Creativity involves not only generating new ideas from scratch but also redefining existing concepts and synthesizing previous insights. Among various techniques developed to foster creative thinking, brainstorming is widely used. With recent advancements in Large Language Models (LLMs), tools like ChatGPT have significantly impacted various fields by using prompts to facilitate complex tasks. While current research primarily focuses on generating accurate responses, there is a need to explore how prompt engineering can enhance creativity, particularly in brainstorming. Therefore, this study addresses this gap by proposing a framework called GPS, which employs goals, prompts, and strategies to guide designers to systematically work with an LLM tool for improving the creativity of ideas generated during brainstorming. Additionally, we adapted the Torrance Tests of Creative Thinking (TTCT) for measuring the creativity of the ideas generated by AI. Our framework, tested through a design example and a case study, demonstrates its effectiveness in stimulating creativity and its seamless LLM tool integration into design practices. The results indicate that our framework can benefit brainstorming sessions with LLM tools, enhancing both the creativity and usefulness of generated ideas.

We discuss ASASSN-24fw, a 13th-magnitude star that optically faded by $\Delta g = 4.12 \pm 0.02$ mag starting in September 2024 after over a decade of quiescence in ASAS-SN. The dimmimg lasted $\sim$8 months before returning to quiescence in late May 2025. The spectral energy distribution (SED) before the event is that of a pre-main sequence or a modestly evolved F star with some warm dust emission. The shape of the optical SED during the dim phase is unchanged and the optical and near-infrared spectra are those of an F star. The SED and the dilution of some of the F star infrared absorption features near minimum suggest the presence of a $\sim$0.25$M_\odot$ M dwarf binary companion. The 43.8 year period proposed by Nair & Denisenko (2024) appears correct and is probably half the precession period of a circumbinary disk. The optical eclipse is nearly achromatic, although slightly deeper in bluer filters, $\Delta (g-z)=0.31\pm0.15$ mag, and the $V$ band emission is polarized by up to 4%. The materials most able to produce such small optical color changes and a high polarization are big ($\sim$20 $\mu$m) carbonaceous or water ice grains. Particle distributions dominated by big grains are seen in protoplanetary disks, Saturn-like ring systems and evolved debris disks. We also carry out a survey of occultation events, finding 42 additional systems, of which only 7 (4) closely match $\varepsilon$ Aurigae (KH 15D), the two archetypes of stars with long and deep eclipses. The full sample is widely distributed in an optical color-magnitude diagram, but roughly half show a mid-IR excess. It is likely many of the others have cooler dust since it seems essential to produce the events.

This paper investigates the origin of the $\gamma$-ray emission from MGRO J1908+06 in the GeV-TeV energy band. By analyzing the data collected by {\it Fermi}-LAT, VERITAS, and HAWC, with the addition of spectral data previously reported by LHAASO, a multiwavelength (MW) study of the morphological and spectral features of MGRO J1908+06 provides insight into the origin of the $\gamma$-ray emission. The mechanism behind the bright TeV emission is studied by constraining the magnetic field strength, the source age and the distance through detailed broadband modeling. Both spectral shape and energy-dependent morphology support the scenario that inverse-Compton (IC) emission of an evolved pulsar wind nebula (PWN) associated with PSR J1907+0602 is responsible for the MGRO J1908+06 $\gamma$-ray emission with a best-fit true age of $T=22\pm 9$ kyr and a magnetic field of $B=5.4 \pm 0.8\ \mu\mathrm{G}$, assuming the distance to the pulsar $d_{\mathrm{PSR}}=3.2$ kpc.

Most smart systems such as smart home and smart health response to human's locations and activities. However, traditional solutions are either require wearable sensors or lead to leaking privacy. This work proposes an ambient radar solution which is a real-time, privacy secure and dark surroundings resistant system. In this solution, we use a low power, Frequency-Modulated Continuous Wave (FMCW) radar array to capture the reflected signals and then construct to 3D image frames. This solution designs $1)$a data preprocessing mechanism to remove background static reflection, $2)$a signal processing mechanism to transfer received complex radar signals to a matrix contains spacial information, and $3)$ a Deep Learning scheme to filter broken frame which caused by the rough surface of human's body. This solution has been extensively evaluated in a research area and captures real-time human images that are recognizable for specific activities. Our results show that the indoor capturing is clear to be recognized frame by frame compares to camera recorded video.

