Miracle-dz/UniFAR-FTUs huggingface dataset:
The Case for Domain-Specific Networks

Modern parallel computers are dichotomized into capacity or capability systems. Capacity systems cater to a wide range of weak scaling workloads, using distributed parallel systems with message passing while capability systems focus on strong scaling workloads across a significant fraction of the machine’s processing units. The interconnection network differs under these regimes, with commodity Ethernet or Infiniband solutions typically deployed for capacity systems, while capability-class systems often necessitate tightly-coupled, fine-grained communication. Systems built for AI training and inference embody traits from both classes: tight coupling and strong scaling for model parallelism, and weak scaling for data parallelism in a distributed system. Handling 100-billion-parameter large-language models and trillion-token data sets presents computational challenges for current supercomputing infrastructure. This paper discusses the crucial role of the interconnection network in these large-scale systems, advocating for flexible, low-latency interconnects that can deliver high throughput at large scales with tens of thousands of endpoints. This work also emphasizes the importance of reliability and resilience in enduring long-running training workloads and demanding inference requirements of domain-specific workloads.
GAGAT: Global Aware Graph Attention Network for 3D Classification and Segmentation

Graph Neural Networks have brought many breakthroughs in graph representation learning and boosted the state of the art in many data processing domains including point cloud learning. Graph structures provide the relative neighborhood information to unordered point clouds and help the exploration of topological structures for the understanding of complex scenes. Therefore, in this paper, we propose novel neural network architectures leveraging graph attention for point cloud-based classification and segmentation. The proposed architectures construct a connected graph from point cloud scans and employ a global aware attention module using global, local, and self-feature information for point cloud segmentation and classification. Various experiments on different datasets (ModelNet40 [1], ShapeNet [2], S3DIC [3], and Semantic3D [4]) show that our methods yield comparable results for classification, part segmentation, and semantic segmentation.
Non-Parametric InSAR Time Series Analysis of Arcs Using Complex B-Splines

Parametric models are widely utilized for interferometric synthetic aperture radar (InSAR) time series analysis under the assumption that the parameterization is invariant over time. Yet, the complexity of InSAR scatterers makes the hypothesis of this consistent and uniform behavior less likely. Here, we propose a method for non-parametric time series analysis for arcs between point scatterers based on basic splines (B-splines), which has the potential of fitting the time series adequately due to its high flexibility. We implement B-spline modelling for the kinematic behavior of arcs, and we propose to apply it in the complex domain and optimize the B-spline settings by means of amplitude data. We find that B-splines in the complex domain show great potential for estimating InSAR time series behavior, due to its insensitivity to errors in the integer ambiguity estimation, and that multiple model solutions can be derived from different B-spline parameter combinations. Our approach does not aim to provide a solution of the InSAR non-uniqueness problem, but it emphasizes the contribution of smoothness constraints to limit the solution space.
A 120 GHZ DOWN CONVERSION MIXER DESIGN FOR IMPROVED LINEARITY, HIGH CONVERSION-GAIN AND LOW NOISE-FIGURE IN 130 NM CMOS TECHNOLOGY

Future mobile networks, like 6G, will use frequencies above 100 GHz. Mobile networks target higher radio frequencies because they provide more bandwidth and capacity. Higher frequencies increase power consumption. This could be a problem for 6G battery-powered phones. High linearity, high conversion gain, and low NF are also challenges. Although Gain, power, linearity, and noise figure are trade-offs. More attention should be paid to the linearity of mixers in the design of wireless receivers because their input signal level is above 100 GHz, which is much higher than any other amplifier in the receiver. This study proposes a Gilbert cell mixer using BALUN to increase linearity, CG, and NF. 120 GHz is the mixer's input frequency. This mixer was simulated using ADS 130 nm RF-CMOS. The RF and LO BALUNS in this Gilbert cell mixer convert 120 GHz to 1 GHz. and obtains 68.664 dB RF-LO isolation, 8.882 CG and 8.4 db NF. The suggested design has -12.2 dBm of 1-dB compression point in this RF bandwidth. At 3 V bias, this double-balanced Gilbert cell mixture dissipates 66.8 mW.
Joint Learning Framework for Roads Semantic Segmentation from VHR and VGI Data

