Miracle-dz/UniFAR-FTUs huggingface dataset:
27.4 Physically Unclonable Function in 28nm FD801 Technology Achieving High Reliability for AEC-Q100 Grade 1 and 1SO26262 ASIL-B

Protection of information is of paramount importance in today's digital age. Physically Unclonable Functions (PUFs) are considered a secure method for security key generation because they generate responses that exist only during operation. A challenge regarding the use of PUFs is to achieve high reliability. Therefore, various schemes such as temporal majority voting [2], [3], [4], spatial majority voting [1], BCH [1], [3], and burn-in [3], are applied to improve the stability of the responses. While a recent paper proposed a method of oxide-break to achieve zero error [5], it is controversial if it is a real PUF since the response value (i.e. the status of the oxide-break) can be observed by reverse engineering. Automotive is an application area where reliability is particularly important, as failures may lead to critical accidents. To satisfy the reliability of AEC-Q100 Grade 1, functionality under −40-to-125°C in ambient temperature (Ta) must be guaranteed, even considering the aging effects on a chip. To satisfy IS026262 ASIL-B, the fault coverage must be over 90%. This paper shows a PUF satisfying both AEC-Q100 Grade 1 and IS026262 ASIL-B, where our testing temperatures cover −40-to-150°C in junction temperature (Tj) to compensate for the increased thermal heat within the SoC package.
Internet of Vehicles (IoV) enabled 5G D2D technology using proposed resource sharing Algorithm

Internet of Vehicles (IoV) importance has been arisen to reduce the current road accidents and provide smarter, greener, and safer Intelligent Transport System (ITS). The wide deployment of 5G cellular network, supported by device-to-device (D2D) technology, can afford the infrastructure to enhance IoV.This paper investigates the resource assignment performance issues and proposes a Maximum Power Allocation algorithm (MAX-PA) for both Vehicles to Infrastructure (V2I) and Vehicles to Vehicles (V2V); where multiple V2V links can share the same channel with a V2I link. Furthermore, the proposed algorithm is provided to guarantee reliability for V2V links while maximizing the V2I links ergodic capacity. Finally, the MAX-PA algorithm performance is evaluated and the simulation results are presented and compared with other existing algorithms to show the enhancement.
Improving TensorFlow's Memory Swapping by Prioritizing Earlier Tensors

The ever-increasing sizes of deep learning models and datasets used increase the need for memory, as insufficient memory may abort a training process. Adding to this problem, deep learning trainings tend to use GPUs over CPUs for better training speed, where in general a GPU has significantly less memory than its CPU counterpart. One solution is memory swapping, a way to free some memory by temporarily moving data to another memory pool, in this case moving data from a GPU memory to a CPU memory. However, since moving data takes time─the larger the data the more the time─performing memory swapping in a training can significantly increase the training duration. Therefore, an ideal memory swapping has to be selective on how much and which data to swap. TensorFlow, a machine learning framework, features memory swapping that, based on our analysis over its implementation, can be improved to reduce the increase on training duration it causes. The improvement is done is by prioritizing earlier tensors (tensor is the basic data unit in TensorFlow) which, because of a certain backpropagation property, allows the asynchronicity of program execution to increase, ultimately reducing training duration. Based on our experiments on Char-RNN models with various hyperparameters and datasets (of size 285 KB and 4.4 MB), the improvement reduces up to around 3% of training duration on certain cases.
The Difference of Visual Field Defect on Diabetic Retinopathy Patients Treated with Panretinal Laser Photocoagulation with 20-Milisecond and 100-Milisecond Duration

Introduction: Panretinal laser photocoagulation (PRP) is a standard treatment for severe nonproliferative and proliferative diabetic retinopathy. Twenty-milisecond duration PRP show same effectiveness with 100-ms standard PRP in inhibit neovascularization progression. This shorter pulse tend to minimize retinal neuronal defect and visual field defect. This study aim to analyze the difference of visual field defect in diabetic retinopathy (DR) patients treated with 20-ms PRP compared with 100-ms PRP in Moh. Hoesin Hospital Palembang. Methods: A clinical trial with single blinding on severe-very severe NPDR and early PDR eyes treated with PRP between June and August 2016. Forty eyes (25 patients) were randomized into two groups. Twenty eyes were treated with 20-ms PRP, and other 20 eyes treated with 100-ms PRP. Visual field defect was evaluated using Humphrey Field Analyzer 30-2 SITA Standard at baseline and 2 weeks follow-up. Result: Unpaired t-test showed significant difference in mean deviation (MD) after laser on NPDR eyes (p=0.042, p 0.05). In NPDR eyes, more MD improvement was found in 20-ms PRP group (0.79±0.93 dB) than in 100-ms group (-0.04±0.61 dB). In early PDR eyes, MD improvement was bigger (1.0±0.88 dB) in 20-ms PRP group than in 100-ms group (0.10±1.47 dB). There was no significant difference in pattern standard deviation (PSD) on both group at any DR grade (p=0.208; p=0.201; p>0.05). Conclusion: After 2 weeks, 20-ms PRP caused more improvement and lesser visual field defect (p=0.042, p<0.05) on NPDR eyes. There was no significant difference in PSD on both groups.
A Resource-Saving TCAM Structure Based on SRAM

