Browsing by Type "conference papers and proceedings"

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#1ReasonWhy Gamers <3 Dickwolves: Understanding Sexism in the Gaming Community
(Conference: 2013 Computers & Writing, 2013) Salter, Anastasia; Blodgett, Bridget Marie
#1ReasonWhy: Game Communities and the Invisible Woman
(Foundations of Digital Games, 2014-04) Blodgett, Bridget M.; Salter, Anastasia
As Cliff Bleszinski [6] states in his blog post the video game community has its own type of religious police charged with enforcing a doctrine regarding gender representation within the gaming community. From the difficulties faced by Anita Skareesian to the resignation of Jennifer Hepler, women who are visible within the industry regularly face threats from the very people who claim to be their entertainment comrades. With the rise of the #1ReasonWhy hashtag on Twitter in 2012, the experiences of women who aren’t in gaming community’s spotlight were also brought to the forefront. This paper uses a thematic analysis of tweets made between November 26th - 29th in the #1ReasonWhy to examine how the explicit and implicit threats of violence, rape, and harassment have manifested within the working world of game development. Through examination of these tweets the authors will show how these threats both from the general gaming community and inside the office workspace shape the experiences of women and continue the decades long cycle of limited participation by women. The authors point out how the cultural dialogue about the potential good games offer to our society may be outweighed by the hostile community climate which places those who could benefit most at risk.
4U 1909+07: a well-hidden pearl (Conf. Proc.)
(2010-10-28) Fürst, F.; Kreykenbohm, I.; Barragán, L.; Wilms, J.; Rothschild, R. E.; Suchy, S.; Pottschmidt, Katja
We present the first detailed spectral and timing analysis of the High Mass X-ray Binary (HMXB) 4U 1909+07 with INTEGRAL and RXTE. 4U 1909+07 is detected with an average of 2.4 cps in ISGRI, but shows flares up to ∼50 cps. The system shows a pulse period of 605 s, but we found that the period changes erratically around this value. The pulse profile is extremely energy dependent: while it shows a double peaked structure at low energies, the secondary pulse decreases rapidly with increasing energy and above 20 keV only the primary pulse is visible. This evolution is consistent between PCA, HEXTE and ISGRI. We find that the phase averaged spectrum can be well fitted with a photoabsorbed power law with a cutoff at high energies and a blackbody component. To investigate the peculiar pulse profile, we performed phase resolved spectral analysis. We find that a change in the cutoff energy is required to fit the changing spectrum of the different pulse phases.
A 50 mK test bench for demonstration of the readout chain of Athena/X-IFU
(SPIE, 2022-08-31) Castellani, Florent; Beaumont, Sophie; Pajot, François; Roudil, Gilles; Adams, Joseph; Sakai, Kazuhiro; Wakeham, Nicholas; et al
The X-IFU (X-ray Integral Field Unit) onboard the large ESA mission Athena (Advanced Telescope for High ENergy Astrophysics), planned to be launched in the mid 2030s, will be a cryogenic X-ray imaging spectrometer operating at 55 mK. It will provide unprecedented spatially resolved high-resolution spectroscopy (2.5 eV FWHM up to 7 keV) in the 0.2-12 keV energy range thanks to its array of TES (Transition Edge Sensors) microcalorimeters of more than 2k pixel. The detection chain of the instrument is developed by an international collaboration: the detector array by NASA/GSFC, the cold electronics by NIST, the cold amplifier by VTT, the WFEE (Warm Front-End Electronics) by APC, the DRE (Digital Readout Electronics) by IRAP and a focal plane assembly by SRON. To assess the operation of the complete readout chain of the X-IFU, a 50 mK test bench based on a kilo-pixel array of microcalorimeters from NASA/GSFC has been developed at IRAP in collaboration with CNES. Validation of the test bench has been performed with an intermediate detection chain entirely from NIST and Goddard. Next planned activities include the integration of DRE and WFEE prototypes in order to perform an end-to-end demonstration of a complete X-IFU detection chain.
