Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors influencing appearance, disappearance, and variability of abundance of the sea nettle Chrysaora quinquecirrha in Chesapeake Bay

University of Maryland Teaching Assistantships Marine, Estuarine, and Environmental Sciences Program Fisheries Oceanography (MEES 698F) Spring 2012 Quantitative Methods in Env. Sciences (MEES 607) Fall 2011 Physics of Estuarine and Marine Env. (MEES 661) Spring 2011 Biological Oceanography (MEES 621) Fall 2010 REU Liaison Summer 2010 Responsibilities for this position included arranging guest speakers and providing academic and logistical support to seven undergraduate interns in the Research Experience for Undergraduates Program.
A Cluster Analysis of Research in Information Technology for Global Development: Where to from here?

While research in Information Systems recognizes the importance of Information Technology in a global context, it continues to focus on a narrow view of IT adoption. In recognizing that the majority of innovations in IT are taking place in developing countries, it is important to find out what research is being undertaken to understand the effects of the different technologies on development outcomes. This paper conducts a cluster analysis of 214 papers from the SigGlobDev Workshop, the Information Technology for Development Journal and other Information Systems Journals and conferences which have published papers in Information Systems and Global Development. This analysis throws light on the ways in which research is being undertaken in this area. The contribution of this paper lies in identifying the key areas in which Information Systems research is addressing the ways that development outcomes are or are not being addressed, and it highlights areas that still need to be investigated.
Modeling and Adjusting Global Ocean Tides Using SEASAT Altimeter Data.

Abstract : In the recent past, SEASAT altimeter data together with global geoidal parameters and sets of state vectors parameters have been adjusted at AFGL through the short-arc algorithm using NSWC precise ephemeris. The excellent quality of the data system, including the altimeter measurements, the ephemeris, the input geoidal parameters, etc., has been confirmed by the finding that the empirical variance for geoid undulations is significantly lower than the theoretical variance. The altimeter residuals from this adjustment have served in regional modeling of short-wavelength geoidal features, as well as in studying geophysical phenomena such as ocean bottom topography. Since the geoid has been assumed to coincide with the ocean surface sensored by the altimeter, the sea-surface effects have been ignored in this adjustment. Due to increasing geophysical interest in a realistic representation of the open ocean tide, the latest development of the short-arc satellite altimetry model allows for the inclusion of the most important tidal constituents. In particular, an adjustment algorithm has been designed in which four long-period constituents, three diurnal constituents and four semidiurnal constituents may be subject to adjustment within the overall adjustment of SEASAT altimetric observations.
Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics

Dissecting Plasmodium drug resistance Malaria is a deadly disease with no effective vaccine. Physicians thus depend on antimalarial drugs to save lives, but such compounds are often rendered ineffective when parasites evolve resistance. Cowell et al. systematically studied patterns of Plasmodium falciparum genome evolution by analyzing the sequences of clones that were resistant to diverse antimalarial compounds across the P. falciparum life cycle (see the Perspective by Carlton). The findings identify hitherto unrecognized drug targets and drug-resistance genes, as well as additional alleles in known drug-resistance genes. Science, this issue p. 191; see also p. 159 Genome sequencing elucidates potential drug resistance in the malaria parasite and identifies antimalarial targets. Chemogenetic characterization through in vitro evolution combined with whole-genome analysis can identify antimalarial drug targets and drug-resistance genes. We performed a genome analysis of 262 Plasmodium falciparum parasites resistant to 37 diverse compounds. We found 159 gene amplifications and 148 nonsynonymous changes in 83 genes associated with drug-resistance acquisition, where gene amplifications contributed to one-third of resistance acquisition events. Beyond confirming previously identified multidrug-resistance mechanisms, we discovered hitherto unrecognized drug target–inhibitor pairs, including thymidylate synthase and a benzoquinazolinone, farnesyltransferase and a pyrimidinedione, and a dipeptidylpeptidase and an arylurea. This exploration of the P. falciparum resistome and druggable genome will likely guide drug discovery and structural biology efforts, while also advancing our understanding of resistance mechanisms available to the malaria parasite.
Average or Outlier? Introductory Statistics Adjunct Instructors’ Beliefs, Practices, and Experiences

In recent years, the adjunct faculty phenomenon has grown steadily. This research focused on adjunct instructors teaching introductory statistics courses. The purpose of the study was to give a voice to adjunct instructors by allowing them to describe their experiences teaching statistics. We conducted a qualitative study with 15 adjunct instructors of introductory statistics through semi-structured interviews. The participants came from several fields: psychology, nursing, and business, among others. Thematic analysis was used to find themes of statistical anxiety, use of technology in the classroom, lack of curriculum flexibility, and connection to the host institution. Our findings can inform institutions and interdisciplinary departments hosting introductory statistics courses. Similarly, educational statisticians can gain knowledge on the practices and barriers of adjunct instructors.
3D hierarchical flower-like TiO2 nanostructure: morphology control and its photocatalytic property