Although ternary content-addressable memory (TCAM) has a faster search speed, its power consumption and scalability is significantly poor compared to SRAM, so many SRAM-based TCAM structures have been proposed. Existing SRAM-based TCAMs reduce the power consumption but consume a lot of SRAM resources. In this paper, a resource-saving SRAM-based TCAM (RS-TCAM) structure is proposed, which can effectively reduce the amount of the SRAM by means of encoding and compressing the data in the coding region. Experimental results based on the real routing table data and Xilinx virtex-7 FPGA show that RS-TCAM uses less 10.94% SRAM than the UE-TCAM.
15.5 A 28nm 64Kb 6T SRAM Computing-in-Memory Macro with 8b MAC Operation for AI Edge Chips

Advanced AI edge chips require multibit input (IN), weight (W), and output (OUT) for CNN multiply-and-accumulate (MAC) operations to achieve an inference accuracy that is sufficient for practical applications. Computing-in-memory (CIM) is an attractive approach to improve the energy efficiency $(\mathrm{EF}_{\mathrm{MAC}}]$ of MAC operations under a memory-wall constraint. Previous SRAM-CIM macros demonstrated a binary MAC [4], an in-array 8b W-merging with near-memory computing (NMC) using 6T SRAM cells (limited output precision) [5], a 7b1N-1 bW MAC using a 10T SRAM cell (large area) [3], an 4b1N-5bW MAC with a T8T SRAM cell [1], and 8b1N-1bW NMC with 8T SRAM (long MAC latency $(T_{\mathrm{AC}})$) [2]. However, previous works have not achieved high IN/W/OUT precision with fast $\mathrm{T}_{\mathrm{AC}}$ compact-area, high $\mathrm{EF}_{\mathrm{MAC}}$, and robust readout against process variation, due to (1) small sensing margin in word-wise multiple-bit MAC operations, (2) a tradeoff between read accuracy vs. area overhead under process variation, (3) limited $\mathrm{EF}_{\mathrm{MAC}}$ due to decoupling of software and hardware development.
Review on gender differences in non-muscle invasive bladder cancer

Differences in the epidemiology, diagnosis and outcomes according to gender in patients diagnosed with non-muscle invasive bladder cancer (NMIBC) has been widely reported. In this article we present gender-specific differences in NMIBC in terms of epidemiology, risk factors, first clinical presentation, management and clinical outcomes based on systematically review evidence of existing literature. A literature search of English-language publications that included an analysis of the association of gender differences in patients with NMIBC was performed using PubMed. Sixty-four studies were selected for analysis with consensus of all authors. The incidence and mortality for urothelial bladder cancer (UBC) are higher in men, whereas cancer specific mortality to incidence ratio is significantly lower for men than for women. This phenomenon could be partially explained by differences in exposure to bladder cancer carcinogens. However female gender is associated with higher stage at presentation. Thirteen studies with a total of 11,069 patients diagnosed with NMIBC were included for analysis according to outcomes. In studies that found statistically significant differences in outcomes between sexes, female gender was reported as risk factor for disease recurrence, progression or cancer specific mortality. None of included studies found worse outcomes in men when compared to women with NMIBC. Results of our review suggest that female gender in patients diagnosed with NMIBC is associated-though inconsistently-with higher stage at presentation and poorer outcomes. Numerous factors may influence gender gap in incidence rate, clinical management and reported outcomes. Consensus on comparable data collection in routine practice and prospective trials including clinical outcomes are required to identify gender-specific differences in patients diagnosed with NMIBC.
Evolutionary patterns within the Anthozoa (phylum Cindaria) reflected in ribosomal gene sequences

