Miracle-dz/UniFAR-FTUs huggingface dataset:
FusionCache: Using LLC tags for DRAM cache

DRAM caches have been shown to be an effective way to utilize the bandwidth and capacity of 3D stacked DRAM. Although they can capture the spatial and temporal data locality of applications, their access latency is still substantially higher than conventional on-chip SRAM caches. Moreover, their tag access latency and storage overheads are excessive. Storing tags for a large DRAM cache in SRAM is impractical as it would occupy a significant fraction of the processor chip. Storing them in the DRAM itself incurs high access overheads. Attempting to cache the DRAM tags on the processor adds a constant delay to the access time. In this paper, we introduce FusionCache, a DRAM cache that offers more efficient tag accesses by fusing DRAM cache tags with the tags of the on-chip Last Level Cache (LLC). We observe that, in an inclusive cache model where the DRAM cachelines are multiples of on-chip SRAM cachelines, LLC tags could be re-purposed to access a large part of the DRAM cache contents. Then, accessing DRAM cache tags incurs zero additional latency in the common case.
Making the News: Digital Creativity Support for Journalists

This paper reports the design and first evaluations of new digital support for journalists to discover and examine crea-tive angles on news stories under development. The support integrated creative news search algorithms, interactive crea-tive sparks and reusable concept cards into one daily work tool of journalists. The first evaluations of INJECT by jour-nalists in their places of work to write published news sto-ries revealed that the journalists generated new angles on existing stories rather than new stories, changed their writ-ing behaviour, and reported evidence that INJECT use had the potential to increase the objectivity and the boldness of journalism methods used.
Metamaterial Textures

We present metamaterial textures---3D printed surface geometries that can perform a controlled transition between two or more textures. Metamaterial textures are integrated into 3D printed objects and allow designing how the object interacts with the environment and the user's tactile sense. Inspired by foldable paper sheets ("origami") and surface wrinkling, our 3D printed metamaterial textures consist of a grid of cells that fold when compressed by an external global force. Unlike origami, however, metamaterial textures offer full control over the transformation, such as in between states and sequence of actuation. This allows for integrating multiple textures and makes them useful, e.g., for exploring parameters in the rapid prototyping of textures. Metamaterial textures are also robust enough to allow the resulting objects to be grasped, pushed, or stood on. This allows us to make objects, such as a shoe sole that transforms from flat to treaded, a textured door handle that provides tactile feedback to visually impaired users, and a configurable bicycle grip. We present an editor assists users in creating metamaterial textures interactively by arranging cells, applying forces, and previewing their deformation.
Selective harmonic elimination in multilevel inverters using gravitational search algorithm

Selective harmonic elimination (SHE) techniques are widely employed in multilevel inverters (MLIs) for the calculation of switching angles in such a manner that the desired fundamental output voltage is obtained while simultaneously eliminating the dominant low order harmonics. In order to obtain this objective, the solution of nonlinear transcendental equations by means of algebraic, iterative and optimization techniques is required. In this article, Gravitational Search Algorithm (GSA) based SHE technique is applied to find the optimum switching angles for 11-level cascaded MLI. A comparative study has been carried out which showed that the GSA based SHE method provided more precise results with less number of iterations as compared to the Genetic Algorithm (GA) based SHE method. GSA based SHE technique has been programmed in MATLAB/Simulink software environment. Simulation and harmonic analysis results exhibit the efficacy of GSA in the elimination of low order harmonics while obtaining the desired fundamental voltage at the same time.
Observations and Opportunities for Deploying Virtual Reality for Passenger Boats

We identify difficulties and opportunities in using virtual reality on passenger boats. As part of a larger ecotourism study, we experimented with the use of virtual reality (VR) on a boat using a head mounted display. This paper gives an overview of the causes and current knowledge of motion sickness, and details our experience of using VR on a catamaran. While there may be some liMassachusetts Institute of Technologyations and challenges to overcome related to current hardware offerings, there are also opportunities in minimizing seasickness through visual distraction that are worth investigating further.
Effect of dynamic threshold on sensing duration for robust detection in cognitive radio systems for low SNR scenarios

Cognitive Radio aims at solving the spectrum scarcity issue and Spectrum sensing seems to be the quintessence of the cognitive radio system. The performance of a spectrum sensing method is scrutinized by classical parameters like detection probability, false alarm probability and sensing duration. These parameters are affected in practical scenarios by noise uncertainty and fixed threshold concept. This paper revisits the energy detection technique for sensing the spectrum and attempts to improve the sensing duration and detection probability in an uncertain noise environment with low SNR. An approach to relate sensing duration with dynamic threshold is presented which can help to achieve desirable and robust performance in terms of probability of detection at low SNR in presence of noise uncertainty factor.
Human identification and verification based on signature, fingerprint and iris integration