The 9th SIGKDD International Workshop on Mining and Learning from Time Series
(ACM, 2023-08-04) Purushotham, Sanjay; Song, Dongjin; Wen, Qingsong; Huan, Jun; Shen, Cong; Nevmyvaka, Yuriy
Time series data has become pervasive across domains such as finance, transportation, retail, entertainment, and healthcare. This shift towards continuous monitoring and recording, fueled by advancements in sensing technologies, necessitates the development of new tools and solutions. Despite extensive study, the importance of time series analysis continues to increase. However, modern time series data present challenges to existing techniques, including irregular sampling and spatiotemporal structures. Time series mining research is both challenging and rewarding as it connects diverse disciplines and requires interdisciplinary solutions. The goals of this workshop are to (1) highlight the significant challenges that underpin learning and mining from time series data (e.g., irregular sampling, spatiotemporal structure, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) to synergize the research activities and discuss both new and open problems in time series analysis and mining. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from academia and industry to discuss potential research directions and critical technical issues and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.
A11yFutures: Envisioning the Future of Accessibility Research
(ACM, 2023-10-23) Mankoff, Jennifer; Mack, Kelly Avery; Wiese, Jason; Crawford, Kirk; Hamidi, Foad
The future of accessibility research is a topic we take up every day as researchers; yet it is important also to step back and ask ourselves about the most important, and overlooked, areas for inquiry in our field. With the rapid pace of change in both computational capabilities and the environmental, social, and political context in which disability plays out, we believe this is a critical time for such inquiry. This is even more important given the relatively narrow set of topics that accessibility research has focused on for most of our field’s history. We invite our community to come together to define what the next generation of accessibility research should engage with.
Abstracting Interactions with IoT Devices Towards a Semantic Vision of Smart Spaces
(Association for Computing Machinery, 2019-11-13) Yus, Roberto; Bouloukakis, Georgios; Mehrotra, Sharad; Venkatasubramanian, Nalini
This paper describes a middleware framework for IoT smart spaces, SemIoTic, that provides application developers and end-users with the semantic domain-relevant view of the smart space, hiding the complexity of having to deal with/understand lower-level information generated by sensors and actuators. SemIoTic uses a meta-model, based on the popular SOSA/SSN ontology with some extensions, to represent relationships between the low-level IoT devices' world (i.e., devices, observations) and semantic concepts (i.e., users and spaces and their observable attributes). It supports a language using which users can express their action requirements (i.e., requests for sensor data, commands for actuators, and privacy preferences) in terms of user-friendly high-level concepts. We present an ontology-based algorithmic approach to translate user-defined actions into sensor/actuators commands. Finally, our end-to-end approach includes a cross-layer solution to provide interoperability with diverse IoT devices and their data exchange protocols.
Accelerated AI for Edge Computing
(2020-02-25) Rahnemoonfar, Maryam
Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch
(2023-10-02) Obe, Ruth; Kaufmann, Brandt; Baird, Kaelen; Kadel, Sam; Soltani, Yasmin; Cham, Mostafa; Gobbert, Matthias; Barajas, Carlos A.; Jiang, Zhuoran; Sharma, Vijay R.; Ren, Lei; Peterson, Stephen W.; Polf, Jerimy C.
Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mitigate unnecessary radiation exposure in healthy tissues. Realtime imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera’s non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera’s image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch’s flexibility, powerful capabilities in handling sequential data, and enhanced GPU usage. This accelerates the model’s computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7% to over 60%. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple GPUs, which significantly reduced the training time with a minor impact on model accuracy.
Acceptance of a speech interface for biomedical data collection.
(1997) Grasso, M. A.; Ebert, D.; Finin, Tim
Speech interfaces have the potential to address the data entry bottleneck of many applications in the field of medical informatics. An experimental study evaluated the effect of perceptual structure on a multimodal speech interface for the collection of histopathology data. A perceptually structured multimodal interface, using speech and direct manipulation, was shown to increase speed and accuracy. Factors influencing user acceptance are also discussed.
