Miracle-dz/UniFAR-FTUs huggingface dataset:
A spectroscopic study of 14 structures behind Holm15A: Detecting a galaxy group candidate at z=0.58.

Holm15A hosts one of the most massive back holes ever known. Hence, it is important to characterize any structure within its core to avoid any wrong association with its central black hole and, therefore, bias any future study. In this work, we present the first identification and characterization of 14 structures hidden behind the surface brightness of Holm15A. We model and subtract the spectral contribution of Holm15A to obtain the spectral information of these structures. We spectroscopically confirm that the 14 objects found are not associated with Holm15A. Ten objects have a well-defined galaxy spectrum from which we implement a fossil record analysis to reconstruct their past evolution. Nine objects are candidates members to be part of a compact galaxy group at redshift 0.5814. We find past mutual interaction among the group candidates that support the scenario of mutual crossings. Furthermore, the fossil reconstruction of the group candidates brings evidence that at least three different merger trees could assemble the galaxy group. We characterize the properties of the galaxy group from which we estimate a lower limit of the scale and mass of this group. We obtain a scale of >146 ±3 kpc with a dispersion velocity of 622±300 km s−1. These estimations consider the lensing effects of the gravitational potential of Holm15A. The other five objects were studied individually. We use public archive data of integral field spectroscopic observations from the Multi-Unit Spectroscopic Explorer instrument.
Unsteady aerodynamic modeling of Aerobat using lifting line theory and Wagner's function

Flying animals possess highly complex physical characteristics and are capable of performing agile maneuvers using their wings. The flapping wings generate complex wake structures that influence the aerodynamic forces, which can be difficult to model. While it is possible to model these forces using fluidstructure interaction, it is very computationally expensive and difficult to formulate. In this paper, we follow a simpler approach by deriving the aerodynamic forces using a relatively small number of states and presenting them in a simple state-space form. The formulation utilizes Prandtl's lifting line theory and Wagner's function to determine the unsteady aerodynamic forces acting on the wing in a simulation, which then are compared to experimental data of the bat-inspired robot called the Aerobat. The simulated trailingedge vortex shedding can be evaluated from this model, which then can be analyzed for a wake-based gait design approach to improve the aerodynamic performance of the robot.
Waveform Design for Joint Sensing and Communications in Millimeter-Wave and Low Terahertz Bands

The convergence of radar sensing and communication applications in the millimeter-wave (mmWave) and low terahertz (THz) bands has been envisioned as a promising technology, since it incorporates high-rate data transmission of hundreds of gigabits per second (Gbps) and mm-level radar sensing in a spectrum- and cost-efficient manner, by sharing both the frequency and hardware resources. However, the joint radar sensing and communication (JRC) system faces considerable challenges in the mmWave and low-THz scale, due to the peculiarities of the propagation channel and radio-frequency (RF) front ends. To this end, the waveform design for the JRC systems in mmWave and low-THz bands with ultra-broad bandwidth is investigated in this paper. Firstly, by considering the JRC design based on the co- existence concept, where both functions operate in a time-domain duplex (TDD) manner, a novel multi-subband quasi-perfect (MS-QP) sequence, composed of multiple perfect subsequences on different subbands, is proposed for target sensing, which achieves accurate target ranging and velocity estimation, whilst only requiring cost-efficient low-rate analog-to-digital converters (A/Ds) for sequence detection. Furthermore, the root index of each perfect subsequence is designed to eliminate the influence of strong Doppler shift on radar sensing. Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission. Unlike the co- existence-based JRC system in TDD manner, the proposed DE-MS-QP waveform enables simultaneous interference-free sensing and communication, whilst inheriting all the merits from MS-QP sequences. Numerical results validate the superiority of the proposed waveforms regarding the communication and sensing performances, hardware cost as well as flexibility of the resource allocation between the dual functions.
Inapproximability of a Pair of Forms Defining a Partial Boolean Function

