Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Observing in Continuous-time Control

The control of continuous-time environments while actively deciding when to take costly observations in time is a crucial yet unexplored problem, particularly relevant to real-world scenarios such as medicine, low-power systems, and resource management. Existing approaches either rely on continuous-time control methods that take regular, expensive observations in time or discrete-time control with costly observation methods, which are inapplicable to continuous-time settings due to the compounding discretization errors introduced by time discretization. In this work, we are the first to formalize the continuous-time control problem with costly observations. Our key theoretical contribution shows that observing at regular time intervals is not optimal in certain environments, while irregular observation policies yield higher expected utility. This perspective paves the way for the development of novel methods that can take irregular observations in continuous-time control with costly observations. We empirically validate our theoretical findings in various continuous-time environments, including a cancer simulation, by constructing a simple initial method to solve this new problem, with a heuristic threshold on the variance of reward rollouts in an offline continuous-time model-based model predictive control (MPC) planner. Although determining the optimal method remains an open problem, our work offers valuable insights and understanding of this unique problem, laying the foundation for future research in this area.
Learning ultrasound plane pose regression: assessing generalized pose coordinates in the fetal brain

—In obstetric ultrasound (US) scanning, the learner’s ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a signiﬁcant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily-oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 3.53 mm and 6.42° for translation and rotation, respectively.
Provably Efficient Offline Reinforcement Learning in Regular Decision Processes

This paper deals with offline (or batch) Reinforcement Learning (RL) in episodic Regular Decision Processes (RDPs). RDPs are the subclass of Non-Markov Decision Processes where the dependency on the history of past events can be captured by a finite-state automaton. We consider a setting where the automaton that underlies the RDP is unknown, and a learner strives to learn a near-optimal policy using pre-collected data, in the form of non-Markov sequences of observations, without further exploration. We present RegORL , an algorithm that suitably combines automata learning techniques and state-of-the-art algorithms for offline RL in MDPs. RegORL has a modular design allowing one to use any off-the-shelf offline RL al-gorithm in MDPs. We report a non-asymptotic high-probability sample complexity bound for RegORL to yield an ε -optimal policy, which makes appear a notion of concentrability relevant for RDPs. Furthermore, we present a sample complexity lower bound for offline RL in RDPs. To our best knowledge, this is the first work presenting a provably efficient algorithm for offline learning in RDPs.
STR : A Simple and E cient Algorithm for R-Tree Packing

In this paper we present the results from an extensive comparison study of three R-tree packing algorithms, including a new easy to implement algorithm. The algorithms are evaluated using both synthetic and actual data from various application domains including VLSI design, GIS (tiger), and computational uid dynamics. Our studies also consider the impact that various degrees of bu ering have on query performance. Experimental results indicate that none of the algorithms is best for all types of data. In general, our new algorithm requires up to 50% fewer disk accesses than the best previously proposed algorithm for point and region queries on uniformly distributed or mildly skewed point and region data, and approximately the same for highly skewed point and region data. The work of Leutenegger and Lopez was supported in part by Colorado Advance Software Institute grant number TT-97-05. The work of Leutenegger was additionally supported in part by the National Aeronautics and Space Administration under NASA contract NAS1-19480 while visiting the Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA 23681-0001
LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing

Computational lithography provides algorithmic and mathematical support for resolution enhancement in optical lithography, which is critical for semiconductor manufacturing. The time-consuming lithography simulation and mask optimization processes limit the practical application of inverse lithography technology (ILT), a promising solution to the challenges of advanced-node lithography. Although machine learning for ILT has shown promise for reducing the computational burden, this ﬁeld lacks a dataset that can train the models thoroughly and evaluate the performance comprehensively. To boost the development of AI-driven computational lithography, we present the LithoBench dataset, a collection of circuit layout tiles for deep-learning-based lithography simulation and mask optimization. LithoBench consists of more than 120k tiles that are cropped from real circuit designs or synthesized according to topologies of widely adopted ILT testcases. Ground truths are generated by a famous lithography model in academia and an advanced ILT method. We provide a framework to design and evaluate deep neural networks (DNNs) with the data. The framework is used to benchmark state-of-the-art models on lithography simulation and mask optimization. LithoBench is available at https://github.com/shelljane/lithobench .
Characteristics of Water Physiological Integration and its Ecological Significance for Populus euphratica Young Ramets in an Extremely Drought Environment

