Miracle-dz/UniFAR-FTUs huggingface dataset:
Biogeography of rodents in Iran: species richness, elevational distribution and their environmental correlates

Abstract Rodent biogeographic studies are disproportionately scarce in Iran, however, they are an ideal system to understand drivers of biodiversity distributions in the country. The aims of the present research are to determine (i) the pattern of rodent richness across the country, (ii) quantify their elevational distribution patterns, and (iii) explore the underlying mechanisms. To reach these goals, an updated species list was compiled based on the latest taxonomic revisions, published until December 2021. We mapped all 76 rodent species distributions to develop the first map of rodent richness in Iran. We furthermore investigated their elevational distribution patterns in the following four geographic regions based on 100 m intervals; the Zagros Mountains, north of Alborz Mountains, south of Alborz and Kopet-Dagh mountains, and central and east mountains. North-east of Iran, Zagros Mountains, Alborz Mountains, and northwestern of Iran showed the highest richness and were identified as biodiversity hotspots of rodents in the country. This study highlights the importance of past climate change as the key driver of rodent richness in Iran. We showed that rodents’ elevational distribution patterns differ among geographic regions. Areas and elevational zones with the highest species richness should be prioritized for the conservation planning of rodents in Iran.
A 1.23W/mm2 83.7%-Efficiency 400MHz 6-Phase Fully Integrated Buck Converter in 28nm CMOS with On-Chip Capacitor Dynamic Re-Allocation for Inter-Inductor Current Balancing and Fast DVS of 75mV/ns

For more efficient power management in processors, a high-frequency and multi-phase (MP) integrated voltage regulator (IVR) using multiple inductors would be an ideal solution to deliver optimized power with rapid dynamic voltage scaling (DVS) [1]–[5]. Nevertheless, in practical use, MP-IVRs suffer from an inter-inductor current imbalance because of mismatches in integrated inductances, different parasitic resistances, and duty-control skews among all phases (top of Fig. 18.1.1). Such a current mismatch may cause larger output ripple and efficiency degradation, losing the benefits of multi-phase operation. Also, thermal hotspots can occur due to excessive current density and deteriorate reliability. Previous approaches [2], [4] use current sensors to calibrate the duty cycle of each phase, but the design complexity and power overhead tend to be greatly increased in the high-speed sensing circuitry for fast-switching converters. An additional consideration in MP-IVRs is the phase-shedding technique, which can improve the light-load efficiency by reducing the number of phases. In many prior works [1], [2], the number of phases was adjusted by 2N (e.g., 1-2-4) for simplicity of phase division. If the phase-shedding is more fine-grained, the overall efficiency can be more flattened over a wide range of loads. Moreover, because the frequency response and output ripple vary according to the number of phases, the MP-IVR should be adaptively optimized further for them. This paper presents a 400MHz 6-phase fully integrated buck converter (bottom of Fig. 18.1.1). Key contributions of this work include 1) inter-inductor true-average-current matching by the flying-capacitor-based peak-and-valley differential sensing (PVDS) technique with near-zero power overhead, 2) area-efficient dynamic re-allocation of on-chip capacitors used either in the PVDS or at the output for optimizing responsiveness and voltage ripple adaptively to the number of phases, and 3) DLL -based multi-phase clock generation (MPCG) for fine-grained phase-shedding functionality, improving efficiency over a wide load range.
3D V-Cache: the Implementation of a Hybrid-Bonded 64MB Stacked Cache for a 7nm x86-64 CPU

AMD's V-Cache is a 3D stacked product that attaches additional cache onto a high-performance processor through hybrid bonding, a technology that offers significant bandwidth and power benefits over state-of-the-art uBump based approaches. V-Cache expands Zen3's on-die L3 Cache from 32MB to 96MB, providing up to 2TB/s of bandwidth and 15% average gaming performance uplift. This paper describes the hybrid bonding technology components, provides insight into the V-Cache's architecture and design, discusses the associated DFT implications, and offers measured performance results.
The Uses of Brain-Computer Interface in Different Postures to Application in Real Life