We consider the problem of jointly minimizing forms of two Boolean functions f, g : { 0 , 1 } J → { 0 , 1 } such that f + g ≤ 1 and so as to separate disjoint sets A ∪· B ⊆ { 0 , 1 } J such that f ( A ) = { 1 } and g ( B ) = { 1 } . We hypothesize that this problem is easier to solve or approximate than the well-understood problem of minimizing the form of one Boolean function h : { 0 , 1 } J → { 0 , 1 } such that h ( A ) = { 1 } and h ( B ) = { 0 } . For a large class of forms, including binary decision trees and ordered binary decision diagrams, we refute this hypothesis. For disjunctive normal forms, we show that the problem is at least as hard as min-set-cover . For all these forms, we establish that no o (ln( | A | + | B | − 1))-approximation algorithm exists unless p=np . Abstract We consider the problem of jointly minimizing forms of two Boolean functions f, g : { 0 , 1 } J → { 0 , 1 } such that f + g ≤ 1 and so as to separate disjoint sets A ∪· B ⊆ { 0 , 1 } J such that f ( A ) = { 1 } and g ( B ) = { 1 } . We hypothesize that this problem is easier to solve or approximate than the well-understood problem of minimizing the form of one Boolean function h : { 0 , 1 } J → { 0 , 1 } such that h ( A ) = { 1 } and h ( B ) = { 0 } . For a large class of forms, including binary decision trees and ordered binary decision diagrams, we refute this hypothesis. For disjunctive normal forms, we show that the
Running head: Role of translesion polymerase ζ in DNA repair

crosslink ABSTRACT REV3, the catalytic subunit of the translesion synthesis polymerase POLζ, is 59 known to play an important role in the repair of DNA damage induced by 60 crosslinking and methylating agents. Here, we demonstrate that in Arabidopsis 61 thaliana , the basic polymerase activity of REV3 is essential for resistance 62 protection against these different types of damaging agents. Interestingly, its 63 processivity is mainly required for resistance to inter- and intrastrand 64 crosslinking agents but not alkylating agents. To better define the role of REV3 65 in relation to other key factors involved in DNA repair, we performed epistasis 66 analysis and show that REV3-mediated resistance to DNA damaging agents is 67 independent of the replication damage checkpoint kinase ATR. REV3 68 cooperates with the endonuclease MUS81 in response to interstrand crosslinks 69 and alkylated bases, whereas it acts independently of the DNA helicase 70 RECQ4A. Taken together, our data show that four DNA intrastrand crosslink 71 subpathways exist in Arabidopsis , defined by RECQ4A, MUS81, REV3 and the 72 ATPase RAD5A. 73
Smoothing Noisy Data with Tapered Coiflets Series

This paper is concerned with an orthogonal wavelet series estimator of an unknown smooth regression function observed with noise on a bounded interval. A penalized least-squares approach is adopted and our method uses the specific asymptotic interpolating properties of the wavelet approximation generated by a particular wavelet basis, Daubechie's coiflets. A simple procedure is described to estimate the smoothing parameter of the penalizing functional and conditions are given for the estimator to attain optimal convergence rates in the integrated mean square sense as the sample size increases to infinity. The results are illustrated with simulated and real examples and a comparison with other non-parametric smoothers is made. where f(v) indicates the vth derivative off The asymptotic properties of smoothing splines have been studied by a number of authors, including Wahba (1975, 1990), Rice & Rosenblatt (1981), Rice (1984), Speckman (1985), Nussbaum (1985) and Cox (1988), to cite only a few. The continuous analogue of spline smoothing is called Tikhonov regularization. The estimates of g to be considered in this paper are obtained by such a method of regularization and are based on wavelet decompositions on the interval. Our purpose is to show that wavelets can be used within the framework of regularization methods, thus augmenting rather than supplanting the many popular univariate curving methods. In regularization, a target function g is approximated by a smoother function f To be precise, we must define what we mean by "approximate" and "smoother". To this end, as in DeVore & Lucier (1992), we introduce the error function space g with norm || || and the
Paper Title (use style: paper title)