Abstract : This project assessed phylogenetic relationships within the Class Anthozon (Cnidaria) using 185 rDNA sequences from 71 taxa, including museum specimens, providing the most thorough taxonomic sampling to date. Morphological taxonomists disagree over the evolutionary relationships within the Anthozoa. Sequence information and relevant evolutionary models were used to address subclass- and ordinal-level divisions within the Anthozoa. Nuclear 18S sequence information did not support some of the traditional classifications within the Anthozon. Members of the Subclass Ceriantipatharia were genetically disparate, and phylogenetic divisions within the Anthozoa resembled the previous two-subclass system. The orders within the Hexacorallia constituted monophyletic groups with the exception of the Corallimorpharia, which were closely associated with the Scleractinia. The morphological characters of mesentery and tentacle structure were reflected in genetic structure of the Anthozoa. The morphological characters of skeletal structure and colony morphology were not evident in the phylogenetic divisions of the Octocorallia. The current orders and suborders were not monophyletic entities. Colony dimorphism corresponded loosely with genetic divisions within the Octocorallia. Half of the octocoral species included in this study formed a large, undifferentiated group, suggesting that a rapid radiation occurred. This project provides valuable insight into the evolution and diversity of these important organisms.
A Novel Method for Real-Time Aircraft Detection in Airports

This paper presents a SSD (Single Shot multibox Detector) based aircraft detection method for airport video surveillance. The original SSD is not fast and accurate enough to meet the requirements of such real-time applications as airport surveillance. We introduce ResNet50 into SSD by using ResNet50 instead of the feature selection step in SSD framework. The proposed method and the original SSD are evaluated and compared based on a self-made aircraft database, which includes various cases as rainy, hazy weather, small targets, etc. Compared with original SSD, the proposed method is much faster, and the average accuracy is increased by 3%. Additional experiments based on public Pascal VOC database also indicate that the proposed method is better than the original SSD.
Preoperative Prediction of Prosthetic Size in Total Knee Arthroplasty Based on Multimodal Data and Deep Learning

Total knee arthroplasty (TKA) is an effective treatment method for severe knee osteoarthritis and other knee-related diseases. Accurate match of prostheses is a crucial factor to improve the clinical efficacy and patients’ postoperative satisfaction in TKA, to which no enough attention is paid currently. In this paper, we introduce deep learning algorithm to analyze the patients’ multimodal data, such as preoperative radiograph of knees and relevant physical features (e.g. sex, height, weight), and design a software system for preoperative prediction of prosthetic type in TKA. The main processing steps include the pre-processing of X-ray images and the prediction of prosthetic type based on convolutional neural network. Research on loss function and model structure is implemented to fit the dataset better for further improvement of prediction accuracy. Transfer learning method is employed to address the problem of inadequate data. The experimental results shows that our prediction system can achieve the same accuracy level compared with that of traditional methods manipulated by experienced doctors, while it can complete the preoperative prediction automatically with lower cost and better stability.
Photophoresis-based laser manipulation of airborne particles using structured laser beams

We investigate laser trapping of airborne light-absorbing particles via photophoretic forces. It is well known that the use of photophoretic forces allows one to guide light-absorbing nano- and microparticles at distances of up to tens of centimeters. We use various types of structured laser beams (optical "bottle" beams and line-shaped optical traps) for manipulation of agglomerations of carbon nanoparticles. The possibility to trap and guide both single light-absorbing particles and arrays of light-absorbing particles is demonstrated. In addition, the possibility of power-dependent particle guiding is shown.
Speckle-contrast imaging of pathological tissue microhemodynamics in the development of various diabetes models

The study of blood microcirculation is one of the most important problems of the medicine. This is caused by the fact that many diseases, such as cardio-vascular diseases, atherosclerosis, diabetes, chronic venous insufficiency, oncology diseases, cause functional and morphological changes of microcirculation of blood flow. The results of experimental study of changes of blood microcirculation of pancreas in rats with diabetes measured by using Laser Speckle Contrast Imaging (LSCI) at application of optical clearing agents are presented. Laser speckle contrast techniques are based on the spatial and temporal statistics of the speckle pattern, calculating of contrast of time-averaged dynamic speckles in dependence on the exposure time at the registration of the speckle-modulated images. In research, 38 Wistar rats weighing 300-500 g were used. Alloxan induced animal model and streptozotocin model of diabetes was explored. The influence of solution of glycerol, Omnipaque®-300 was investigated. Application of 70%-aqueous glycerol solution demonstrates 50%-decrease of blood flow velocity in the group of aloxan diabetic animals, to 10th min blood flow velocity was completely restored. In a group of animals with streptozotocin diabetes, the blood flow rate decreased by 54%, by 10 minutes the speed did not reach the initial value. Blood flow in the control group almost stopped, to 10 min has not recovered. Application of Omnipaque®-300 demonstrates 65%-increase of blood flow in the group of alloxan diabetic animals and an increase of bloode flow in the first 2 minutes of applying the solution by 45%. In both cases to 10th min, blood flow velocity was completely restored. Blood flow in the control group did not show any noticeable changes.
A new method for separate reconstruction of fluorophore absorption and fluorescence lifetime in time domain: first results of a numerical experiment