The use of the BCI as a communication tool in real life may be different from the use in the laboratory environment. As an example of this difference between two environments, posture could be a consideration for the performance of BCI. In this study, six participants kept their postures sitting, standing, and walking in place and used the P300-based BCI. The stimuli were presented in augmented reality (AR) devices. Comparing the performance of BCI performed online, there was no significant difference in the three postures. Indeed, the area amplitude and latency of the P300 and N200 components seem to not change with posture. In conclusion, the P300-based BCI might not be affected by different postures. It is expected that this will not be a major problem in using P300-based BCI in real life regarding postures.
A 512Gb In-Memory-Computing 3D-NAND Flash Supporting Similar-Vector-Matching Operations on Edge-AI Devices

Similar-vector-matching (SVM) applications for unstructured vectors that are generated via machine-learning methods, such as face search and audio texturing from a dataset for access control systems, are frequently operated on edge devices, as depicted in Fig. 7.5. 1. The SVM operation [1]–[3] typically comprises of (1) in the offline phase, the extracted raw vectors $(\vec{V}_{RAW})$ are obtained from machine learning approaches and stored in non-volatile NAND Flash; (2) in the online phase, a processor request $\vec{V}_{RAW}$ data from edge storage; (3) the entire $\vec{V}_{RAW}$ dataset is moved from storage to the processor; (4) the processor scores the similarities between an input query and each candidate $\vec{V}_{RAW}$ and provide a best match. However, the large-amount data movement across the memory hierarchy consumes a large amount of energy $(\mathrm{E}_{\mathrm{M}\mathrm{E}\mathrm{M}})$, while also resulting in a long search-latency $(\mathrm{t}_{\mathrm{S}\mathrm{R}})$ for SVM operations. The entire $\vec{V}_{RAW}$ dataset includes a large amount of invalid data. To reducing data movement will lower $\mathrm{E}_{\mathrm{M}\mathrm{E}\mathrm{M}}$ and $\mathrm{t}_{\mathrm{S}\mathrm{R}}$; edge storage with nonvolatile computing-in-memory (nvCIM) support for similarity computation (vector-vector multiplication (VVM) for cosine similarity) is required to reduce the $\vec{V}_{RAW}$ dataset to a small candidate size. However, there are challenges in leveraging 3D NAND for VVM operations: (1) a low-readout accuracy when there is a large amount of current summation by using the wide range $\mathrm{V}_{\mathrm{t}}$ -level of cells ($\mathrm{e}.\mathrm{g}., 1^{\mathrm{s}\mathrm{t}}$ to $4^{\mathrm{t}\mathrm{h}}\mathrm{V}_{\mathrm{t}}$ -level of TLC cell) and (2) the large readout power consumption required to achieve a constant settling time against a wide range of summation currents for the possible data-patterns.
Automatic Power Factor Correction System Using IoT in University Building

Energy efficiency becomes more than a cost issue today. Using energy efficiently leads to a greater savings in resources and saving the environment for future. Increasing efficiency has significant impact on energy savings. A small saving at load level can create a major savings of resources at the power plant. The inductive loads like motors, welding, furnaces, ballasts and compressors needs reactive power(KVAR) to operate. The consumption of reactive power from the grid is possible by the increased consumption of apparent power(KVA) and increase in load current which in turn increase the losses. The power factor is an indication of how well the electricity is efficiently used. The utilities are charging for the inefficient usage and adding incentives for the efficient usage. So, there comes the need for the Power factor correction devices to improve overall electrical efficiency of the power system. As the load variations are unpredictable the Power factor correction has to be automated based on the load level. This project proposes an APFC (Automatic Power Factor Correction) at the load end embedded with the communication devices and sensors for the assurance of better power quality and reduced electricity consumption and ultimately, reduction in electricity bills. The objective of the project is to design a zero human intervention of energy surveillance in order to achieve the unity power factor strategy using the IoT (Internet of Things) technology.
Development of CUSUM Scheme for Monitoring Multiple Attributes

