact with any surrounding objects within immediate vicinity of a mobile robot undetected by LiDARs. By incorporating the tactile layer, the robot can take more risks in its movements and possibly go right up to an obstacle or wall, and gently squeeze past it. In addition, we built up a simulation platform via Pybullet which integrates Robot Operating System (ROS) and reinforcement learning (RL) together. A touch-aware neural network model was trained on it to create an RL-based local path planner for dynamic obstacle avoidance. Our proposed method was demonstrated successfully on an omni-directional mobile robot who was able to navigate in a crowded environment with high agility and versatility in movement, while not being overly sensitive to nearby obstacles-not-in-contact.

Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the mechanisms underlying this phenomenon are not fully elucidated, complicating the design of proton-conductive MOFs. In response, we developed a comprehensive database of proton-conductive MOFs and applied machine learning techniques to predict their proton conductivity. Our approach included the construction of both descriptor-based and transformer-based models. Notably, the transformer-based transfer learning (Freeze) model performed the best with a mean absolute error (MAE) of 0.91, suggesting that the proton conductivity of MOFs can be estimated within one order of magnitude using this model. Additionally, we employed feature importance and principal component analysis to explore the factors influencing proton conductivity. The insights gained from our database and machine learning model are expected to facilitate the targeted design of proton-conductive MOFs.

The dynamics of a quantum self-sustained oscillator as it evolves toward its limit cycle may be useful in solving related problems like those in quantum synchronization, yet is inadequately studied. Here we investigate the evolution of a quantum Rayleigh-van der Pol (RvdP) oscillator, the simplest form of a self-sustained oscillator exhibiting a quasiharmonic limit cycle, starting from Fock, thermal, and coherent states. We find that the phase-space dynamics significantly differ depending on the initial state -- one evolution toward the limit cycle may take much longer than another and a least-time parameter may be present. We describe the resulting dynamics in terms of the coherence decay and the redistribution of eigenstate occupation., Comment: 6 pages, 4 figures

Excitonic semiconductors are attractive for next-generation photovoltaics (PVs) with lower cost, lighter weight, and lower material consumption than conventional technologies. Among them, transition metal dichalcogenide materials like WS2 are especially interesting due to exceptionally strong light-matter interaction. Photocurrent generation in excitonic PVs relies on exciton diffusion to heterointerfaces. However, efficiencies of excitonic PVs are often limited by short exciton diffusion lengths. Here we report that the strong coupling of excitons to cavity photons in a WS2 absorber layer can enhance the external quantum efficiency by a factor of >10, internal quantum efficiency by a factor of ~3, and power conversion efficiency of excitonic PVs by an order of magnitude. The resulting hybrid states, exciton-polaritons, enhance the resonant absorption and exciton transport while the use of the WS2 layer as its own optical cavity enables broadband absorption. Thickness dependent device characterization reveals anomalous internal quantum efficiency and fill factor behavior that are attributed to novel exciton-polariton transport. Exciton-polariton enhanced transport occurs for both resonant and off-resonant excitation, emphasizing the value and practicality of the self-hybridized device structure. Our work presents a route towards excitonic PVs with broadband absorption and improved exciton transport without strict requirements of donor/acceptor structure of other excitonic PVs.

3D object detection is crucial for applications like autonomous driving and robotics. However, in real-world environments, variations in sensor data distribution due to sensor upgrades, weather changes, and geographic differences can adversely affect detection performance. Semi-Supervised Domain Adaptation (SSDA) aims to mitigate these challenges by transferring knowledge from a source domain, abundant in labeled data, to a target domain where labels are scarce. This paper presents a new SSDA method referred to as Target-Oriented Domain Augmentation (TODA) specifically tailored for LiDAR-based 3D object detection. TODA efficiently utilizes all available data, including labeled data in the source domain, and both labeled data and unlabeled data in the target domain to enhance domain adaptation performance. TODA consists of two stages: TargetMix and AdvMix. TargetMix employs mixing augmentation accounting for LiDAR sensor characteristics to facilitate feature alignment between the source-domain and target-domain. AdvMix applies point-wise adversarial augmentation with mixing augmentation, which perturbs the unlabeled data to align the features within both labeled and unlabeled data in the target domain. Our experiments conducted on the challenging domain adaptation tasks demonstrate that TODA outperforms existing domain adaptation techniques designed for 3D object detection by significant margins. The code is available at: https://github.com/rasd3/TODA., Comment: Accepted to IEEE Transactions on Intelligent Vehicles (T-IV). The code is available at: https://github.com/rasd3/TODA

