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We survey undergraduate students at a large public university to understand the pecuniary and non-pecuniary factors driving their college major and career decisions with a focus on K-12 teaching. While the average student reports there is a 6% chance they will pursue teaching, almost 27% report a nonzero chance of working as a teacher in the future. Students, relative to existing statistics, generally believe they would earn substantially more in a non-teaching job (relative to a teaching job). We run a randomized information experiment where we provide students with information on the pecuniary and non-pecuniary job characteristics of teachers and non-teachers. This low-cost informational intervention impacts students' beliefs about their job characteristics if they were to work as a teacher or non-teacher, and increases the reported likelihood they will major or minor in education by 35% and pursue a job as a teacher or in education by 14%. Linking the survey data with administrative transcript records, we find that the intervention had small (and weak) impacts on the decision to minor in education in the subsequent year. Overall, our results indicate that students hold biased beliefs about their career prospects, they update these beliefs when provided with information, and that this information has limited impacts on their choices regarding studying and having a career in teaching.

Social robot navigation (SRN) is a relevant problem that involves navigating a pedestrian-rich environment in a socially acceptable manner. It is an essential part of making social robots effective in pedestrian-rich settings. The use cases of such robots could vary from companion robots to warehouse robots to autonomous wheelchairs. In recent years, deep reinforcement learning has been increasingly used in research on social robot navigation. Our work introduces CAMRL (Context-Aware Mamba-based Reinforcement Learning). Mamba is a new deep learning-based State Space Model (SSM) that has achieved results comparable to transformers in sequencing tasks. CAMRL uses Mamba to determine the robot's next action, which maximizes the value of the next state predicted by the neural network, enabling the robot to navigate effectively based on the rewards assigned. We evaluate CAMRL alongside existing solutions (CADRL, LSTM-RL, SARL) using a rigorous testing dataset which involves a variety of densities and environment behaviors based on ORCA and SFM, thus, demonstrating that CAMRL achieves higher success rates, minimizes collisions, and maintains safer distances from pedestrians. This work introduces a new SRN planner, showcasing the potential for deep-state space models for robot navigation.

Symmetric Multi-Processing (SMP) based on cache coherency is crucial for high-end embedded systems like automotive applications. RISC-V is gaining traction, and open-source hardware (OSH) platforms offer solutions to issues such as IP costs and vendor dependency. Existing multi-core cache-coherent RISC-V platforms are complex and not efficient for small embedded core clusters. We propose an open-source SystemVerilog implementation of a lightweight snoop-based cache-coherent cluster of Linux-capable CVA6 cores. Our design uses the MOESI protocol via the Arm's AMBA ACE protocol. Evaluated with Splash-3 benchmarks, our solution shows up to 32.87% faster performance in a dual-core setup and an average improvement of 15.8% over OpenPiton. Synthesized using GF 22nm FDSOI technology, the Cache Coherency Unit occupies only 1.6% of the system area., Comment: 4 pages, 4 figures, DSD2024 and SEAA2024 Works in Progress Session AUG 2024; Updated the acknowledgments

We observe that the performance of SOTA visual trackers surprisingly strongly varies across different video attributes and datasets. No single tracker remains the best performer across all tracking attributes and datasets. To bridge this gap, for a given video sequence, we predict the "Best of the N Trackers", called the BofN meta-tracker. At its core, a Tracking Performance Prediction Network (TP2N) selects a predicted best performing visual tracker for the given video sequence using only a few initial frames. We also introduce a frame-level BofN meta-tracker which keeps predicting best performer after regular temporal intervals. The TP2N is based on self-supervised learning architectures MocoV2, SwAv, BT, and DINO; experiments show that the DINO with ViT-S as a backbone performs the best. The video-level BofN meta-tracker outperforms, by a large margin, existing SOTA trackers on nine standard benchmarks - LaSOT, TrackingNet, GOT-10K, VOT2019, VOT2021, VOT2022, UAV123, OTB100, and WebUAV-3M. Further improvement is achieved by the frame-level BofN meta-tracker effectively handling variations in the tracking scenarios within long sequences. For instance, on GOT-10k, BofN meta-tracker average overlap is 88.7% and 91.1% with video and frame-level settings respectively. The best performing tracker, RTS, achieves 85.20% AO. On VOT2022, BofN expected average overlap is 67.88% and 70.98% with video and frame level settings, compared to the best performing ARTrack, 64.12%. This work also presents an extensive evaluation of competitive tracking methods on all commonly used benchmarks, following their protocols. The code, the trained models, and the results will soon be made publicly available on https://github.com/BasitAlawode/Best_of_N_Trackers.