Accepting Educational Responsibility Through Technologies in Afrikan Cultures
(American Educational Research Association (AERA), 2021-04-09) Asino, Tutaleni I.; Young, Patricia; Kinuthia, Wanjira
Afrikan countries and cultures create solutions for the needs and conditions of their people, yet their contributions to technological innovations continue to be overlooked. In this paper, we argue that app developers on the Afrikan continent have been accepting educational responsibility through the creation of mobile applications that specifically meet the needs of the people. From a qualitative research perspective, we conduct a systematic review and cultural studies analyses to explore Afrikan innovations to mobile applications. The findings reveal that mobile developers are 1) building apps that are specific to the Afrikan context and 2) making use of existing apps and appropriating them to the Afrikan context.
Accretion geometry in the persistent Be/X-ray binary RXJ0440.9+4431
(EDP Sciences, 2014-01-08) Ferrigno, C.; Farinelli, R.; Bozzo, E.; Pottschmidt, Katja; Klochkov, D.; Kretschmar, P.
The persistent Be/X-ray binary RXJ0440.9+4431 flared in 2010 and 2011 and has been followed by various X-ray facilities (Swift, RXTE, XMM-Newton, and INTEGRAL). We studied the source timing and spectral properties as a function of its X-ray luminosity to investigate the transition from normal to flaring activity. The source spectrum can always be described by a bulk-motion Comptonization model of black body seed photons attenuated by a moderate photoelectric absorption. At the highest luminosity, we measured a curvature of the spectrum, which we attribute to a significant contribution of the radiation pressure in the accretion process. This allows us to estimate that the transition from a bulk-motion-dominated flow to a radiatively dominated one happens at a luminosity of ~ 2 × 10³⁶ erg s⁻¹. The luminosity dependency of the size of the black body emission region is found to be rBB ∝ LX ⁰.³⁹±⁰.⁰². This suggests that either matter accreting onto the neutron star hosted in RXJ0440.9+4431 penetrates through closed magnetic field lines at the border of the compact object magnetosphere or that the size of the black-body emitting hotspot is larger than the footprint of the accretion column. This phenomenon can be due to illumination of the surface by a growing column or by a a structure of the neutron star magnetic field more complicated than a simple dipole at least close to the surface.
Acetylcholine and Acetylcholine Receptors in Taste Receptor Cells
(Oxford University Press, 2005-01-01) Ogura, Tatsuya; Lin, Weihong
Acoustic steering of audible and ultrasonic waves using THermally-induced Optical Reflection of Sound (THORS)
(SPIE, 2021-04-12) Kazal, Danny; Holthoff, Ellen L.; Cullum, Brian
In this work, we describe the phenomenon, Thermally-induced Optical Reflection of Sound (THORS), and how it can be used to optically steer acoustic waves around a 90 degree corner of a physical obstruction, where observed acoustic amplitudes are increased by a factor of 30. In addition, we discuss the introduction of ultrasonic waves to the THORS phenomenon, and preliminary results for THORS barriers generated in ambient air, using a 5.3-5.7 μm CO laser source.The manipulation and guiding of sound waves have typically required the use of physical barriers for the reflection of an incident pressure wave. With the manipulation of acoustic waves being critical for many applications in scientific and engineering fields, including subsurface tissue imaging, photoacoustic sensing, secure communications, acoustic stealth technology, and acoustic design engineering; the requirement for physical barriers often represents a significant limitation. The recently discovered phenomenon THermally-induced Optical Reflection of Sound (THORS), provides the ability to generate acoustically reflective barriers, in air, by exciting media in the path of an IR laser beam, causing abrupt changes in compressibility between the excited and surrounding media. In this work, we demonstrate the ability to efficiently reflect sound waves around physical obstructions using a laser. Additionally, this work demonstrates the ability to also manipulate ultrasonic waves via THORS barriers, where the reflection and suppression of ultrasonic pulses in the frequency range of 120-300 kHz are shown. Finally, preliminary results demonstrating the ability to employ THORS in ambient air using water vapor as the absorbing media and a 5.5 μm CO laser beam for excitation.
Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data
(SPIE, 2020-03-16) Nguyen, Phuong; Chapman, David; Menon, Sumeet; Morris, Michael; Yesha, Yelena
Artificial intelligence (AI) has great potential in medical imaging to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. There is a lack of available, reliable, discretized and annotated labels for computer vision research in radiology despite the abundance of diagnostic imaging examinations performed in routine clinical practice. Furthermore, the process to create reliable labels is tedious, time consuming and requires expertise in clinical radiology. We present an Active Semi-supervised Expectation Maximization (ASEM) learning model for training a Convolutional Neural Network (CNN) for lung cancer screening using Computed Tomography (CT) imaging examinations. Our learning model is novel since it combines Semi-supervised learning via the Expectation-Maximization (EM) algorithm with Active learning via Bayesian experimental design for use with 3D CNNs for lung cancer screening. ASEM simultaneously infers image labels as a latent variable, while predicting which images, if additionally labeled, are likely to improve classification accuracy. The performance of this model has been evaluated using three publicly available chest CT datasets: Kaggle2017, NLST, and LIDC-IDRI. Our experiments showed that ASEM-CAD can identify suspicious lung nodules and detect lung cancer cases with an accuracy of 92% (Kaggle17), 93% (NLST), and 73% (LIDC) and Area Under Curve (AUC) of 0.94 (Kaggle), 0.88 (NLST), and 0.81 (LIDC). These performance numbers are comparable to fully supervised training, but use only slightly more than 50% of the training data labels .
Adaptive and Efficient Streaming Time Series Forecasting with Lambda Architecture and Spark
(IEEE, 2021-03-19) Pandya, Arjun; Odunsi, Oluwatobiloba; Liu, Chen; Cuzzocrea, Alfredo; Wang, Jianwu
The rise of the Internet of Things (IoT) devices and the streaming platform has tremendously increased the data in motion or streaming data. It incorporates a wide variety of data, for example, social media posts, online gamers in-game activities, mobile or web application logs, online e-commerce transactions, financial trading, or geospatial services. Accurate and efficient forecasting based on real-time data is a critical part of the operation in areas like energy & utility consumption, healthcare, industrial production, supply chain, weather forecasting, financial trading, agriculture, etc. Statistical time series forecasting methods like Autoregression (AR), Autoregressive integrated moving average (ARIMA), and Vector Autoregression (VAR), face the challenge of concept drift in the streaming data, i.e., the properties of the stream may change over time. Another challenge is the efficiency of the system to update the Machine Learning (ML) models which are based on these algorithms to tackle the concept drift. In this paper, we propose a novel framework to tackle both of these challenges. The challenge of adaptability is addressed by applying the Lambda architecture to forecast future state based on three approaches simultaneously: batch (historic) data-based prediction, streaming (real-time) data-based prediction, and hybrid prediction by combining the first two. To address the challenge of efficiency, we implement a distributed VAR algorithm on top of the Apache Spark big data platform. To evaluate our framework, we conducted experiments on streaming time series forecasting with four types of data sets of experiments: data without drift (no drift), data with gradual drift, data with abrupt drift and data with mixed drift. The experiments show the differences of our three forecasting approaches in terms of accuracy and adaptability.
Adaptive Energy Control of Longitudinal Aircraft Dynamics
(American Institute of Aeronautics and Astronautics, 2021-12-29) Anandakumar, Ashwin; Bernstein, Dennis S.; Goel, Ankit
The Total Energy Control System (TECS) is a method to control airplane longitudinal flight dynamics by regulating energy and energy balance. This multiple input, multiple output method accounts for the highly coupled nature of aircraft dynamics and allows for control of airspeed and altitude with a proportional-integral controller. This paper reviews the heuristic TECS control law and then validates it in a MATLAB simulation. Next, an adaptive TECS is designed by augmenting the fixed-gain controllers in a nominal TECS with retrospective cost optimization-based adaptive controllers. It is shown through simulations that adaptive augmentation improves the closed-loop performance and accelerates the tuning process.