Methods for adaptive beamforming have obtained positive results improving the quality of medical ultrasound images compared to the non-adaptive technique Delay and Sum (DAS). Generalized Sidelobe Canceler (GSC) can considerably improve the lateral resolution of medical ultrasound images, but the contrast of the image is not satisfactory. The Eigenspace-Based Minimum Variance beamformer (EBMV) is an adaptive beamformer that can suppress a large part of the noise and provide images with higher contrast and resolution in comparison with DAS and Minimum Variance beamformer (MVB). In this work, it was applied Eigenspace-Based Generalized Sidelobe Canceller Beamformer to plane wave ultrasound images. All the simulated echo data was obtained from Plane Wave Challenge in Medical Ultrasound Imaging (PICMUS) website. The data were acquired using a Verasonics Vantage 256 equipment using a L11-4v 128 elements transducer. The results obtained were compared with DAS and MVGSC and have shown improvement of lateral resolution. Keywords— generalized sidelobe canceller; eigenspace-based beamforming; resolution; plane wave.
DARPA Technical Paper: Team Caltech

Team Caltech consists of over 50 undergraduates who have worked to conceive, design, build and optimize Alice, our entry in the 2005 DARPA Grand Challenge. Alice utilizes a highly networked control system architecture to provide high performance, autonomous driving in unknown environments. Innovations include a vehicle designed for testing and racing in harsh environments, a highly sensory-driven approach to fuse sensor data into speed maps used by real-time trajectory optimization algorithms, health and contingency management algorithms to manage failures at the component and system level, and a software logging and display environment that enables rapid assessment of performance during testing. ∗Address all correspondence to Richard M. Murray, murray@cds.caltech. edu
The effects of damage accumulation in optimizing a piezoelectric energy harvester configuration

Bimorph piezoelectric elements are often used to harvest energy for low-power structural health monitoring systems. When these piezoelectric elements are deployed for extended periods of time and operate under near-resonant conditions, the resulting high amplitude cycling can lead to degradation of the piezoelectric element, resulting in a shift in the design fundamental frequency. For scenarios in which the piezoelectric harvester is subject to slowly-varying time-dependent frequency inputs, the natural frequency shift due to degradation may cause the piezoelectric harvester to detune from resonance, subsequently affecting the harvester’s power output. The current study seeks to understand how the accumulation of damage shifts the optimal tip mass and resistive load in a bimorph piezoelectric energy harvester. A cantilever piezoelectric element is modeled utilizing coupled electromechanical equations in a distributed system. The piezoelectric is subject to ground accelerations; the resulting power output is recorded for a range of tip masses and resistive loads. A rainflow analysis is then performed to calculate the piezoelectric element’s tip displacement amplitude and the corresponding cycle count. A damage accumulation model based on a weighted form of Miner’s rule is then used to degrade the harvester’s flexural rigidity, piezoelectric capacitance, and piezoelectric strain constant. The piezoelectric is again loaded and the process repeated. The resulting power output contours reveal how the optimal realization of tip mass and resistive load changes as damage accumulates in the piezoelectric element. Apparent trends in the power output contours are explained. Approved for publication, LA-UR-18-20075.
Space-Time Attention with Shifted Non-Local Search

Efficiently computing attention maps for videos is challenging due to the motion of objects between frames. While a standard non-local search is high-quality for a window surrounding each query point, the window's small size cannot accommodate motion. Methods for long-range motion use an auxiliary network to predict the most similar key coordinates as offsets from each query location. However, accurately predicting this flow field of offsets remains challenging, even for large-scale networks. Small spatial inaccuracies significantly impact the attention module's quality. This paper proposes a search strategy that combines the quality of a non-local search with the range of predicted offsets. The method, named Shifted Non-Local Search, executes a small grid search surrounding the predicted offsets to correct small spatial errors. Our method's in-place computation consumes 10 times less memory and is over 3 times faster than previous work. Experimentally, correcting the small spatial errors improves the video frame alignment quality by over 3 dB PSNR. Our search upgrades existing space-time attention modules, which improves video denoising results by 0.30 dB PSNR for a 7.5% increase in overall runtime. We integrate our space-time attention module into a UNet-like architecture to achieve state-of-the-art results on video denoising.
A MAC PROTOCOL FOR ENERGY EFFICIENCY AND FAST EMERGENCY MEDICAL JUDGMENT IN WIRELESS BODY SENSOR NETWORKS