Automated road segmentation is considered an essential aspect of the development and planning of cities. However, automatically extracting road information from remote sensing imagery with manual labeling is still challenging due to the road network structures diversity. We propose a deep learning method that is joint learning from very high-resolution remote sensing images and volunteer geographic information. The method can handle complex scenarios. We modified the channel attention residual U-Net model with a weighted loss technique to address the problem of unbalanced training data and increase accuracy. Experimental results indicate that U-Net, Residual U-Net, and Attention U-Net achieved an overall accuracy of 0.91%, 0.93%, and 0.97%, respectively, and the proposed method with 0.99% overall accuracy has the best performance within the tested U-Net series.
Characterization of the Optical Properties and Vertical Distribution of the Asian Dust

The AErosol RObotic NETwork (AERONET) inversion data over East Asia (72°E – 135°E, 30°N – 50°N) in spring from 2018 to 2020 were collected to characterize the microphysical and single scattering properties of the dust aerosols in Asia, and a climatological aerosol model was then built up for the Asian dust. A bimodal log-normal size distribution is assumed for the climatological Asian dust model, and the non-spherical effect was considered for the coarse mode only. The microphysical and single scattering properties of dust aerosols are parameterized by the 675-nm AOD and wavelength in the Asian dust aerosol model. The parameterization is based on the fitting or interpolation of the AERONET data. A lookup table was generated based on the Asian dust model via the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) for retrieving the dust plume height from Earth Polychromatic Imaging Camera (EPIC) measurements.
Seasonal Forest Disturbance Detection Using Sentinel-1 SAR & Sentinel-2 Optical Timeseries Data and Transformers

Tropical seasonal forests make up 40% of the globally available forest stock and play an essential role in regulating the variability in the global carbon cycle. Therefore, there is a strong need to persistently monitor seasonal forest changes to understand the forest carbon fluctuation and to better conserve biodiversity and enforce laws. In this regard, the advent of the European Space Agency (ESA) Copernicus program avails a dense time-series of both Synthetic Aperture Radar (SAR) and optical images, globally and free of charge, that enables the exploitation of these images for near real-time forest monitoring. Detecting seasonal forest changes in dense time-series, however, is complicated by fluctuation in the detected signal that is induced by forest phenology change. Therefore, forest disturbance detection methods should account for these seasonal fluctuations to make an accurate inference about forest disturbances. In this regard, deep learning approaches designed for sequential data such as Transformers can be used to implicitly learn the natural forest seasonality pattern in the signal to detect forest disturbances. This abstract demonstrates the efficacy of Transformers to detect seasonal forest disturbance in a seasonal dry-forest region in Bolivia.
Integrating Radar Backscattering and Flood Water Depth Information for Improved Flood Mapping: A Case Study in Houston City

Houston is a large city with a population of over 2.3 million people, making it the fourth most populous city in the United States. It is known for its well-developed infrastructure and is a major economic center, but it is also vulnerable to flooding due to its low elevation, flat topography, and location along the Gulf Coast. Flooding is a major concern in coastal regions of Houston city because it can cause significant damage to buildings, infrastructure, and natural environments, as well as pose a serious risk to human safety. Hence, in this paper, a case study on flood in Houston city during Harvey-2017 has been conducted by integrating radar backscattering (Sentinel-1 SAR) and flood water depth information (FWDI), derived from flood depth tool (FWDT) using publicly available data in order to circumvent limitations in data access and improve flood mapping.
Utilization of Lactic Acid Bacteria Bacteriocin for Prolonging Shelf Life of Minced Meat

