Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of High-Throughput Scalable Radix-4 FFT Processor Architecture Using Fixed-Point Arithmetic

In this paper, we propose a scalable and high-throughput pipeline FFT processor architecture and evaluate its variations. To achieve high-throughput with reasonable operating frequency, the proposed architecture utilizes radix-4, where four points are processed every clock cycle. Like IP core generators, our architecture can be reconfigured by changing the number of FFT stages to support various numbers of FFT points. Our architecture is based on fixed-point arithmetic to relieve the complexity but might be extended to support floating-point implementation to keep high dynamic ranges. The proposed architecture achieves four times the throughput of the operating frequency. For example, 492Msamples/sec of throughput can be achieved when the operating frequency is 123 MHz, which may be a reasonable performance for 5G OFDM implementation. In this case, the gate count of our 4K-point FFT is 443,419, excluding SRAMs for pipeline buffers.
A 12-to-15 b, 100-to-25 kS/s Resolution Reconfigurable, Power Scalable Incremental ADC Using Ultrathin Chips

Resolution reconfigurable Nyquist-rate analog-to-digital converters (ADCs) are able to deliver an optimized end-to-end power efficiency when a single ADC is time-multiplexed between different sensors. In this letter, a 12-to-15b resolution reconfigurable power scalable Nyquist-rate incremental <inline-formula><tex-math notation="LaTeX">$\mathrm{\Delta \Sigma }$</tex-math></inline-formula> (I-<inline-formula><tex-math notation="LaTeX">$\mathrm{\Delta \Sigma }$</tex-math></inline-formula>) ADC, operating at sampling rates from 100 to 25 kS/s, is proposed. A widely reconfigurable dynamic range (DR) (73–92 dB) with fine selection granularity is achieved by incorporating a scalable first integrator and optimally setting the oversampling ratio (OSR) of the <inline-formula><tex-math notation="LaTeX">$\mathrm{\Delta \Sigma }$</tex-math></inline-formula> modulator. A 2.5× power reduction is measured for the proposed technique in comparison to a pure OSR-based resolution scaling. A Walden figure-of-merit (FoM <inline-formula><tex-math notation="LaTeX">$_{\rm W}$</tex-math></inline-formula>) of 880 <inline-formula><tex-math notation="LaTeX">$\,$</tex-math></inline-formula> fJ/conv. -step and Schreier (FoM <inline-formula><tex-math notation="LaTeX">$_{\rm S}$</tex-math></inline-formula>) of 160.3 dB are measured in the 14b mode. The I-<inline-formula><tex-math notation="LaTeX">$\mathrm{\Delta \Sigma }$</tex-math></inline-formula> ADC is fabricated in a 180-nm CMOS technology and consumes 0.48 and 0.93 mW from a supply voltage of 3 V at the 12b and 15b modes, respectively. By exploiting the back-thinning of the ADC chips, the presented 30-<inline-formula><tex-math notation="LaTeX">${\mu }$</tex-math></inline-formula>m-thick ADC becomes readily available for integration into hybrid flexible sensor systems.
EFSSD: An Enhanced Fusion SSD with Feature Fusion and Visual Object Association Method*

The single-shot multibox detector (SSD) is a landmark work in object detection field that achieves excellent performance in target detection. In this paper, an accurate single-stage object detector, namely enhanced fusion SSD (EFSSD) is proposed for the object detection problems. The residual network is introduced to extract deeper feature information. Moreover, we use the feature fusion method to fuse feature maps of different layers. To further detect small objects, a visual object association method is developed, which makes full use of relationships between targets instead of the feature of the target itself. To evaluate the proposed EFSSD, it was trained on PASCAL VOC2007 and PASCAL VOC2012 training datasets and tested on the PASCAL VOC2007 test dataset. For 300 × 300 input, EFSSD achieves 80.1 % and 53 frames per second (FPS) and EFSSD achieves 83.7% and 17.1 FPS for 512 × 512 input. The above experimental performances show that EFSSD achieves higher accuracy comparing with SSD and other advanced detectors.
Harmonic Preserving Neural Networks for Efficient and Robust Multipitch Estimation