3D hierarchical flower-like TiO2 nanostructures were synthesized via a facile solvothermal method combining a calcination process. A series of electron microscopy characterization results suggest that the growth of 3D hierarchical flower-like TiO2 nanostructures is governed by a nucleation and nuclei growth–dissolution–recrystallization growth mechanism from time-dependent morphology evolution. The influence of the experimental parameters such as reaction species, temperature, and time on the morphology growth was systematically studied. Importantly, the prepared 3D hierarchical flower-like TiO2 nanostructures showed enhanced photocatalytic activity compared with Degussa P25 for the degradation of phenol under UV light irradiation, which is attributed to its special hierarchical porous structure, good permeability, high light-harvesting capacity and more surface active sites. Electron spin resonance (ESR) detection of active oxygen species (hydroxyl radicals) and the total organic carbon (TOC) changes are also investigated to evaluate the degradation efficacy.
Time-resolved laser speckle contrast imaging (TR-LSCI) of cerebral blood flow

To address many of the deficiencies in optical neuroimaging technologies such as poor spatial resolution, time-consuming reconstruction, low penetration depth, and contact-based measurement, a novel, noncontact, time-resolved laser speckle contrast imaging (TR-LSCI) technique has been developed for continuous, fast, and high-resolution 2D mapping of cerebral blood flow (CBF) at different depths of the head. TR-LSCI illuminates the head with picosecond-pulsed, coherent, widefield near-infrared light and synchronizes a newly developed, high-resolution, gated single-photon avalanche diode camera (SwissSPAD2) to capture CBF maps at different depths. By selectively collecting diffuse photons with longer pathlengths through the head, TR-LSCI reduces partial volume artifacts from the overlying tissues, thus improving the accuracy of CBF measurement in the deep brain. CBF map reconstruction was dramatically expedited by incorporating highly parallelized computation. The performance of TR-LSCI was evaluated using head-simulating phantoms with known properties and in-vivo rodents with varied hemodynamic challenges to the brain. Results from these pilot studies demonstrated that TR-LSCI enabled mapping CBF variations at different depths with a sampling rate of up to 1 Hz and spatial resolutions ranging from tens of micrometers on the head surface to 1-2 millimeters in the deep brain. With additional improvements and validation in larger populations against established methods, we anticipate offering a noncontact, fast, high-resolution, portable, and affordable brain imager for fundamental neuroscience research in animals and for translational studies in humans.
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving

We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of domain shift of the visual features across clients, in this work, we focus on the distribution skewness of the labels. We propose six new federated scenarios to investigate how label skewness affects the performance of segmentation models and compare it with the effect of domain shift. Finally, we study the impact of using the domain information during testing. Official website: https://feddrive.github. io
Sealing Performance of Bionic Striped Mud Pump Piston

The mud pump piston is a key part for providing mud circulation, but its sealing performance often fails under complex working conditions, which shorten its service life. Inspired by the ring segment structure of earthworms, the bionic striped structure on surfaces of the mud pump piston (BW-160) was designed and machined, and the sealing performances of the bionic striped piston and the standard piston were tested on a sealing performance testing bench. It was found the bionic striped structure efficiently enhanced the sealing performance of the mud pump piston, while the stripe depth and the angle between the stripes and lateral of the piston both significantly affected the sealing performance. The structure with a stripe depth of 2 mm and angle of 90° showed the best sealing performance, which was 90.79% higher than the standard piston. The sealing mechanism showed the striped structure increased the breadth and area of contact sealing between the piston and the cylinder liner. Meanwhile, the striped structure significantly intercepted the early leaked liquid and led to the refluxing rotation of the leaked liquid at the striped structure, reducing the leakage rate.
Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition

This paper studies the problem of learning the large-scale Gaussian graphical models that are multivariate totally positive of order two ($\text{MTP}_2$). By introducing the concept of bridge, which commonly exists in large-scale sparse graphs, we show that the entire problem can be equivalently optimized through (1) several smaller-scaled sub-problems induced by a \emph{bridge-block decomposition} on the thresholded sample covariance graph and (2) a set of explicit solutions on entries corresponding to bridges. From practical aspect, this simple and provable discipline can be applied to break down a large problem into small tractable ones, leading to enormous reduction on the computational complexity and substantial improvements for all existing algorithms. The synthetic and real-world experiments demonstrate that our proposed method presents a significant speed-up compared to the state-of-the-art benchmarks.
Human detection in images and videos