Fingerprint, Signature, Iris biometric identification are some of the best authentication system used nowadays. Biometric system finds its application in fields like passport, banking, mobiles or laptops (i.e. gadgets), houses, offices, Aadhaar Card, forensic depts. etc. The aim of this paper is to discuss the Steps involved in identifying and verifying users using biometric systems along with their advantages and disadvantages. This paper also discusses about various performance parameters, applications and future scope of these biometric systems.
WiGym: Exercise recognition and counting using Wi-Fi signals

In this paper, we propose a Wi-Fi based passive tracking system WiGym, which recognizes and counts the fitness exercises performed by the user. The body movements change the multi paths reflected against the body, and thus generate the unique pattern in CSI time series. To count the number of exercises, short time energy is calculated. To extract features from CSI time series, discrete wavelet transform is performed. We implemented WiGym using commercial Wi-Fi devices and conducted extensive experiments to verify its performance. Our evaluation shows that WiGym achieves 100% recognition accuracy and 95.6% segmentation accuracy. Compared with previous on-body sensor based fitness tracking systems, it is more ubiquitous and requires no hardware modifications.
Discovering latent spatial structured patterns using graph models for scene classification

Despite progress in scene recognition tasks such as image classification and attribute detection, computers still be difficult to understand the scenes as a whole. Existing methods often ignore global spatial constructed pattern among different local semantic objects. This paper propose a method for discovering the Latent spatial structured patterns to describe the visual semantic characters of images to improve the performance of scene recognition tasks. Unlike the existing approaches that mainly rely on the discriminant visual feature cues, we learn the latent spatial structured pattern to model the interaction relationships by using the graph models, which consider semantics and their localization information. We first train the pLSA models to obtain the latent semantic topics. Then we construct the graph models to discover the latent spatial structure patterns with combing the character vector and localization cues. Meanwhile, we treat the edge in model as link-affinity matrix to describe the interaction relationships between semantics. The extensive experiments on public datasets have demonstrated that the suggested method can significantly boost the performance of scene classification tasks.
Relative object localization using logistic regression

This paper discusses the technique of relative localization of object (Region Of Interest) based on logistic regression classifier. In proposed method, local features for the whole image computed and represented as a set of vectors for training the logistic regression classifier to classify the given feature vector either to the region of interest or the background. With the classification, the background and the region of interest can be separated. Then, the region of interest is localized with due reference to the background in the subsequent images. The method shows advancement in the object localization and it additionally works in real time without being the computationally comprehensive approach.
Malaria Parasite Detection and Species Identification on Thin Blood Smears Using a Convolutional Neural Network

To aid efforts for the total elimination of malaria, effective and fast diagnosis of cases must be done. The gold standard for malaria diagnosis is microscopy. This process becomes problematic when cases are in far-flung rural areas as experts may not be present in these areas to make such diagnosis. Automation of the diagnostic process with the use of an intelligent system that would recognize malaria parasites could solve this problem. This study proposes such an intelligent system, detecting malaria parasites through images of thin blood smears. We used a Convolutional Neural Network in this research where we obtained an accuracy of 92.4% and sensitivity of 95.2% for malaria parasite detection, and an accuracy of 87.9% for identifying the two species Plasmodium falciparum and Plasmodium vivax.
“That Really Pushes My Buttons”: Designing Bullying and Harassment Training for the Workplace

Workplace bullying and harassment have been identified as two of the most concerning silent and unseen occupational hazards of the 21st century. The design of bespoke training addressing domain-specific job roles and relations presents a particular challenge. Using the concept of data-in-place where data is understood as being bound and produced by a particular place, this paper describes how locally-situated accounts can be used to engage employees in workplace-specific training seminars. Using higher education as a case study, we describe a four-stage design process for future training efforts: (1) in-depth interviews for further understanding of bullying and harassment; (2) design of digital probes for capturing contextual data; (3) probe deployment and subsequent data analysis; (4) data-driven discussion-based seminars. We outline the potential for digital probes in promoting the denormalization of toxic workplace cultures, considerations for novel sensitive data governance models, and the discussion of data-in-place's temporal dimension.
Paraphrase identification using textual entailment recognition