Adaptive Token Sampling For Efficient Vision Transformers
(Springer, 2022-11-03) Fayyaz, Mohsen; Koohpayegani, Soroush Abbasi; Jafari, Farnoush Rezaei; Sengupta, Sunando; Joze, Hamid Reza Vaezi; Sommerlade, Eric; Pirsiavash, Hamed; Gall, Juergen
While state-of-the-art vision transformer models achieve promising results in image classification, they are computationally expensive and require many GFLOPs. Although the GFLOPs of a vision transformer can be decreased by reducing the number of tokens in the network, there is no setting that is optimal for all input images. In this work, we therefore introduce a differentiable parameter-free Adaptive Token Sampler (ATS) module, which can be plugged into any existing vision transformer architecture. ATS empowers vision transformers by scoring and adaptively sampling significant tokens. As a result, the number of tokens is not constant anymore and varies for each input image. By integrating ATS as an additional layer within the current transformer blocks, we can convert them into much more efficient vision transformers with an adaptive number of tokens. Since ATS is a parameter-free module, it can be added to the off-the-shelf pre-trained vision transformers as a plug and play module, thus reducing their GFLOPs without any additional training. Moreover, due to its differentiable design, one can also train a vision transformer equipped with ATS. We evaluate the efficiency of our module in both image and video classification tasks by adding it to multiple SOTA vision transformers. Our proposed module improves the SOTA by reducing their computational costs (GFLOPs) by 2 ×, while preserving their accuracy on the ImageNet, Kinetics-400, and Kinetics-600 datasets. The code is available at https://adaptivetokensampling.github.io/ .
Advanced fabrication technologies for ultraprecise replicated mirrors for x-ray telescopes
(SPIE, 2020-12-13) Mimura, Hidekazu; Yamaguchi, Gota; Kume, Takehiro; Takeo, Yoko; Ito, Akinari; Matsuzawa, Yusuke; Saito, Takahiro; Hiraguri, Kentarou; Takehara, Yusuke; Takigawa, Ayumu; Tamura, Keisuke; Kanoh, Tetsuo; Tachibana, Kenji; Hashizume, Hirokazu; Mitsuishi, Ikuyuki
For many years, Wolter mirrors have been used as imaging elements in X-ray telescopes. The shape error of Wolter mirrors fabricated by replicating the shape of a mandrel originates from the replication error in electroforming. We have been developing an X-ray focusing mirror for synchrotron radiation X-rays, as well as a high-precision electroforming process. In this paper, we report on the application of the advanced electroforming process to the fabrication of Wolter mirrors for the FOXSI Sun observation project. We also discuss the figuring accuracy of the mandrel.
Advances in zinc sensors for studying zinc release events from pancreatic cells
(SPIE, 2006-10-25) Rosenzweig, Zeev; Rosenzweig, Nitsa; Crivat, Georgeta
This paper describes the fabrication and characterization of the analytical properties of fluorescence-based zinc ion sensing glass slides and antibody based zinc sensors and their application in monitoring zinc release from beta pancreatic cells. The zinc ion indicator ZnAF-2 {6-[N- [N', N'-bis (2-pyridinylmethyl)-2-aminoethyl] amino-3',6'-dihydroxy-spiro[isobenzofuran-1(3H),9'-[9H] xanthene]-3-one} was modified to include a sufficiently long linking aliphatic chain, with a terminal carboxyl functional group. The activated carboxyl-modified ZnAF-2 was conjugated to the amino silanized surface of glass slides and to free amino groups of the A2B5 antibody molecules. The sensors were used to monitor zinc ion release events from glucose-stimulated pancreatic cells.

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