This dissertation addresses the problems of detecting objects in images and videos with a particular emphasis on human detection. Object detection and recognition is a critical task for many real-world applications. Recent studies have resulted in several general approaches that combine salient local image features and effective learning techniques. We developed near real-time systems for automatically finding people under two practical scenarios: (I) Under the assumption that humans can be found in isolation (e.g., in personal photos), our solution integrates a cascade-of-rejector approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. (II) For the situations where humans move in crowds (e.g., the exit of a railway station), we present a system which first detects interest points in each frame, tracks them over time and clusters them. The key challenge is to cluster the feature points that are in a high dimensional, non-Euclidean space and are of variable length, depending on the number of frames they appeared in so far. In both approaches, we explore the effectiveness of using local image features for representing an object, and then employing machine learning techniques for detecting the target object in images and videos. Extensive experiments on real data demonstrate that the proposed approaches achieve high accuracy in practice.
AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming

With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging models of computation, such as approximate and stochastic computing, that leverage the inherent error-resilience of such algorithms are being actively explored for implementing ML inference on resource-constrained systems. Approximate computing (AxC) aims to provide disproportionate gains in the power, performance, and area (PPA) of an application by allowing some level of reduction in its behavioral accuracy (BEHAV). Using approximate operators (AxOs) for computer arithmetic forms one of the more prevalent methods of implementing AxC. AxOs provide the additional scope for finer granularity of optimization, compared to only precision scaling of computer arithmetic. To this end, the design of platform-specific and cost-efficient approximate operators forms an important research goal. Recently, multiple works have reported the use of AI/ML-based approaches for synthesizing novel FPGA-based AxOs. However, most of such works limit the use of AI/ML to designing ML-based surrogate functions that are used during iterative optimization processes. To this end, we propose a novel data analysis-driven mathematical programming-based approach to synthesizing approximate operators for FPGAs. Specifically, we formulate mixed integer quadratically constrained programs based on the results of correlation analysis of the characterization data and use the solutions to enable a more directed search approach for evolutionary optimization algorithms. Compared to traditional evolutionary algorithms-based optimization, we report up to 21% improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the design of signed 8-bit multipliers. Further, we report up to 27% better hypervolume than other state-of-the-art approaches to DSE for FPGA-based application-specific AxOs.
Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials

Personalized adaptive interventions offer the opportunity to increase patient benefits, however, there are challenges in their planning and implementation. Once implemented, it is an important question whether personalized adaptive interventions are indeed clinically more effective compared to a fixed gold standard intervention. In this paper, we present an innovative N-of-1 trial study design testing whether implementing a personalized intervention by an online reinforcement learning agent is feasible and effective. Throughout, we use a new study on physical exercise recommendations to reduce pain in endometriosis for illustration. We describe the design of a contextual bandit recommendation agent and evaluate the agent in simulation studies. The results show that, first, implementing a personalized intervention by an online reinforcement learning agent is feasible. Second, such adaptive interventions have the potential to improve patients' benefits even if only few observations are available. As one challenge, they add complexity to the design and implementation process. In order to quantify the expected benefit, data from previous interventional studies is required. We expect our approach to be transferable to other interventions and clinical interventions.
Terrestrial Locomotion of PogoX: From Hardware Design to Energy Shaping and Step-to-step Dynamics Based Control

We present a novel controller design on a robotic locomotor that combines an aerial vehicle with a spring-loaded leg. The main motivation is to enable the terrestrial locomotion capability on aerial vehicles so that they can carry heavy loads: heavy enough that flying is no longer possible, e.g., when the thrust-to-weight ratio (TWR) is small. The robot is designed with a pogo-stick leg and a quadrotor, and thus it is named as PogoX. We show that with a simple and lightweight spring-loaded leg, the robot is capable of hopping with TWR $<1$. The control of hopping is realized via two components: a vertical height control via control Lyapunov function-based energy shaping, and a step-to-step (S2S) dynamics based horizontal velocity control that is inspired by the hopping of the Spring-Loaded Inverted Pendulum (SLIP). The controller is successfully realized on the physical robot, showing dynamic terrestrial locomotion of PogoX which can hop at variable heights and different horizontal velocities with robustness to ground height variations and external pushes.
Early-Exit with Class Exclusion for Efficient Inference of Neural Networks

Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a large number of multiply-accumulate (MAC) operations are required to be performed, posing critical challenges in applying them in resource-constrained platforms, e.g., edge devices. To address this challenge, in this paper, we propose a class-based early-exit for dynamic inference. Instead of pushing DNNs to make a dynamic decision at intermediate layers, we take advantage of the learned features in these layers to exclude as many irrelevant classes as possible, so that later layers only have to determine the target class among the remaining classes. When only one class remains at a layer, this class is the corresponding classification result. Experimental results demonstrate the computational cost of DNNs in inference can be reduced significantly with the proposed early-exit technique. The codes can be found at https://github.com/HWAI-TUDa/EarlyClassExclusion.
D-Separation for Causal Self-Explanation

Rationalization is a self-explaining framework for NLP models. Conventional work typically uses the maximum mutual information (MMI) criterion to find the rationale that is most indicative of the target label. However, this criterion can be influenced by spurious features that correlate with the causal rationale or the target label. Instead of attempting to rectify the issues of the MMI criterion, we propose a novel criterion to uncover the causal rationale, termed the Minimum Conditional Dependence (MCD) criterion, which is grounded on our finding that the non-causal features and the target label are \emph{d-separated} by the causal rationale. By minimizing the dependence between the unselected parts of the input and the target label conditioned on the selected rationale candidate, all the causes of the label are compelled to be selected. In this study, we employ a simple and practical measure of dependence, specifically the KL-divergence, to validate our proposed MCD criterion. Empirically, we demonstrate that MCD improves the F1 score by up to $13.7\%$ compared to previous state-of-the-art MMI-based methods. Our code is available at: \url{https://github.com/jugechengzi/Rationalization-MCD}.
Point torque representations of ciliary flows

Ciliary flows are generated by a vast array of eukaryotic organisms, from unicellular algae to mammals, and occur in a range of different geometrical configurations. We employ a point torque -- or `rotlet' -- model to capture the time-averaged ciliary flow above a planar rigid wall. We demonstrate the advantages (i.e. accuracy and computational efficiency) of using this, arguably simpler approach compared to other singularity-based models in Stokes flows. Then, in order to model ciliary flows in confined spaces, we extend the point torque solution to a bounded domain between two plane parallel no-slip walls. The flow field is resolved using the method of images and Fourier transforms, and we analyze the role of confinement by comparing the resultant fluid velocity to that of a rotlet near a single wall. Our results suggest that the flow field of a single cilium is not changed significantly by the confinement, even when the distance between the walls is commensurate with the cilium's length.
Design and Evaluation of Motion Planners for Quadrotors

Motion planning techniques for quadrotors have advanced significantly over the past decade. Most successful planners have two stages: a front-end that determines a path that incorporates geometric (or kinematic or input) constraints and specifies the homotopy class of the trajectory, and a back-end that optimizes this path to respect dynamics and input constraints. While there are many different choices for each stage, the eventual performance depends critically not only on these choices, but also on the environment. Given a new environment, it is difficult to decide a priori how one should design a motion planner. In this work, we develop (i) a procedure to construct parametrized environments, (ii) metrics that characterize the difficulty of motion planning in these environments, and (iii) an open-source software stack that can be used to combine a wide variety of two-stage planners seamlessly. We perform experiments in simulations and a real platform. We find, somewhat conveniently, that geometric front-ends are sufficient for environments with varying complexities if combined with dynamics-aware backends. The metrics we designed faithfully capture the planning difficulty in a given environment. All code is available at https://github.com/KumarRobotics/kr_mp_design
Generalized impedance boundary conditions with vanishing or sign-changing impedance

We consider a Laplace type problem with a generalized impedance boundary condition of the form $\partial_\nu u=-\partial_x(g\partial_xu)$ on a flat part $\Gamma$ of the boundary. Here $\nu$ is the outward unit normal vector to $\partial\Omega$, $g$ is the impedance function and $x$ is the coordinate along $\Gamma$. Such problems appear for example in the modelling of small perturbations of the boundary. In the literature, the cases $g=1$ or $g=-1$ have been investigated. In this work, we address situations where $\Gamma$ contains the origin and $g(x)=\mathbb{1}_{x>0}(x)x^\alpha$ or $g(x)=-\mbox{sign}(x)|x|^\alpha$ with $\alpha\ge0$. In other words, we study cases where $g$ vanishes at the origin and changes its sign. The main message is that the well-posedness in the Fredholm sense of the corresponding problems depends on the value of $\alpha$. For $\alpha\in[0,1)$, we show that the associated operators are Fredholm of index zero while it is not the case when $\alpha=1$. The proof of the first results is based on the reformulation as 1D problems combined with the derivation of compact embedding results for the functional spaces involved in the analysis. The proof of the second results relies on the computation of singularities and the construction of Weyl's sequences. We also discuss the equivalence between the strong and weak formulations, which is not straightforward. Finally, we provide simple numerical experiments which seem to corroborate the theorems.