Paraphrase Identification is a challenging and popular research area in Natural Language Processing Applications, including document summarization, information retrieval, question answering etc. In this paper, we focus on paraphrase identification using textual entailment recognition. In the proposed method Paraphrasing is seen as a bidirectional textual entailment. The predicate argument structures are used to represent the text and distant supervision technique for mining the indirect facts from these texts. The proposed method uses a probabilistic network for judging the confidence of each fact and a word sense disambiguation module is used to check the similarity between two sentences.
Coordinated Control Strategy of Scalable Superconducting Magnetic Energy Storage

Superconducting magnetic energy storage (SMES) has the characteristics of high power density and zero impedance that helps to develop renewable energy generation and micro-grid. A coordinated control for large capacity SMES application is proposed in this paper, which can improve power quality and system robustness effectively. Based on the topology and mathematical model of large capacity SMES, the coordinated control of outer power and inner current double closed loops control for power conditioning system is desired and vector switching automatic current sharing control method for multiphase chopper is developed. A low-pass filter and repetitive controller are included in the inner current loop controller to suppress periodic disturbance and increase robustness of the system. Simulation based on MATLAB/Simulink and experimental results demonstrate the effectiveness of large-capacity SMES coordinated control.
Adding Force Feedback to Mixed Reality Experiences and Games using Electrical Muscle Stimulation

We present a mobile system that enhances mixed reality experiences and games with force feedback by means of electrical muscle stimulation (EMS). The benefit of our approach is that it adds physical forces while keeping the users' hands free to interact unencumbered-not only with virtual objects, but also with physical objects, such as props and appliances. We demonstrate how this supports three classes of applications along the mixed-reality continuum: (1) entirely virtual objects, such as furniture with EMS friction when pushed or an EMS-based catapult game. (2) Virtual objects augmented via passive props with EMS-constraints, such as a light control panel made tangible by means of a physical cup or a balance-the-marble game with an actuated tray. (3) Augmented appliances with virtual behaviors, such as a physical thermostat dial with EMS-detents or an escape-room that repurposes lamps as levers with detents. We present a user-study in which participants rated the EMS-feedback as significantly more realistic than a no-EMS baseline.
Algorithmic and memory optimizations on multiple application mapping onto FPGAs

Field Programmable Gate Arrays (FPGAs) offer a low power flexible accelerator alternative due to their inherent parallelism. Reprogrammability, although its their key feature, it is used almost exclusively on design time due to the constrains imposed by the modern CAD tools that require even days to run and tens of GB of RAM. In order to effectively utilize FPGAs on run time we propose a novel methodology and the supporting toolflow that enable efficient mapping of multiple applications onto heterogeneous FPGAs. With the use of a floorplanning step, memory optimizations and custom memory allocators, we alleviate the constrains imposed by CAD tools, and provide a proof of concept that application mapping onto FPGAs can be done on run time. Experimental results prove the efficiency of the introduced solution, as we achieve application's mapping 40× faster on average compared to a state-of-art approach, without performance degradation and with 12× on average reduced memory usage.
The age of Rubisco: the evolution of oxygenic photosynthesis

The evolutionary history of oxygenesis is controversial. Form I of ribulose 1,5‐bisphosphate carboxylase/oxygenase (Rubisco) in oxygen‐tolerant organisms both enables them to carry out oxygenic extraction of carbon from air and enables the competitive process of photorespiration. Carbon isotopic evidence is presented from ~2.9 Ga stromatolites from Steep Rock, Ontario, Canada, ~2.9 Ga stromatolites from Mushandike, Zimbabwe, and ~2.7 Ga stromatolites in the Belingwe belt, Zimbabwe. The data imply that in all three localities the reef‐building autotrophs included organisms using Form I Rubisco. This inference, though not conclusive, is supported by other geochemical evidence that these stromatolites formed in oxic conditions. Collectively, the implication is that oxygenic photosynthesizers first appeared ~2.9 Ga ago, and were abundant 2.7–2.65 Ga ago.
Identifying signal source using channel state information in wireless LANs

In this paper, we study the feasibility of identifying signal source in wireless LANs using channel state information obtained from preambles. In current standard, the signal source can be identified by reading the MAC header which requires high SNR and takes more time than receiving the preamble. For each packet, CSI is obtained for each (group of) subcarriers and for each TX-RX path, which makes the information rich in features to be used for machine learning-based classification. Experiments in a typical office environment show that with simple kNN or neural network models, we can classify tens of signal sources with over 90% accuracy. Moreover, confidence levels differ significantly between correct and incorrect samples, which can be exploited to avoid false positives with little sacrifice of correct samples. The CSI-based source identification method can be used to improve spectral efficiency of WLANs, and can be used along with schemes such as BSS coloring included in the IEEE 802.11ax standard for highly efficiency WLANs.
Double-input high-gain bidirectional DC-DC converter for hybrid energy storage systems in DC-micro grid