We show that modeling a Grassmannian as symmetric orthogonal matrices $\operatorname{Gr}(k,\mathbb{R}^n) \cong\{Q \in \mathbb{R}^{n \times n} : Q^{\scriptscriptstyle\mathsf{T}} Q = I, \; Q^{\scriptscriptstyle\mathsf{T}} = Q,\; \operatorname{tr}(Q)=2k - n\}$ yields exceedingly simple matrix formulas for various curvatures and curvature-related quantities, both intrinsic and extrinsic. These include Riemann, Ricci, Jacobi, sectional, scalar, mean, principal, and Gaussian curvatures; Schouten, Weyl, Cotton, Bach, Pleba\'nski, cocurvature, nonmetricity, and torsion tensors; first, second, and third fundamental forms; Gauss and Weingarten maps; and upper and lower delta invariants. We will derive explicit, simple expressions for the aforementioned quantities in terms of standard matrix operations that are stably computable with numerical linear algebra. Many of these aforementioned quantities have never before been presented for the Grassmannian., Comment: 25 pages

We study the visual semantic embedding problem for image-text matching. Most existing work utilizes a tailored cross-attention mechanism to perform local alignment across the two image and text modalities. This is computationally expensive, even though it is more powerful than the unimodal dual-encoder approach. This work introduces a dual-encoder image-text matching model, leveraging a scene graph to represent captions with nodes for objects and attributes interconnected by relational edges. Utilizing a graph attention network, our model efficiently encodes object-attribute and object-object semantic relations, resulting in a robust and fast-performing system. Representing caption as a scene graph offers the ability to utilize the strong relational inductive bias of graph neural networks to learn object-attribute and object-object relations effectively. To train the model, we propose losses that align the image and caption both at the holistic level (image-caption) and the local level (image-object entity), which we show is key to the success of the model. Our model is termed Composition model for Object Relations and Attributes, CORA. Experimental results on two prominent image-text retrieval benchmarks, Flickr30K and MSCOCO, demonstrate that CORA outperforms existing state-of-the-art computationally expensive cross-attention methods regarding recall score while achieving fast computation speed of the dual encoder., Comment: Accepted to CVPR'24

In this report, we propose three novel methods for developing a sound event detection (SED) model for the DCASE 2024 Challenge Task 4. First, we propose an auxiliary decoder attached to the final convolutional block to improve feature extraction capabilities while reducing dependency on embeddings from pre-trained large models. The proposed auxiliary decoder operates independently from the main decoder, enhancing performance of the convolutional block during the initial training stages by assigning a different weight strategy between main and auxiliary decoder losses. Next, to address the time interval issue between the DESED and MAESTRO datasets, we propose maximum probability aggregation (MPA) during the training step. The proposed MPA method enables the model's output to be aligned with soft labels of 1 s in the MAESTRO dataset. Finally, we propose a multi-channel input feature that employs various versions of logmel and MFCC features to generate time-frequency pattern. The experimental results demonstrate the efficacy of these proposed methods in a view of improving SED performance by achieving a balanced enhancement across different datasets and label types. Ultimately, this approach presents a significant step forward in developing more robust and flexible SED models, Comment: DCASE 2024 challenge Task4, 4 pages

Radiance fields produce high fidelity images with high rendering speed, but are difficult to manipulate. We effectively perform avatar texture transfer across different appearances by combining benefits from radiance fields and mesh surfaces. We represent the source as a radiance field using 3D Gaussian Splatter, then project the Gaussians on the target mesh. Our pipeline consists of Source Preconditioning, Target Vectorization and Texture Projection. The projection completes in 1.12s in a pure CPU compute, compared to baselines techniques of Per Face Texture Projection and Ray Casting (31s, 4.1min). This method lowers the computational requirements, which makes it applicable to a broader range of devices from low-end mobiles to high end computers., Comment: SIGGRAPH Posteres 2024

Energy-efficient spintronic devices require a large spin-orbit torque (SOT) and low damping to excite magnetic precession. In conventional devices with heavy-metal/ferromagnet bilayers, reducing the ferromagnet thickness to $\sim$1 nm enhances the SOT but dramatically increases damping. Here, we investigate an alternative approach based on a 10 nm thick single-layer ferromagnet to attain both low damping and a sizable SOT. Instead of relying on a single interface, we continuously break the bulk inversion symmetry with a vertical compositional gradient of two ferromagnetic elements: Fe with low intrinsic damping and Ni with sizable spin-orbit coupling. We find low effective damping parameters of $\alpha_\mathrm{eff} < 5\times10^{-3}$ in the FeNi alloy films, despite the steep compositional gradients. Moreover, we reveal a sizable anti-damping SOT efficiency of $\theta_\mathrm{AD} \approx 0.05$, even without an intentional compositional gradient. Through depth-resolved x-ray diffraction, we identify a lattice strain gradient as crucial symmetry breaking that underpins the SOT. Our findings provide fresh insights into damping and SOTs in single-layer ferromagnets for power-efficient spintronic devices.