Water physiological integration plays an important role and has profound effects on the population renewal and expansion of clonal plants. This work discussed the spatial distribution architecture, water sources, water physiological integration, and the ecological significance of Populus euphratica young ramets in an extremely drought environment based on field investigation, stable isotope technology, and eco‐physiological experiments. The spatial distribution architecture of P. euphratica young ramets assumes a guerrilla growth form that is adapted to significant heterogeneity and patches of habitat resources. There is obvious water integration that is characterized by an acropetally flow. The water integration process and daily flow showed the bimodal pattern following the physiological rhythm of parent trees and declined with increasing length of spacers connecting parent trees and their young daughter ramets. The young daughter ramets of P. euphratica can use the deeper soil water in a similar manner to the parent trees and obtain an average daily water acquisition of about 1.09 kg by water integration when the spacer length is about 2–3 m. It is about 5 times as much water as seedlings sucking by themselves. This lets young ramets maintain higher leaf water content and midday leaf water potential of 10.27% and 29.73%, respectively, than seedlings in same habitats. Therefore, P. euphratica young ramets can benefit from a higher survival advantage in extreme drought habitats compared to seedlings. This facilitates the establishment and growth of young ramets in those adverse habitats where P. euphratica seedlings are less likely to survive.
Characterization and Analysis of a Potential Hidden Geothermal Resource in the Jersey Summit Area, North-Central, Nevada

The Great Basin region (GBR) in the western U.S. is known for prolific geothermal activity and has substantial geothermal resource potential due to its favorable tectonic setting. However, most of the geothermal systems in this region are probably hidden, with no surface manifestations such as hot springs or steam vents. These hidden systems can be challenging to discover using traditional exploration methods, prompting new multidisciplinary approaches that are able to increase the ability to detect these untapped resources while decreasing the overall risk associated with geothermal exploration. For this study, we aim to better characterize hidden geothermal systems in the GBR using detailed structural analyses of designated field sites. Here, we focus on the Jersey Summit area, which includes southern Buffalo Valley and northern Jersey Valley in north-central Nevada. This site has been identified as having high potential for a hidden system based on an abundance of Quaternary faults and the presence of a major accommodation zone between the southward terminating, east-dipping Buffalo Valley normal fault zone and northward terminating, west-dipping Jersey Valley normal fault zone. Our objectives are to integrate multiple geological, geophysical, and geochemical datasets to establish the stratigraphic and structural framework of the Jersey Summit area, delineate the geometry and kinematics of the Quaternary fault systems, identify particularly favorable structural settings within the broader accommodation zone, define locations of thermal anomalies, and assign the most favorable targets for future temperature gradient drilling. Detailed geologic mapping incorporating new high-resolution lidar data, structural analysis, and 2-m temperature surveys were employed to identify the most highly prospective areas. These data suggest two particularly promising areas:
Shapeshifting Antibiotics: Bullvalene Linked Vancomycin Dimers are Effective Against Multidrug-Resistant Gram-Positive Bacteria

: The alarming rise in superbugs that are resistant to drugs of last resort, including vancomycin-resistant enterococci and staphylococci, has become a significant global health hazard. Here we report the click chemistry synthesis of an unprecedented class of shapeshifting vancomycin dimers (SVDs) that display potent activity against bacteria that are resistant to the parent drug, including the ESKAPE pathogens, vancomycin-resistant Enterococcus (VRE), methicillin-resistant Staphylococcus aureus (MRSA) as well as vancomycin-resistant S. aureus (VRSA). The shapeshifting modality of the dimers is powered by a click-linked bullvalene core, hence exploiting the dynamic covalent rearrangements of the ﬂuxional carbon cage and creating ligands with the capacity to inhibit bacterial cell wall biosynthesis. The new shapeshifting antibiotics are not disadvantaged by the common mechanism of vancomycin resistance resulting from the alteration of the C-terminal dipeptide with the corresponding D -Ala- D -Lac depsipeptide. Further, evidence suggests that the shapeshifting ligands destabilize the complex formed between the flippase MurJ and lipid II, inferring the potential for a new mode of action for polyvalent glycopeptides. The SVDs show little propensity for acquired resistance by enterococci, suggesting that this new class of shapeshifting antibiotic will display durable antimicrobial activity not prone to rapidly acquired clinical resistance.
WIP: A Trust Assessment Method for In-Vehicular Networks using Vehicle Risk Assessment