Imitation learning holds the promise to address challenging robotic tasks such as autonomous navigation. It however requires a human supervisor to oversee the training process and send correct control commands to robots without feedback, which is always prone to error and expensive. To minimize human involvement and avoid manual labeling of data in the robotic autonomous navigation with imitation learning, this paper proposes a novel semi-supervised imitation learning solution based on a multi-sensory design. This solution includes a suboptimal sensor policy based on sensor fusion to automatically label states encountered by a robot to avoid human supervision during training. In addition, a recording policy is developed to throttle the adversarial affect of learning too much from the suboptimal sensor policy. This solution allows the robot to learn a navigation policy in a self-supervised manner. With extensive experiments in indoor environments, this solution can achieve near human performance in most of the tasks and even surpasses human performance in case of unexpected events such as hardware failures or human operation errors. To best of our knowledge, this is the first work that synthesizes sensor fusion and imitation learning to enable robotic autonomous navigation in the real world without human supervision.

Though calculations based on density functional theory (DFT) are used remarkably widely in chemistry, physics, materials science, and biomolecular research and though the modern form of DFT has been studied for almost 60 years, some mathematical problems remain. For context, we provide an outline of the basic structure of DFT, then pose several questions regarding both its time-independent and time-dependent forms. Progress on any of these would aid in development of better approximate functionals and in interpretation.

The evolution of computer architecture has led to a paradigm shift from traditional single-core processors to multi-core and domain-specific architectures that address the increasing demands of modern computational workloads. This paper provides a comprehensive study of this evolution, highlighting the challenges and key advancements in the transition from single-core to multi-core processors. It also examines state-of-the-art hardware accelerators, including Tensor Processing Units (TPUs) and their derivatives, RipTide and the Catapult fabric, and evaluates their strategies for optimizing critical performance metrics such as energy consumption, latency, and flexibility. Ultimately, this study emphasizes the role of reconfigurable systems in overcoming current architectural challenges and driving future advancements in computational efficiency.

Movements for social change are often tied to a particular locale. This makes Augmented Reality (AR), which changes how people perceive their surroundings, a promising technology for social justice. Site-specific AR empowers activists to re-tell the story of a place, with or without permission of its owner. It has been used, for example, to reveal hidden histories, re-imagine problematic monuments, and celebrate minority cultures. However, challenges remain concerning technological ownership and accessibility, scalability, sustainability, and navigating collaborations with marginalized communities and across disciplinary boundaries. This half-day workshop at CHI 2024 seeks to bring together an interdisciplinary group of activists, computer scientists, designers, media scholars, and more to identify opportunities and challenges across domains. To anchor the discussion, participants will each share one example of an artifact used in speculating, designing, and/or delivering site-specific AR experiences. This collection of artifacts will inaugurate an interactive database that can inspire a new wave of activists to leverage AR for social justice.

We describe the remote facilities operated by the Southeastern Association for Research in Astronomy (SARA), a consortium of colleges and universities in the US partnered with Lowell Observatory, the Chilean National Telescope Allocation Committee, and the Instituto de Astrofisica de Canarias. SARA observatories comprise a 0.96m telescope at Kitt Peak, Arizona; a 0.6m instrument on Cerro Tololo, Chile; and the 1m Jacobus Kapteyn Telescope at the Roque de los Muchachos, La Palma, Spain. All are operated using standard VNC or Radmin protocols communicating with on-site PCs. Remote operation offers considerable flexibility in scheduling, allowing long-term observational cadences difficult to achieve with classical observing at remote facilities, as well as obvious travel savings. Multiple observers at different locations can share a telescope for training, educational use, or collaborative research programs. Each telescope has a CCD system for optical imaging, using thermoelectric cooling to avoid the need for frequent local service, and a second CCD for offset guiding. The Arizona and Chile instruments also have fiber-fed echelle spectrographs. Switching between imaging and spectroscopy is very rapid, so a night can easily accommodate mixed observing modes. We present some sample observational programs. For the benefit of other groups organizing similar consortia, we describe the operating structure and principles of SARA, as well as some lessons learned from almost 20 years of remote operations.