Most industrial applications like healthcare and service sectors include several independent attribute characteristics. These characteristics require effective monitoring tools and techniques. This paper proposes a Cumulative Sum (CUSUM) scheme for monitoring multiple attribute characteristics, titled MCUSUM chart. The proposed scheme is compared with the multi-attribute np (Mnp) chart. The developed MCUSUM chart overcomes the common weaknesses of the traditional CUSUM charts for detecting large shifts. As a result, the MCUSUM scheme substantially outperforms the Mnp scheme, in terms of the Average Number of Defectives (AND), for detecting a wide range of process shifts. The results reveal that the MCUSUM chart outdoes the Mnp chart by 268%, on average, under ten cases with different design specifications.
Survey of Skills Required by the Forensic Accountants: Evidence from a Developing Country

Accounting frauds and scams are perennial. They occurred in all eras and in all countries, and affected many organizations, regardless of their size, location, or industry. From Enron and WorldCom in 2001 to Madoff and Satyam in 2009, accounting frauds and scams have been dominating news items in the past decade. Corporations and regulatory bodies are trying their best to analyze and correct existing defects in their reporting system. After having an overview of the fraud scenario in India, it is apparent that criminals have become technology-savvy, and invented newer schemes to perpetuate crimes. In the current reporting environment of "digital-age," forensic accountants (FA’s) are in great demand for their "niche" accounting, auditing, legal and investigative skills. Hence, "forensic" accounting has been thrown in the "forefront of the crusade" against financial deception and accounting scandals.The present study investigates through a questionnaire, which was conducted in three leading States of the national capital region (NCR) of India during 2011-12, "if there are differences in the views of the relevant skills of FA’s among accounting practitioners, academics, and users of forensic accounting services. " From the statistical test of the hypotheses propounded for this study, we discovered that "core skills are not enough requirements for FA’s, there are significant differences in the relevant skills of FA’s, as given by previous researchers with the current research, and the necessary skills of FA’s, as identified by both academics and professionals, will hopefully meet employers’ expectations too. " Therefore, FA’s, being professional experts having "sixth" sense and possessing "special" skills are urgently required to counter all the ingenuity of these criminals. At present, some Universities in India are considering adding forensic accounting course to their curriculum. The results of this study may provide some guidance to educators for the development of forensic accounting curriculum by identifying the pertinent skills to accompany such a program of study.
DIANA: An End-to-End Energy-Efficient Digital and ANAlog Hybrid Neural Network SoC

Energy-efficient matrix-vector multiplications (MVMs) are key to bringing neural network (NN) inference to edge devices. This has led to a wide range of state-of-the-art MVM acceleration chips, which fall into two categories: 1) Digital NN accelerators [1]–[2], constituting widely parallel multiply-accumulate (MAC) arrays at medium (typically 4-8b) precision. 2) Analog in-memory compute (AiMC) NN accelerators [3]–[4], which enable much higher energy efficiencies and throughput per unit area at the cost of a reduced computational precision, reduced dataflow flexibility, and resulting reduced mapping efficiency for some layer configurations. Neither of these approaches dominates the other, as it depends on the layer type which approach is the optimal. The ideal processor would enable exploiting both digital and AiMC NN acceleration concepts and select the best accelerator depending on the layer characteristics. Consequently, this work presents DIANA, a low-power NN processing SoC, comprising a precision-scalable digital NN accelerator, an AiMC core, an optimized shared-memory subsystem and a RISC-V host processor to achieve SOTA end-to-end inference at the edge. This SoC includes innovations in: a) its 16x16 digital NN core with flexible dataflow for fully connected and high-precision CONV layer execution, b) its 1152x512 AiMC core with SIMD digital post-processing and support for output unrolling for improving array utilization, and c) a shared memory system supporting efficient layer-fused execution schedules, controlled by the RISC-V. This allows simultaneous execution of subsequent layers across the digital and analog cores, assigning high-precision layers and layers with limited AiMC utilization (e.g. FC layers and layers with low channel count) to the digital core, and all other intermediate layers to the AiMC core. A top-level overview of the designed system and its highlights is depicted in Fig. 15.6. 1.
A 256-Channel Actively-Multiplexed µECoG Implant with Column-Parallel Incremental $\Delta\Sigma$ ADCs Employing Bulk-DACs in 22-nm FDSOI Technology