Emergency data collected from sensor nodes widely distributed in wireless body sensor networks (WBSNs) are delivered to medical staff as quickly as possible, so patients’ lives can be saved through appropriate actions and treatments. However, relevant data and vital data may be required for appropriate actions by the medical staff. Therefore, all these data must be properly delivered to the medical staff within the set time. In this paper, we propose a MAC protocol with a reservation function and an operation frame extension function to extend the overall network lifetime by reducing the energy consumption of given sensor nodes and quickly deliver information to medical systems in case of emergency. This MAC protocol makes it possible to achieve fast transmission of related data by utilizing the related-priority slots. As a result of the experiment, the transmission delay was reduced by about 12.5%, and the lifetime was increased by approximately 19% over the existing MAC protocol. It also can be seen that the proposed MAC protocol works well in an environment where emergency events often occur.
Examining Students’ Over-time Behavioral, Cognitive, Emotional, and Agentic Engagement in Gamified Preclass Learning

Pre-class learning is crucial to the overall instructional success of flipped classrooms. However, researchers have pointed out that students show low pre-class learning engagement in flipped learning. At the same time, gamification, a rising instructional approach, has been increasingly used in the educational domain. By using mixed methods, this study examines students’ over-time behavioral, cognitive, emotional, and agentic engagement in the gamified pre-class learning section of a 15-week flipped English classroom in a Chinese university. Quantitative results of this study reveal that students’ behavioral, cognitive, emotional, and agentic engagement did not show statistical differences over time. While, the qualitative findings explain that learning engagement could be balanced by the positive influences of gamification design and some negative factors, such as the novelty effect. This study is expected to provide academic references for teachers and researchers about applying gamification design to flipped classrooms.
Deep Ensemble Learning for Human Activity Recognition Using Wearable Sensors via Filter Activation

During the past decade, human activity recognition (HAR) using wearable sensors has become a new research hot spot due to its extensive use in various application domains such as healthcare, fitness, smart homes, and eldercare. Deep neural networks, especially convolutional neural networks (CNNs), have gained a lot of attention in HAR scenario. Despite exceptional performance, CNNs with heavy overhead is not the best option for HAR task due to the limitation of computing resource on embedded devices. As far as we know, there are many invalid filters in CNN that contribute very little to output. Simply pruning these invalid filters could effectively accelerate CNNs, but it inevitably hurts performance. In this article, we first propose a novel CNN for HAR that uses filter activation. In comparison with filter pruning that is motivated for efficient consideration, filter activation aims to activate these invalid filters from an accuracy boosting perspective. We perform extensive experiments on several public HAR datasets, namely, UCI-HAR (UCI), OPPORTUNITY (OPPO), UniMiB-SHAR (Uni), PAMAP2 (PAM2), WISDM (WIS), and USC-HAD (USC), which show the superiority of the proposed method against existing state-of-the-art (SOTA) approaches. Ablation studies are conducted to analyze its internal mechanism. Finally, the inference speed and power consumption are evaluated on an embedded Raspberry Pi Model 3 B plus platform.
Gamification of Learning as a Supplementary Learning Support for Mapua IT Students