Multi-pitch estimation (MPE) is a fundamental yet challenging task in audio processing. Recent MPE techniques based on deep learning have shown improved performance but are computation-hungry and relatively sensitive to the variation of data such as noise contamination, cross-domain data, etc. In this paper, we present the harmonic preserving neural network (HPNN), a model that incorporates deep learning and domain knowledge in signal processing to improve the efficiency and robustness of MPE. The proposed method starts from the multilayered cepstrum (MLC), a feature representation that utilizes repeated Fourier transform and nonlinear scaling to suppress the non-periodic components in signals. Following the combined frequency and periodicity (CFP) principle, the last two layers of the MLC are integrated to suppress the harmonics of pitches in the spectrum and enhance the components of true fundamental frequencies. A convolutional neural network (CNN) is then placed to further optimize the pitch activation. The whole system is constructed as an end-to-end learning scheme. Improved time efficiency and performance robustness to noise and cross-domain data are demonstrated with experiments on polyphonic music in various noise levels and multi-talker speech.
A Novel Approach for Developing Emergency Evacuation Plans

In case of a disaster, planning for pedestrian evacuation from buildings is a major issue since it threatens human lives. To cope with this problem, evacuation plans are developed to ensure efficient evacuation in minimum time. These plans can be very sophisticated according to the complexity of the evacuation environment. This advocates the use of architectures such as Multi-Agent Systems (MAS) to develop the evacuation plans before happening of a real accident. Since developing an evacuation plan using MAS requires considerable effort, finding more efficient approaches is still an open problem. This paper introduces a new approach, based on the model-driven principles, to support developing evacuation plans. The approach includes utilizing a graphical editor for designing evacuation models, automatic generation of the evacuation plan code, as well as running the generated code on a MAS platform. We evaluated our approach using a case study. The results show that our approach provides elevated speed, less effort, high abstraction level, and more flexibility and productivity in developing emergency evacuation plans.
Gradual Deep Residual Network for Remote Sensing Images Fusion

Remote sensing image fusion (also known as pansharpening) aims to improve the spatial resolution of multispectral (MS) images using high frequency details extracted from Panchromatic (PAN) images. Recently, residual learning (ResNet) exhibits improved performance in many application domains. At the same time, numerous upsampling methods were developed, from the classical interpolation to deep learning based methods. In this paper, the Gradual Deep Residual Network (GDRN) is developed. The principle key of GDRN is that: instead of using one step upsampling layer, we progressively upscale and fuse MS and PAN images at two pyramid levels where each of them consists of a residual block and one step upsampling layer with a scale factor of 2. Experimental results of the proposed approach show better performance than classical methods and competitive performance against the state-of-the-art approaches
AWAF: AI Enabled Web Contents Authoring Framework

Artificial intelligence (AI) in web development is a new sector that a lot of people are into recently. AI continues to evolve and grow, and plays an increasingly important role in the web app development space [1]. When it comes to developing innovative and more sophisticated web applications, the involved technologies continue to play a bigger role. With the involvement of the internet into our daily lives, particularly businesses are enjoying the aspects of AI. Precisely, companies use AI in proper marketing of their products and enhancing their brand visibility by building their websites and web applications [2]. AI or Machine Learning (ML) models are able to help web app developers to solve problems related to security, user experience, content analysis, quality assurance and much more. This presents the need for a framework or tool that can allow third party developers to seamlessly build an AI based app. In this paper, we present an AI Enabled Web Contents Authoring Framework (AWAF), where AI models can be simply dragged into workspace, provide options to build, train Deep learning models using a simple web visual interface, and ultimately ship the AI features into the web application. Also, we provide an option to connect together smart blocks called AI Nodes, to create our custom deep learning models. These AI Nodes are designed flexible enough to reap the advantages of portability and reusability. And, laterally, we also focus on assigning or distributing the computational AI Nodes to capable IOT-edge devices like high-end TV etc. to leverage their hardware capabilities in order to increase the overall responsiveness of the AI application on low-end devices [3].
Nondestructive Sensing of Water Activity From Measurement of the Dielectric Properties at Microwave Frequencies

A rapid, nondestructive method for determining water activity of granular and particulate materials from measurement of the dielectric properties at a single microwave frequency is presented. Results are shown for almond kernels at 8 GHz and 25 °C. Situations in which bulk density is known and unknown were considered. For situations, when bulk density is known, direct correlations between the dielectric properties and water activity were identified. When the bulk density is unknown, correlations between the loss tangent (tan δ) and a density-independent calibration function (ψ), expressed in terms of the dielectric properties and water activity, were used. For purposes of illustration, analytical expressions of the water activity are derived for each situation for almond kernels for which the dielectric properties were measured at 8 GHz and 25 °C. In both situations, the dielectric constant, loss factor, loss tangent, and ψ increased exponentially with water activity. The standard error of calibration (SEC) was calculated to evaluate the effectiveness of each of these analytical expressions in predicting water activity from dielectric properties measurement. The SEC ranged from 0.02 to 0.04 for almond kernels with water activity ranging from 0.5 to 0.9 and moisture content ranging from 4.8 to 16.5%.
DENDRITIC POLYGLUTAMIC PORPHYRINS : PROBING PORPHYRIN PROTECTION BY OXYGEN-DEPENDENT QUENCHING OF PHOSPHORESCENCE