This paper reports on results from an experiment designed to search for exotic particles interacting with nuclear matter. These particles could be created through the Primakoff coupling between photons and an external magnetic field. Theory suggests this coupling leads to the production of weakly interacting particles (e.g. axions) that are important to understanding the lack of a measured neutron electric dipole moment (nEDM). The current experiment has been run to look for evidence of weakly interacting particles, created by photons propagating through a magnetic field, by studying their influence on the measured decay spectrum of Americium (241Am). The results shown here reflect a statistically significant difference (sigma > 6.0) between observed decays when the experiment was run in a mode that allowed photons to traverse a magnetic field (light mode or sP for system-Photons) when compared to a second mode where the light was blocked from entering the cavity (dark mode or sD for system-Dark). This difference was observed to impact the count rate for the release of a 59.54keV gamma from 237Np. Repeated experimentation suggests the effect is robust and not due to spurious changes in background events. This could be confirmation that the Primakoff mechanism has been observed for visible photons. As importantly, this experiment looks at the possibility to develop a novel nuclear instrument that can modify nuclear decay rates.

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension this http URL there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowdsourced questions from Latinx caregivers and noncaregivers as well as caregivers and noncaregivers from other demographics. We examine variations in question--asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open--ended questions that are representative of those asked by Latinx caregivers.

Superluminous supernovae (SLSNe) are a rare class of stellar explosions with luminosities ~10-100 times greater than ordinary core-collapse supernovae. One popular model to explain the enhanced optical output of hydrogen-poor (Type I) SLSNe invokes energy injection from a rapidly spinning magnetar. A prediction in this case is that high-energy gamma rays, generated in the wind nebula of the magnetar, could escape through the expanding supernova ejecta at late times (months or more after optical peak). This paper presents a search for gamma-ray emission in the broad energy band from 100 MeV to 30 TeV from two Type I SLSNe, SN2015bn, and SN2017egm, using observations from Fermi-LAT and VERITAS. Although no gamma-ray emission was detected from either source, the derived upper limits approach the putative magnetar's spin-down luminosity. Prospects are explored for detecting very-high-energy (VHE; 100 GeV - 100 TeV) emission from SLSNe-I with existing and planned facilities such as VERITAS and CTA.

In-band full duplex wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, however, is throttled by its key challenge, namely self-interference. Therefore, effective self-interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non-linear self-interference cancellation solution based on deep learning. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self-interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self-interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.

Sequence-based specification and usage-driven statistical testing are designed for rigorous and cost-effective software development, offering a semi-formal approach to assessing the behavior of complex systems and interactions between various components. This approach is particularly valuable for scientific computing applications in which comprehensive tests are needed to prevent flawed results or conclusions. As scientific discovery becomes increasingly more complex, domain scientists couple multiple scientific computing models or simulations to solve intricate multiphysics and multiscale problems. These model-coupling applications use a hardwired coupling program or a flexible web service to link and combine different models. In this paper, we focus on the quality assurance of the more elastic web service via a combination of rigorous specification and testing methods. The application of statistical testing exposes problems ignored by pre-written unit tests and highlights areas in the code where failures might occur. We certify the model-coupling server controller with a derived reliability statistic, offering a quantitative measure to support a claim of its robustness.

In the field of Artificial Intelligence, traditional approaches to choosing moves in games involve the we of the minimax algorithm. However, recent research results indicate that minimizing may not always be the best approach. In this paper we summarize the results of some measurements on several model games with several different evaluation functions. These measurements, which are presented in detail in [NPT], show that there are some new algorithms that can make significantly better use of evaluation function values than the minimax algorithm does.