Gamification is a method to increase learners' engagement by incorporating game design elements into contexts that are not game-related. It has helped motivate students to learn through a game in an educational learning environment. Due to the current switch to online learning, many students face problems, causing their motivation, self-efficacy, and cognitive engagement to decrease. This study aims to develop a gamified learning application to provide supplementary learning support for students to fill this gap. The application consists of Object-oriented programming lessons for a single module. Assessments include game design elements such as points, timer, achievements, and leaderboard. Before application distribution and usability testing, the designed system was subjected to software compatibility and performance testing using Unity Simulator and Firebase Robo Test. Furthermore, the effectiveness of gamification on the group with application intervention compared to the group without is determined using a pretest-posttest technique.
Dynamic Vertex Hierarchies for Parallel View-Dependent Progressive Meshes

Triangles are one way we measure the complexity in a synthesized image. There often exists a trade-off between how realistic the synthesized image is based on the number of triangles and the overall rendering time. In real time applications, this trade-off becomes more important as we need to render detailed scenes at a fixed frame rate without sacrificing image quality. We propose a new view-dependent method that creates a more dynamic structure and is easy to parallelize. We also propose an amortized rebalancing operation to reduce long dependency lines in our data structure, prevent worst-case behavior, and in some instances improve the average case. Our implementation is still in-progress, but our new method is provably consistent and has potential to reduce the amount of storage needed for view-dependent methods and remove more geometry at higher fidelity, along with other performance improvements.
Time-Variant Seismic Risk Analysis of Transportation Networks Considering Economic and Socioeconomic Impacts

ABSTRACT This paper puts forward a new methodology for time-variant seismic risk analysis of transportation networks. Risk here denotes the probability of exceeding the sum of direct loss due to repair costs of bridges in the network and indirect loss due to the reduced capacity for traffic flow. The proposed methodology employs reliability methods and a host of probabilistic models for earthquake events, damage, repair cost, downtime, and travel time, while accounting for time-varying phenomena of aging and discounting. A comprehensive application on the transportation network of Isfahan, Iran, is presented that features 64 bridges subject to seismicity from seven sources.
Quantifying the Epistemic Uncertainty in the Probabilistic Seismic Hazard from Two Major Faults in Western Nevada

The probabilistic seismic hazards in the larger cities of western Nevada are dominated by the Mount Rose and Carson Range faults. These are normal faults; Reno and Carson City are on the hanging wall. This paper finds the sensitivity of the hazard posed by these faults to epistemic uncertainty of dip, slip rate, magnitude, and choice of ground motion prediction equation (GMPE) for an earthquake that ruptures the length of each fault. SA(0.01 s) with exceedance probability of 2% in 50 years was determined for each branch of a logic tree for those properties. This paper uses tools of probability theory to characterize this logic tree distribution with a lognormal distribution function, and to suggest an estimate of uncertainty in the mean hazard. In this case, which should not be considered general, on the hanging wall, the uncertainties in fault dip and the choice of the GMPE are the dominant contribution to the standard deviation of the lognormal distribution function. Elsewhere nearby, uncertainties in fault slip rate dominate, while at larger distances uncertainties in the magnitude dominate.
Paradol Induces Cell Cycle Arrest and Apoptosis in Glioblastoma Cells

Abstract Despite being the most common primary malignant tumor of the central nervous system, the prognosis of glioblastoma (GBM) is still remarkably poor. Paradol is a flavor phenolic constituent found in pepper and ginger, with anti-tumor, anti-inflammatory, and antioxidant activities. However, the effects of paradol on GBM cells remain unknown. In this study, we investigated the cytotoxicity of paradol on U-87 and U-251 GBM cells. Cell viability and Transwell assays revealed that paradol treatment markedly inhibited the viability and migration of GBM cells. Flow cytometry analysis showed G0/G1 cell cycle arrest, which was verified by the downregulation of CCNA and CCNB expression using western blotting. Paradol-induced cell apoptosis was confirmed by annexin V-FITC/PI staining and nuclear morphology. Furthermore, the phosphorylation of extracellular signal-regulated kinase (ERK) and p38 mitogen-activated protein kinase (MAPK) was determined by western blotting. Collectively, our data revealed that paradol inhibited cell viability and migration of GBM cells by inducing G0/G1 phase arrest and apoptosis, and activating ERK and p38 MAPK signaling.