Electrocorticography (ECoG) recording is a non-penetrating electrophysiology technique that achieves a good balance between spatial resolution, brain coverage and invasiveness [1]. For this reason, it is widely used for the diagnosis of neural disorders and holds promise for prosthetic applications. Although ECoG electrodes have been until recently quite large (~4mm diameter), several studies have shown that micro-scale electrodes (< 1mm diameter) are better suited for studying cortical pathology and implementing neural prostheses [2]. The design of flexible, high-density $\mu\mathsf{ECoG}$ arrays is challenging since every electrode must be individually addressed, resulting in a wiring bottleneck. Therefore, passive µECoG arrays have a limited electrode count and suffer from poor spatial coverage [3]. In contrast, active $\mu\mathsf{ECoG}$ arrays have emerged to provide higher spatial coverage with better spatial resolution. The most prominent active array [4] uses Si nanomembrane transistors to multiplex the electrodes and significantly reduce the signal routing. However, the source follower in this array severely limits its noise performance. Moreover, multiplexed $\mu\mathsf{ECoG}$ arrays impose additional design challenges on the readout IC (ROIC): i) compared to regular non-multiplexed ROICs [3], at least N times higher bandwidth (BW) is required (where N:1 is the multiplexing ratio); ii) the electrode DC offsets (EDOs) from N electrodes are modulated and cannot be removed with traditional AC coupling or DC servo loops (DSLs); and iii) sufficiently low noise is needed to compensate for the noise aliasing caused by the electrode multiplexing.
Developments Towards the Qualification of Two SHM Systems for S-SHM Application

In order to understand the various aspects of Structural Health Monitoring (SHM), over the years Embraer has investigated many different SHM technologies. Less complex and less time-consuming procedures – when compared to current NDT technologies – allowed by SHM can not only reduce the amount of time and burden of the inspection tasks, but also minimize the effects of “human-factors” when compared to current inspection procedures. Tests were performed in an E-Jets flight tests aircraft with two different SHM technologies which were considered as promising ones for monitoring structural components. Sensors, cables and connectors of Comparative Vacuum Monitoring (CVM) and Lamb Waves (LW) were installed as part of the developments for the qualification of the two systems. Aircraft scheduled maintenance can take advantage of these systems. Scheduled Structural Health Monitoring (SSHM) application type considers the installation of sensors and cables into the aircraft for periodic scheduled structural inspections, these inspections being performed onground with ground support equipment. Additionally to the installations in an E-Jets flight tests aircraft, Embraer developed Service Bulletins for the installation of the SSHM systems in different areas of an airline’s E-Jets aircraft. Over a fixed period of time, Embraer intends to collect data from the installed S-SHM systems at least four times, aiming to identify if they are operating properly in a normal operating environment. For this qualification program, laboratory tests will also be performed with CVM and LW using structural samples that represent the monitored areas of an aircraft. These lab tests will provide data for generating Probability of Detection (POD) curves for both CVM and LW systems. Embraer representatives will participate in all tests conducted. All the information will be compiled and analyzed by Embraer teams and a data package will be created to provide support for the verification of the SHM solutions as a first step in this qualification effort. doi: 10.12783/SHM2015/336
A 28nm 1Mb Time-Domain Computing-in-Memory 6T-SRAM Macro with a 6.6ns Latency, 1241GOPS and 37.01TOPS/W for 8b-MAC Operations for Edge-AI Devices