Phosphorescence quenching of the porphyrin core by O2 has been studied in a series of dendritic polyglutamic porphyrins. As the generation number increases, in water the dendritic ligands make it increasingly difficult for oxygen to reach the Pd–porphyrin core (figure); they provide no protection in DMF, since there the structure of the dendrimer is more open.
A Gamification Approach to Getting Students Engaged in Academic Study

—Serious games and gamification are emerging in many areas of society. In addition to an effective way of entertainment, games can be adopted as a persuading tool to guide person to achieve specific goal, citing their ultimate attraction. In this paper, we are interested in the mechanism and application of gamification in the field of academic education. Games feature a potential to promote students to learn in a spontaneous and effective manner, since the challenge and fun in games encourage students to hold on to the end. We propose a framework for gamification in education by integrating the procedures for designing academic courses and the game elements. A case study in digital circuit course, which is filled with diagrams, equations and is hence complicated and some kind of boring for some students, is conducted to illustrate the proposed approach in academic education.
Geographical CO 2 sensitivity of phytoplankton correlates with ocean buffer 1 capacity 2 3 Running head: Phytoplankton response to ocean buffer capacity 4

A range of biological impacts resulting from this oceanic CO 2 accumulation are 34 emerging, however the mechanisms responsible for observed differential 35 susceptibility between organisms and across environmental settings remain obscure. A 36 primary consequence of increased oceanic CO 2 uptake is a decrease in the carbonate 37 system buffer capacity, which characterises the system’s chemical resilience to 38 changes in CO 2 , generating the potential for enhanced variability in p CO 2 and the 39 concentration of carbonate [CO 32- ], bicarbonate [HCO 3- ] and protons [H + ] in the 40 future ocean. We conducted a meta-analysis of 17 shipboard manipulation 41 experiments performed across three distinct geographical regions that encompassed a 42 wide range of environmental conditions from European temperate seas to Arctic and 43 Southern oceans. These data demonstrated a correlation between the magnitude of 44 natural phytoplankton community biological responses to short-term CO 2 changes and 45 variability in the local buffer capacity across ocean basin scales. Specifically, short- 46 term suppression of small phytoplankton (<10 µm) net growth rates were consistently 47 observed under enhanced p CO 2 within experiments performed in regions with higher 48 ambient buffer capacity. The results further highlight the relevance of phytoplankton 49 cell size for the impacts of enhanced p CO 2 in both the modern and future ocean. 50 Specifically, cell-size related acclimation and adaptation to regional environmental 51 variability, as characterised by buffer capacity, likely influences interactions between 52 primary producers and carbonate chemistry over a range of spatio-temporal scales. 53
Collective dynamics in entangled worm and robot blobs

Significance Living organisms form collectives across all scales, enabling biological functions not accessible by individuals alone. In a few cases, the individuals are physically connected to each other, forming an additional class of entangled active matter systems with emergent mechanofunctionalities of the collective. Here, we describe the dynamics of macroscopic aquatic worms that braid their long, soft bodies to form large entangled worm blobs. We discover that the worm blob behaves as a living material to undergo dynamic shape transformations to reduce evaporation or break-symmetry and locomote to safety against thermal stresses. We validate our biological hypotheses in robophysical swarming blobs, which pave the way for additional classes of mechanofunctional active matter systems and collective emergent robotics. Living systems at all scales aggregate in large numbers for a variety of functions including mating, predation, and survival. The majority of such systems consist of unconnected individuals that collectively flock, school, or swarm. However, some aggregations involve physically entangled individuals, which can confer emergent mechanofunctional material properties to the collective. Here, we study in laboratory experiments and rationalize in theoretical and robophysical models the dynamics of physically entangled and motile self-assemblies of 1-cm-long California blackworms (Lumbriculus variegatus, Annelida: Clitellata: Lumbriculidae). Thousands of individual worms form braids with their long, slender, and flexible bodies to make a three-dimensional, soft, and shape-shifting “blob. ” The blob behaves as a living material capable of mitigating damage and assault from environmental stresses through dynamic shape transformations, including minimizing surface area for survival against desiccation and enabling transport (negative thermotaxis) from hazardous environments (like heat). We specifically focus on the locomotion of the blob to understand how an amorphous entangled ball of worms can break symmetry to move across a substrate. We hypothesize that the collective blob displays rudimentary differentiation of function across itself, which when combined with entanglement dynamics facilitates directed persistent blob locomotion. To test this, we develop a robophysical model of the worm blobs, which displays emergent locomotion in the collective without sophisticated control or programming of any individual robot. The emergent dynamics of the living functional blob and robophysical model can inform the design of additional classes of adaptive mechanofunctional living materials and emergent robotics.
Cumulative Cardiovascular Disease Risk and Triglycerides Differentially Relate to Subdomains of Executive Function in Bipolar Disorder. Running title: CVD risk factors and executive function in BD