Novel double input non-isolated high gain bidirectional DC-DC converter is presented for hybrid battery and super-capacitor energy storage system. Coupled inductor has been used for achieving high gain in single input converters, but it has been rarely used in multi-input bidirectional high gain converters. The proposed converter can achieve ZVS operation for all of the switches, with the combination of quasi resonant and active clamp techniques. The proposed converter does not utilize transformer for achieving high gain, hence the number of switches is decreased in comparison to its counterparts. Therefore, it has better reliability and it is more cost effective. Furthermore, proposed converter utilizes just four switches in each mode, hence the conduction loss is decreased considerably. Also unlike its isolated counterparts which use phase shift technique, it has excellent voltage matching. Accordingly, the circulating current that causes current stress and loss in isolated converters naturally does not exist. The switching strategy of the converter is presented and simulations for different modes of the converter is carried out to demonstrate the performance of converter in different modes. The simulation results and efficiency calculations show the effects of the proposed converter.
Gaussian material synthesis

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumination, which makes this process impracticable for real-world workflows. Our neural network eliminates this bottleneck by providing high-quality image predictions in real time, after which it is possible to pick the desired materials from a gallery and assign them to a scene in an intuitive manner. Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling. Similarly, expert users experience a significant decrease in the total modeling time when populating a scene with materials. Furthermore, our proposed solution also offers controllable recommendations and a novel latent space variant generation step to enable the real-time fine-tuning of materials without requiring any domain expertise.
Infrared wire-grid polarizer with sol-gel antireflection films on both sides

We fabricated an infrared wire-grid polarizer with the high transverse magnetic (TM) polarization transmittance and high extinction ratio by soft imprint lithography, sol-gel method, and Al shadow coating processes. A zilconia film was coated on Si substrate by using sol-gel method and spin coating method. Then, sol-gel zirconia grating was formed on the back side using imprinting using a silicone mold. The polarizer was produced by depositing Al obliquely on the grating. The TM transmittance of the fabricated element was greater than 80% at a wavelength of 4.8 μm. The sol-gel zilconia films acted as antireflection films. The extinction ratio exceeded 26 dB at its wavelength.
Effect of Follower Force on Vibration Frequency of Magneto-Strictive-Faced Sandwich Plate with CNTR Composite Core

This study deals with the vibration response of sandwich plate with nano-composite core and smart magneto-strictive face sheets. Composite core is reinforced by carbon nanotubes (CNTs) and its effective elastic properties are obtained by the rule of Mixture. Terfenol-D films are used as the face sheets of sandwich due to magneto-mechanical coupling in magneto-strictive material (MsM). In order to investigate the magnetization effect on the vibration characteristics of sandwich plate, a feedback control system is utilized. Also the sandwich plate undergoes the follower forces in opposite direction of x. Based on energy method, equations of motions are derived using Reddy’s third order shear deformation theory, and Hamilton’s principle and solved by differential quadrature method (DQM). A detailed numerical study is carried out based on third-order shear deformation theory to indicate the significant effect of follower forces, volume fraction of CNTs, temperature change, core-to-face sheet thickness ratio and controller effect of velocity feedback gain on dimensionless frequency of sandwich plate. These finding can be used to automotive industry, aerospace and building industries.
NN compactor: Minimizing memory and logic resources for small neural networks

Special neural accelerators are an appealing hardware platform for machine learning systems because they provide both high performance and energy efficiency. Although various neural accelerators have recently been introduced, they are difficult to adapt to embedded platforms because current neural accelerators require high memory capacity and bandwidth for the fast preparation of synaptic weights. Embedded platforms are often unable to meet these memory requirements because of their limited resources. In FPGA-based IoT (internet of things) systems, the problem becomes even worse since computation units generated from logic blocks cannot be fully utilized due to the small size of block memory. In order to overcome this problem, we propose a novel dual-track quantization technique to reduce synaptic weight width based on the magnitude of the value while minimizing accuracy loss. In this value-adaptive technique, large and small value weights are quantized differently. In this paper, we present a fully automatic framework called NN Compactor that generates a compact neural accelerator by minimizing the memory requirements of synaptic weights through dual-track quantization and minimizing the logic requirements of PUs with minimum recognition accuracy loss. For the three widely used datasets of MNIST, CNAE-9, and Forest, experimental results demonstrate that our compact neural accelerator achieves an average performance improvement of 6. 4χ over a baseline embedded system using minimal resources with minimal accuracy loss.