The paper is devoted to a new method of time-domain lifetime fluorescence molecular tomography. It is based on the use of early arriving diffuse photons and original measurement data, which are determined for the individual time gates of the diffuse temporal responses for fluorescence and exiting radiation. The paper presents in short the theoretical foundation of the method and describes a numerical experiment on the separate reconstruction of fluorophore absorption coefficient and fluorescence lifetime for 3D reflectance geometry that is suitable for small animal imaging. We show our first reconstruction results for the 3D scattering object region 11×11×8 mm3 in size with two spherical fluorescent inclusions 1 mm in diameter. The ways how to improve these results in the future are discussed.
Disminución de la actividad bactericida del ciprofloxacino en mutantes de Salmonella generados tras exposición repetida a diversas fluoroquinolonas

SUMMARY Salmonella mutants with reduced fluoroquinolone susceptibility were generated in vitro following repeated exposure to subinhibitory concentrations of the drugs and the alterations in the gyrA gene were characterized. Afterwards, the change in bactericidal activity exhibited by ciprofloxacin against the resulting mutants was determined. A decrease in the bactericidal activity of ciprofloxacin was found in all the mutants, but was more pronounced in mutants of nalidixic acid-resistant strains. This may help to explain the therapeutic failures sometimes described when fluoroquinolones are used in the treatment of these strains. In addition, our model evaluates the first interactions produced between the microorganism and the antibiotics, and demonstrates that the loss of bactericidal activity of ciprofloxacin occurs following exposure to all of the fluoroquinolones tested.
Attributing Increases in Fire Weather to Anthropogenic Climate Change Over France

Anthropogenic climate change is widely thought to have enhanced fire danger across parts of the world, including Mediterranean regions through increased evaporative demand and diminished precipitation during the fire season. Previous efforts have detected increases in fire danger across parts of southern Europe but a formal attribution of the role of anthropogenic climate forcing has not been undertaken. Here, we attempt to disentangle the confounding effects of anthropogenic climate change and natural variability on observed increases in fire danger in France over the past six decades, with a focus on the fire-prone Mediterranean region. Daily fire weather and fire-related drought indices were computed from a reanalyses dataset covering the 1958–2017 period. Anthropogenic signals in meteorological variables were isolated using 17 climate models and then removed from observations to form a set of counterfactual observations free of anthropogenic climate change. Our results show that anthropogenic climate change is responsible for nearly half of the long-term increases in fire weather and fire-related drought conditions across the Mediterranean region and have significantly elevated the likelihood of summers with extreme fire danger. Fire danger conditions such as those observed during the near-record breaking 2003 fire season have a <0.2% annual probability (return interval >500 years) of occurrence in the absence of anthropogenic climate change, compared to a probability of ~10% (return interval ~10 years) under today's climate accounting for anthropogenic climate change. Our approach provides modernized estimates of current fire danger levels and expected return levels of extreme fire seasons considering climate change, which may help inform fire management agencies and decision making.
Quantitative Modeling Extends the Antibacterial Activity of Nitric Oxide

Numerous materials have been developed to try and harness the antimicrobial properties of nitric oxide (NO). However, the short half-life and reactivity of NO have made precise, tunable delivery difficult. As such, conventional methodologies have generally relied on donors that spontaneously release NO at different rates, and delivery profiles have largely been constrained to decaying dynamics. In recent years, the possibility of finely controlling NO release, for instance with light, has become achievable and this raises the question of how delivery dynamics influence therapeutic potential. Here we investigated this relationship using Escherichia coli as a model organism and an approach that incorporated both experimentation and mathematical modeling. We found that the best performing delivery mode was dependent on the NO payload, and developed a mathematical model to quantitatively dissect those observations. Those analyses suggested that the duration of respiratory inhibition was a major determinant of NO-induced growth inhibition. Inspired by this, we constructed a delivery schedule that leveraged that insight to extend the antimicrobial activity of NO far beyond what was achievable by traditional delivery dynamics. Collectively, these data and analyses suggest that the delivery dynamics of NO have a considerable impact on its ability to achieve and maintain bacteriostasis.
Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development

Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.
Improving Dialogue Smoothing with A-priori State Pruning

: When Dialogue Systems (DS) face real usage, a challenge to solve is managing unforeseen situations without breaking the coherence of the dialogue. One way to achieve this is by redirecting the interaction to known dialogue states in a transparent way. This work proposes a simple a-priori pruning method to rule out in-valid candidates when searching for similar dialogue states in unexpected scenarios. The proposed method is evaluated on a User Model (UM) based on Attributed Probabilistic Finite State Bi-Automata (A-PFSBA), trained on the Dialogue State Tracking Challenge 2 (DSTC2) corpus. Results show that the proposed technique improves response times and achieves higher F1 scores than previous A-PFSBA implementations and deep learning models.