Objectives: Cardiovascular disease is disproportionally prevalent in bipolar disorder (BD) and has been linked to cognition in preliminary studies. Herein we evaluate the association between known risk factors for cardiovascular disease and executive function in BD patients compared to healthy controls. Methods: In a sample of n=57 individuals (n=23 BD, n=34 controls) we assessed two subdomains of executive function; cognitive flexibility (using the Trail Making Test Part B) and cognitive inhibition (using the Stroop Colour Word Interference Task). Cardiovascular risk was assessed by means of serum triglyceride levels, body mass index (BMI) and waist circumference, as well as dietary saturated fat intake and a sex-specific cumulative cardiovascular risk score calculated using the Framingham Heart Study method. Results: Patients with BD had higher BMI and waist circumference, with more BD patients categorized as having central obesity than controls. In the BD group only, higher triglyceride levels were associated with worse cognitive flexibility, and elevated cumulative cardiovascular disease risk was associated with worse cognitive inhibition. No correlations between cardiovascular risk factors and executive function were evident in the control group. Limitations: The study was limited by the small sample size and should be considered hypothesis-generating Conclusions: The associations between triglyceride levels, cumulative cardiovascular disease risk and executive functioning evident in BD in this study preliminarily indicate the potential for mechanistic overlap of physical health and cognitive function in the disorder.
Enabling power measurement and control on Astra: The first petascale Arm supercomputer

Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large‐scale HPC environments. In addition to ARM, several other important first‐of‐a‐kind developments were used in the machine, including new approaches to cooling the datacenter and machine. This article documents our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large‐scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on‐going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization.
Discrimination of Imbalanced Seismic Events between Earthquakes and Artificial Explosions using Support Vector Machine

The discrimination between earthquake and artificial explosions is an important issue in seismology. Unlike an earthquake, the artificial explosion is caused by a quarry blast or a nuclear test. The surface or receiver response to earthquake and artificial explosion is different. However, the discrimination of earthquake from other impulsive explosion is not easy especially in seismically noisy environment. The discrimination accuracy is limited by the imbalanced data between earthquake and artificial explosion. This paper proposes a seismic discrimination method using a support vector machine (SVM) with the P/S amplitude ratio as a feature vector. Furthermore, to enhance the performance of discrimination using SVM, the adaptive synthetic (ADASYN) sampling algorithm is applied to reduce the error caused by imbalanced data. In the learning and test process, different kernel functions are applied on SVM to improve the performance of discrimination between earthquake and artificial explosion. The accuracy and outcome of the proposed method using ADASYN-based SVM are evaluated by the receiver operating characteristic curve and the area under the curve. The simulation results show that the proposed method leads to more precise analysis and classification for seismic event discrimination. Keywords—support vector machine; ADASYN; imbalanced data; artificial explosion; seismic discrimination
KABLOSUZ DUYARGA AĞLARINDA İKİ TEMEL ARAŞTIRMA ALANI: SAAT EŞZAMANLAMASI VE TOPOLOJİ KONTROLÜ

Tiny sensor devices with limited energy and resource and sensor networks that appeared by the revolution of wireless communication is an important distributed application area with different requirements and properties. Clock synchronization and topology control for wireless sensor networks are two research areas on which there is currently active work. These problems which were well studied for classical distributed systems came out again with different parameters and requirements for wireless sensor network domain. In this paper, we give information and the basic solutions for clock synchronization and topology control that are currently active research problems for wireless sensor networks.
Session 17 overview: Embedded memory and DRAM I/O: Memory subcommittee

Demand for smaller and lighter personal devices with increasing functionality in the cloud drives advancements in both embedded memory technology and high-speed DRAM interfaces. Lower power through voltage reduction and increased performance through higher memory density and bandwidth are key enablers. This year's conference highlights 14nm FinFET technologies with the smallest bit cells achieved to date for both SRAM — at 0. 05μm2 — and embedded DRAM — at 0. 01747μm2. A new assist technique to drive V MIN reduction and novel bit cells for both 2-port SRAM and TCAM applications are also presented. In addition, two area-efficient techniques to boost DRAM bandwidth are reported.