The Open Dataset of Audio Quality (ODAQ) was recently introduced to address the scarcity of openly available audio datasets with corresponding subjective quality scores. The dataset, released under permissive licenses, comprises audio material processed using six different signal processing methods operating at five quality levels, along with corresponding subjective test results. To expand the dataset, we provided listener training to university students to conduct further subjective tests and obtained results consistent with previous expert listeners. We also showed how different training approaches affect the use of absolute scales and anchors. The expanded dataset now comprises results from three international laboratories providing a total of 42 listeners and 10080 subjective scores. This paper provides the details of the expansion and an in-depth analysis. As part of this analysis, we initiate the use of ODAQ as a benchmark to evaluate objective audio quality metrics in their ability to predict subjective scores

The ground-based gamma-ray observatory VERITAS (Very Energetic Radiation Imaging Telescope Array System) is sensitive to photons of astrophysical origin with energies in the range between $\approx 85$ GeV to $\approx 30$ TeV. The instrument consists of four 12-m diameter imaging Cherenkov telescopes operating at the Fred Lawrence Whipple Observatory (FLWO) in southern Arizona. VERITAS started four-telescope operations in 2007 and collects about 1100 hours of good-weather data per year. The VERITAS collaboration has published over 100 journal articles since 2008 reporting on gamma-ray observations of a large variety of objects: Galactic sources like supernova remnants, pulsar wind nebulae, and binary systems; extragalactic sources like star forming galaxies, dwarf-spheroidal galaxies, and highly-variable active galactic nuclei. This note presents VTSCat: the catalog of high-level data products from all VERITAS publications.

The Electron, Proton and Alpha Monitor, EPAM, located at the L1 Position approximately 1-million miles from the earth in the direction of the sun, was designed to detect fluctuations in solar output through counting the numbers of various particles hitting the detector. The EPAM detector is part of an early warning system that can alert the earth to coronal mass ejection events that can damage our electronic grids and satellite equipment. EPAM gives a real-time estimate of changes in the local solar magnetic field directed towards the earth, recorded in the fluctuations of solar particles being ejected. This paper presents an analysis of fluctuations in data taken by the Geological Survey of Israel, GSI, compared to the changes in detected numbers of protons as seen by EPAM. Surprisingly, the GSI and EPAM detectors show an unexpected correlation between the variation in count rate detected by the GSI detectors and an increased numbers of protons seen at EPAM; well above statistical significance of 5-sigma, indicating a non-random connection between the data sets. The statistically significant overlap between data taken by these two detectors, subject to very different conditions, may hint at a Primakoff mechanism whereby exotic particles, e.g. galactic Dark Matter, couple through magnetic fields to both photons and even nuclei. This work builds on an earlier paper on the observations of Radon decay and their implications for particle physics.

We report the detection of very high energy gamma-ray emission from the blazar S3 1227+25 (VER J1230+253) with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). VERITAS observations of the source were triggered by the detection of a hard-spectrum GeV flare on May 15, 2015 with the Fermi-Large Area Telescope (LAT). A combined five-hour VERITAS exposure on May 16th and May 18th resulted in a strong 13$\sigma$ detection with a differential photon spectral index, $\Gamma$ = 3.8 $\pm$ 0.4, and a flux level at 9% of the Crab Nebula above 120 GeV. This also triggered target of opportunity observations with Swift, optical photometry, polarimetry and radio measurements, also presented in this work, in addition to the VERITAS and Fermi-LAT data. A temporal analysis of the gamma-ray flux during this period finds evidence of a shortest variability timescale of $\tau_{obs}$ = 6.2 $\pm$ 0.9 hours, indicating emission from compact regions within the jet, and the combined gamma-ray spectrum shows no strong evidence of a spectral cut-off. An investigation into correlations between the multiwavelength observations found evidence of optical and gamma-ray correlations, suggesting a single-zone model of emission. Finally, the multiwavelength spectral energy distribution is well described by a simple one-zone leptonic synchrotron self-Compton radiation model.

A positive integer $k$ is called a magic constant if there is a graph $G$ along with a bijective function $f$ from $V(G)$ to first $|V(G)|$ natural numbers such that the weight of the vertex $w(v) = \sum_{uv \in E}f(v) =k$ for all $v \in V$. It is known that all odd positive integers greater equal $3$ and the integer powers of $2$, $2^{t}$, $t \ge 6$ are magic constants. In this paper we characterise all positive integers which are magic constants.