—Future vehicles will run safety-critical applications that rely on data from entities within and outside the vehicle. Malicious manipulation of this data can lead to safety incidents. In our work, we propose a Trust Assessment Framework (TAF) that allows a component in a vehicle to assess whether it can trust the provided data. Based on a logic framework called Subjective Logic, the TAF determines a trust opinion for all components involved in processing or forwarding a data item. One particular challenge in this approach is the appropriate quantification of trust. To this end, we propose to derive trust opinions for electronic control units (ECUs) in an in-vehicle network based on the security controls implemented in the ECU, such as secure boot. We apply a Threat Analysis and Risk Assessment (TARA) to assess security controls at design time and use run time information to calculate associated trust opinions. The feasibility of the proposed concept is showcased using an in-vehicle application with two different scenarios. Based on the initial results presented in this paper, we see an indication that a trust assessment based on quantifying security controls represents a reasonable approach to provide trust opinions for a TAF.
Exploring Phishing Threats through QR Codes in Naturalistic Settings

—QR codes, designed for convenient access to links, have recently been appropriated as phishing attack vectors. As this type of phishing is relatively and many aspects of the threat in real conditions are unknown, we conducted a study in naturalistic settings ( n =42) to explore how people behave around QR codes that might contain phishing links. We found that 28 (67%) of our participants opened the link embedded in the QR code without inspecting the URL for potential phishing cues. As a pretext, we used a poster that invited people to scan a QR code and contribute to a humanitarian aid. The choice of a pretext was persuasive enough that 22 (52%) of our participants indicated that it was the main reason why they scanned the QR code and accessed the embedded link in the first place. We used three link variants to test if people are able to spot a potential phishing threat associated with the poster’s QR code (every participant scanned only one variant). In the variants where the link appeared legitimate or it was obfuscated by a link shortening service, only two out of 26 participants (8%) abandoned the URL when they saw the preview in the QR code scanner app. In the variant when the link explicitly contained the word “phish” in the domain name, this ratio rose to 7 out of 16 participants (44%). We use our findings to propose usable security interventions in QR code scanner apps intended to warn users about potentially phishing links.
Analog Computer Prototyping for the Future

—This research paper focuses on analog computers and creating a modular low-cost analog computer system in a single board computer form factor. The single-board analog computer will have the capacity to solve second-order differential equations. The capabilities and possibilities of the single board Analog computer will be explored as well as analog computing in general. The paper follows design science research methodology (DSRM) with the goal of creating and evaluating a working artifact. The artifacts’ functionality is evaluated based on a demonstration of its ability to solve Mathieu’s differential equation as well as simulate a spring-mass dampening system. This paper proves that it is possible to create a low-cost analog computer in a modern form factor. The artifact is also placed in a larger contextual setting based on the empirical material provided where its value of it in a digital society is presented. For the world to continue its progression in computational power, but still, limit the already high energy usage, a drastic change is needed. This paper suggests adapting to analog/hybrid technology. To further the progression of analog/hybrid technology it must be made accessible to a wider group of people compared to today. The artifact in this paper offers a solution to this.
RIT Digital Institutional Repository RIT Digital Institutional Repository

Machine learning and deep learning have revolutionized industries. Businesses have seen a boost in revenue through the use of these techniques, including the multibillion-dollar loan and credit industry. This study aims to develop a machine learning model to approve or disapprove loan applications based on customer characteristics, using algorithms such as statistical models, tree-based models, boosting models, and voting/stacking models. An exploratory analysis was conducted to understand customer characteristics, and the decision tree model proved to have the best performance with high precision in loan approval prediction.
Achieving Clarity: Separating Sediments from Argan Oil

The objective of our project was to develop a faster and more efficient method for separating argan oil. Our sponsor expressed the need to accelerate the current gravitational process, which takes up to three weeks. Through experimentation, we found that a centrifuge was an effective approach and reduced the separation time to one hour. These findings have significant implications for our sponsor, offering a more efficient method for production and the potential to change how argan oil is processed.
AVM ON : Securing Autonomous Vehicles by Learning Control Invariants and Residual Prediction