The aim of this paper is to introduce a generalization of Steiner symmetrization in Euclidean space for spherical space, which is the dual of the Steiner symmetrization in hyperbolic space introduced by Peyerimhoff (J. London Math. Soc. (2) 66: 753-768, 2002). We show that this symmetrization preserves volume in every dimension, and convexity in the spherical plane, but not in dimensions $n > 2$. In addition, we investigate the monotonicity properties of the perimeter and diameter of a set under this process, and find conditions under which the image of a spherically convex disk under a suitable sequence of Steiner symmetrizations converges to a spherical cap. We apply our results to prove a spherical analogue of a theorem of Sas, and to confirm a conjecture of Besau and Werner (Adv. Math. 301: 867-901, 2016) for centrally symmetric spherically convex disks. We also prove a spherical variant of a theorem of Winternitz., Comment: 27 pages, 3 figures

Radio frequency (RF) wireless power transfer (WPT) is a key technology for future low-power wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is challenging for practical applications. The main factors contributing to it are the channel losses, transceivers' power consumption, and losses related, e.g., to the digital-to-analog converter (DAC), high-power amplifier, and rectenna. Optimizing PTE requires careful consideration of these factors, motivating the current work. Herein, we consider an analog multi-antenna power transmitter that aims to charge a single energy harvester. We first provide a mathematical framework to calculate the harvested power from multi-tone signal transmissions and the system power consumption. Then, we formulate the joint waveform and analog beamforming design problem to minimize power consumption and meet the charging requirements. Finally, we propose an optimization approach relying on swarm intelligence to solve the specified problem. Simulation results quantify the power consumption reduction as the DAC, phase shifters resolution, and antenna length are increased, while it is seen that increasing system frequency results in higher power consumption., Comment: Conference

Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices., Comment: 8 pages, 22 figures, Paper accepted at ICARCV 2024, funded by CAIR

Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues by unleashing the potential of a transformer's in-context learning ability. Diverging from existing methods, which primarily belong to the parametric paradigm and often require sufficient training samples, our work exploits the value of the available full-modality data, offering a novel perspective on resolving the challenge. The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks. When only 1% of the training data are available, our proposed method demonstrates an average improvement of 6.1% over a recent strong baseline across various datasets and missing states. Notably, our method also reduces the performance gap between full-modality and missing-modality data compared with the baseline.

An eight-partition of a finite set of points (respectively, of a continuous mass distribution) in $\mathbb{R}^3$ consists of three planes that divide the space into $8$ octants, such that each open octant contains at most $1/8$ of the points (respectively, of the mass). In 1966, Hadwiger showed that any mass distribution in $\mathbb{R}^3$ admits an eight-partition; moreover, one can prescribe the normal direction of one of the three planes. The analogous result for finite point sets follows by a standard limit argument. We prove the following variant of this result: Any mass distribution (or point set) in $\mathbb{R}^3$ admits an eight-partition for which the intersection of two of the planes is a line with a prescribed direction. Moreover, we present an efficient algorithm for calculating an eight-partition of a set of $n$ points in $\mathbb{R}^3$ (with prescribed normal direction of one of the planes) in time $O^{*}(n^{5/2})$., Comment: 22 pages, 3 figures, preliminary version to appear in SoCG'24; this version includes updated grant information

Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities. However, deploying LLMs in resource-constrained edge computing and embedded systems presents significant challenges. Another challenge lies in delivering medical assistance in remote areas with limited healthcare facilities and infrastructure. To address this, we introduce MedAide, an on-premise healthcare chatbot. It leverages tiny-LLMs integrated with LangChain, providing efficient edge-based preliminary medical diagnostics and support. MedAide employs model optimizations for minimal memory footprint and latency on embedded edge devices without server infrastructure. The training process is optimized using low-rank adaptation (LoRA). Additionally, the model is trained on diverse medical datasets, employing reinforcement learning from human feedback (RLHF) to enhance its domain-specific capabilities. The system is implemented on various consumer GPUs and Nvidia Jetson development board. MedAide achieves 77\% accuracy in medical consultations and scores 56 in USMLE benchmark, enabling an energy-efficient healthcare assistance platform that alleviates privacy concerns due to edge-based deployment, thereby empowering the community., Comment: 7 pages, 11 figures, ACM conference paper, 33 references

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL framework, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.

Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data sources for numerous medical studies. EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time series containing a significant number of missing values and irregular time intervals. To address these issues, we introduce HgbNet, a machine learning-based prediction model that emulates clinicians' decision-making processes for hemoglobin level/anemia degree prediction. The model incorporates a NanDense layer with a missing indicator to handle missing values and employs attention mechanisms to account for both local irregularity and global irregularity. We evaluate the proposed method using two real-world datasets across two use cases. In our first use case, we predict hemoglobin level/anemia degree at moment T+1 by utilizing records from moments prior to T+1. In our second use case, we integrate all historical records with additional selected test results at moment T+1 to predict hemoglobin level/anemia degree at the same moment, T+1. HgbNet outperforms the best baseline results across all datasets and use cases. These findings demonstrate the feasibility of estimating hemoglobin levels and anemia degree from EHR data, positioning HgbNet as an effective non-invasive anemia diagnosis solution that could potentially enhance the quality of life for millions of affected individuals worldwide. To our knowledge, HgbNet is the first machine learning model leveraging EHR data for hemoglobin level/anemia degree prediction.

The dynamics of quantum discord is studied in a system of two identical noninteracting qubits coupled to a common squeezed vacuum bath through non-demolition interactions. We concern on how reservoir squeezing influences the dynamical behaviors of quantum discord when both qubits are initially prepared in $X$-type states. We find that the critical time exhibits the sudden change of quantum discord, which is of great significance for the quantum discord amplification. Furthermore, depending on the initial parameters of the system, we numerically calculate the interval when the critical time is finite or infinite. For the finite critical time, we show that the squeezing phase of the bath can prolong the critical time while the squeezing strength exhibits the opposite effect. For infinite critical time, even if there is no sudden transition point, reservoir squeezing still has an effect on the amplification of quantum discord, and the time to reach steady-state quantum discord can be changed by adjusting the squeezing parameters. Fianlly, we investigate the quantum speed limit time for a two-qubit system under squeezed reservoir, and find that the quantum speed limit time can be reduced via the adjustment of the squeezing parameters and the initial parameters. Remarkably, in the short time limit, reservoir squeezing has an obvious influence on the degree of amplification of quantum discord. Our study presents a promising approach to controlling the amplification of quantum correlation., Comment: 12 pages, 8 figures

Despite existing for millennia, tuberculosis (TB) remains a persistent global health challenge. A significant obstacle in controlling TB spread is the need for a rapid, portable, sensitive, and accurate diagnostic test. Currently, sputum culture stands as a benchmark test for TB diagnosis. Although highly reliable, it necessitates advanced laboratory facilities and involves considerable testing time. In this context, we present a rapid, portable, and cost-effective optical fiber sensor designed to measure lipoarabinomannan (LAM), a TB biomarker found in patients' urine samples. Our sensing approach is based on the applications of phase shift-cavity ringdown spectroscopy (PS-CRDS) to an optical fiber cavity created by two fiber Bragg gratings. A tapered fiber is spliced inside the optical cavity to serve as the sensing head. We functionalize the tapered fiber surface with anti-LAM antigen CS-35 through a unique chemistry, creating a strong affinity for LAM molecules. We measure the phase difference between the cavity transmission and the reference modulating signal at the cavity output. The measured phase is directly proportional to the injected LAM concentrations in aqueous solutions over the sensing head. Our demonstrated sensor provides a detection limit of 10 pg/mL and a sensitivity of 2.6$^\circ$/ng/mL. This sensor holds promise for numerous applications in the healthcare sector, particularly in low-resource settings., Comment: This paper is submitted to Biomedical Optics Express on 28 Dec 2023