SRAM-based computing in memory (SRAM-CIM) is an attractive approach to improve the energy efficiency (EF) of edge-AI devices performing multiply-and-accumulate (MAC) operations. SRAM-CIM with a large memory capacity enhances EF by reducing data movement between system memory and compute functions. High-precision inputs (IN), weights (W) and outputs (OUT) are essential to deliver sufficient inference accuracy using SRAM-CIM. These devices must also enable a short compute latency $(\mathrm{t}_{\text{AC}})$ and a high multiply-accumulate throughput (TP) to achieve a fast system-level response time for an inference task. However, previous SRAM-CIMs using voltage-mode in-memory computing (VM-IMC) [1], [3]–[6] or time-domain near-memory computing (TD-NMC) [2] are unable to simultaneously achieve high EF, high readout accuracy, and a short $\mathrm{t}_{\text{AC}}$ for high-precision MAC operations; as increasing IN-W-OUT precision and/or the number of accumulations (ACCU) leads to (1) an exponential decrease in the signal margin that causes a readout accuracy degradation for VM-IMC schemes, and (2) an increased maximum MAC value (MACV) and memory capacity (increased parasitic load), which increases $\mathrm{t}_{\text{AC}}$ and the energy consumption for VM-IMC and TD-NMC schemes, as Fig. 11.8. 1 shows. This work presents a time-domain in-memory-computing SRAM-CI M structure: (1) It uses a time-domain incremental-accumulation (TDIA) scheme to enable MAC operations with a high ACCU and a consistently large signal-margin across MACVs. (2) It also uses a dynamic differential-reference time-to-digital converter (D2REF-TDC) that is based on a software-hardware co-design, which reduces read energy consumption. A 28nm 1 Mb SRAM-CIM macro fabricated using foundry-provided compact 6T-SRAM cells achieves MAC operations with 64 accumulations of an 8b input and an 8b weight and a near-full precision output (22b). The proposed macro also achieves the shortest reported $\mathrm{t}_{\text{AC}}$ and a $0.3\text{ns}/\mathrm{b} \ \mathrm{t}_{\text{AC}}$ per output-precision $(\mathrm{t}_{\mathrm{A}\mathrm{C}\mathrm{p}\text{OUT}})$ with a 1241.2GOPS TP and a high 37.01TOP/W EF for 8b-MAC operations.
Fluid-Structure Interaction study of a vibration-based intraocular pressure measurements tonometer

Intraocular Pressure (IOP) measurement is vital in managing glaucoma progression. Controlling and managing IOP values in patients with glaucoma is very important to avoid blindness. In general, there are two types of IOP measurement devices (tonometers); contact and not contact. In contact tonometers, a probe is used to flatten the cornea, while in non-contact tonometers, air puff is used to deform the cornea. In both methods, IOP measurements can only be performed in hospitals and clinics by high skilled clinicians. Acoustic tonometry is a new non-contact, vibration-based and innovative device to measure IOP based on the natural frequency of the patient’s eye. Many investigators have studied acoustic tonometry and found that the natural frequency is correlated with the IOP.In this work, we conducted a parametric numerical study to investigate the effect of IOP and mechanical properties on the natural frequency of the eye. Fluid-Structure Interaction (FSI) models of human eyes were developed by varying the IOP and mechanical properties. For each case, the corneal displacement was extracted and used to compute the natural frequency using the Fast-Fourier transform (FFT). The study found that vibration-based tonometers can be an excellent alternative tonometer to measure IOP.
Optimization of Gradient Boosting Model for Wine Quality Evaluation

In wine industry, evaluating wine quality and improving the prediction accuracy for quality are important. The aim of this study is to evaluate wine quality based on physical and chemical features. Gradient boosting model is applied on a dataset with both red wine and white wine samples to make predictions. An optimization of gradient boosting model is implemented by using a proposed tuning method for important parameters. Each parameter is tuned to search for its best value, and also multiple variables are adjusted at the same time to find out a combination of parameters that can make the accuracy of model the highest. By using the optimization method for gradient boosting model, the model can achieve a relatively high accuracy on predicting wine quality under the condition that no error is allowed.