—Autonomous systems are vulnerable to physical attacks that manipulate their sensors through spoofing or other adversarial inputs or interference. If the sensors’ values are incorrect, an autonomous system can be directed to malfunction or even controlled to perform an adversary-chosen action, making this a critical threat to the success of these systems. To counter these attacks, a number of prior defenses were proposed that compare the collected sensor values to those predicted by a physics based model of the vehicle dynamics; these solutions can be limited by the accuracy of this prediction which can leave room for an attacker to operate without being detected. We propose AVM ON , which contributes a new detector that substantially improves detection accuracy, using the following ideas: (1) Training and specialization of an estimation filter configuration to the vehicle and environment dynamics; (2) Efficiently overcoming errors due to non-linearities, and capturing some effects outside the physics model, using a residual machine learning estimator; and (3) A change detection algorithm for keeping track of the behavior of the sensors to enable more accurate filtering of transients. These ideas together enable both efficient and high accuracy estimation of the physical state of the vehicle, which substantially shrinks the attacker’s opportunity to manipulate the sensor data without detection. We show that AVM ON can detect a wide range of attacks, with low overhead compatible with real-time implementations. We demonstrate AVM ON for both ground vehicles (using an RC Car testbed) and for aerial drones (using hardware in the loop simulator), as well as in simulations.
Sympathetic and parasympathetic central autonomic networks

Abstract The central-autonomic network (CAN) comprises brain regions that are functionally linked to the activity of peripheral autonomic nerves. While parasympathetic CAN (i.e., the CAN projecting onto parasympathetic branches) has recently been investigated and is known to be involved in neurological and neuropsychiatric disorders, sympathetic CAN (i.e., the CAN projecting onto sympathetic nerves) has not been fully characterized. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project in conjunction with heartbeat dynamics and its orthonormal autoregressive descriptors as a proxy for sympathetic activity estimation, namely, the sympathetic activity index (SAI), we uncover brain regions belonging to the sympathetic CAN at rest. We uncover a widespread CAN comprising both cortical (in all lobes) and subcortical areas, including the cerebellum and brainstem, which is functionally linked to sympathetic activity and overlaps with brain regions driving parasympathetic activity. These findings may constitute fundamental knowledge linking brain and bodily dynamics, including the link between neurological and psychiatric disorders and autonomic dysfunctions.
Disability Discrimination in the Rental Housing Market – A Field Experiment on Blind Tenants , in Italy *

In this study, I show that with the appropriate exp rimental strategy, a correspondence test can be adapted to investigate disability discr imination in the rental housing market. I focus on discrimination against blind tenants assisted by gu ide dogs in Italy and obtain very robust results. The utilization of three fictitious household tenan ts (that is, a married couple, a married couple with a blind wife who owns a guide dog, and a marri ed couple where the wife is normal sighted and owns a pet dog) allows me to investigate whethe r discrimination is due to the blindness or to the guide dog. I find that apartment owners discrim inate blind tenants because of the presence of the guide dog alone. According to the Italian law, this is indirect discrimination, which in the US corresponds to the refusal to provide reasonable ac commodation.
Large-scale RNAi Screen of G Protein-coupled Receptors Involved Large-scale RNAi Screen of G Protein-coupled Receptors Involved in Larval Growth, Molting and Metamorphosis in the Red Flour in Larval Growth, Molting and Metamorphosis in the Red Flour Beetle Beetle

Background: The G protein-coupled receptors (GPCRs) belong to the largest superfamily of integral cell membrane proteins and play crucial roles in physiological processes including behavior, development and reproduction. Because of their broad and diverse roles in cellular signaling, GPCRs are the therapeutic targets for many prescription drugs. However, there is no commercial pesticide targeting insect GPCRs. In this study, we employed functional genomics methods and used the red flour beetle, Tribolium castaneum , as a model system to study the physiological roles of GPCRs during the larval growth, molting and metamorphosis. Results: A total of 111 non-sensory GPCRs were identified in the T. castaneum genome. Thirty-nine of them were not reported previously. Large-scale RNA interference (RNAi) screen was used to study the function of all these GPCRs during immature stages. Double-stranded RNA (dsRNA)-mediated knockdown in the expression of genes coding for eight GPCRs caused severe developmental arrest and ecdysis failure (with more than 90% mortality after dsRNA injection). These GPCRs include dopamine-2 like receptor (TC007490/D2R) and latrophilin receptor (TC001872/Cirl). The majority of larvae injected with TC007490/D2R dsRNA died during larval stage prior to entering pupal stage, suggesting that this GPCR is essential for larval growth and development. Conclusions: The results from our study revealed the physiological roles of some GPCRs in T. castaneum . These findings could help in development of novel pesticides targeting these GPCRs.