: Raw meat is known to be a source of multiple microbial foodborne. Bacteriocins have received a lot of publicity as a natural and safe approach for the preservation of food. In this study, bacteriocin from lactic acid bacteria was evaluated as an effective preservative agent in extending the shelf life of minced meat. The bacteriocin was produced and extracted by pH mediated cell adsorption method from two strains of Lactobacillus rhamnosus and Lactobacillus bulgaricus. The antibacterial activity was investigated by well diffusion and microdilution methods. The results showed that the antibacterial spectrum of both L. bulgaricus and L. rhamnosus products was broad and effective not only against Gram-positive such as B. cereu s but also against Gram-negative bacteria such as E. coli and S. typhimurium . Minimum inhibition concentrations (MIC) ranges for L. bulgaricus -partially purified bacteriocin (BLB) were between 1.25 and 0.625 mg/ml, while for L. rhamnosus (BLR) were between 2.5 to 0.078 mg/ml. Almost all tested pathogenic bacteria could be inhibited at a dilution of 1:4 (1.25 mg/ml) of bacteriocin of either L. bulgaricus and L. rhamonosis , so this concentration was used for preservation of fresh minced meat. From microbiological examination it was noticed that the control sample became unacceptable after 6 days of storage at 4°C, total count bacteria recorded 8.2x 10 CFU/g, whereas A (contains BLR) and B (contains BLB) samples were acceptable until 8 days of 7 storage, with lower total count bacteria of (1.0x 10 and 9.6x 10 CFU/g) and (2.3 x 10 and 7.1 x 10 CFU/g) after 4 4 5 5 6 and 8 days of storage, respectively. The obtained results showed that control samples (C) reached to spoilage level of psychrophilic bacteria after 6 days of storage, while the treated meat samples with bacteriocin recorded (7.7 x 10 and 3.4 x 10 ) and (2.3 x 10 and 2.6 x 10 ) CFU/g after 6 and 8 days of storage at 4°C for A and B 2
A Simple and Rapid Method to Assess the Quality of River Water: Correlation Between Electrical Conductivity and Fecal Coliform Density in Hyogo Prefecture, Japan

Hanna Horiguchi (  hannahoriguchi@gmail.com ) Osaka University: Osaka Daigaku https://orcid.org/0000-0001-7835-0703 Katsumi Shigemura Kobe University: Kobe Daigaku Koichi Kitagawa Kobe University: Kobe Daigaku Madoka Kitakawa Kobe University: Kobe Daigaku Minato Nakazawa Kobe University: Kobe Daigaku Tatsuhiko Noyori Toga River Conservancy Motomu Inoue Kobe University: Kobe Daigaku Michika Moriwaki Kobe University: Kobe Daigaku Reo Onishi Kobe University: Kobe Daigaku Chisato Yoshimura Kobe University: Kobe Daigaku Mayumi Kobayashi Kobe University: Kobe Daigaku Toshinobu Suzaki Kobe University: Kobe Daigaku
Field relations, geochemistry and petrogenesis of the Cretaceous basaltic Jutal dykes, Kohistan, northern Pakistan

A 75 Ma Cretaceous suite of low-medium-K basalt to basaltic andesite dykes intrudes the Kohistan terrane between Jutal and Chalt, Gilgit area, northern Pakistan. Orientational data suggest they intruded in response to NW–SE to N–S tension. The dykes cut the Albian–Aptian Chalt Volcanic Group and the 102 Ma Matum Das trondhjemite pluton. They cut the fold structures and penetrative fabrics associated with the collision between Kohistan and Eurasia and thus constrain the age of formation of the Northern Suture. The dykes define two groups on the basis of trace element geochemistry and geochemical trends. A depleted D-type group has low concentrations of incompatible elements (2–8 x primordial mantle) and (Ce/Yb)N ratios <1. An enriched E-type group is relatively enriched in both low field strength and light Rare Earth elements and exhibits increasing incompatible element concentrations with increasing Zr. Trace element and field data suggest that the dykes are genetically linked and formed by partial melting of a mantle source which has been metasomatized by subduction-related processes. Models involving (i) different degrees of partial melting (ii) two-stage partial melting and (iii) partial melting of a heterogeneous source are considered.
Contamination with Bacillus cereus of attiéké produced in the informal sector in the south of Côte d’Ivoire and risk management by the hydrothermal reheating

This study aimed to reduce the risk of consumption of attieke contaminated with B. cereus at doses higher than the acceptable limits. In total, 16 samples of attieke freshly prepared and 306 samples of attieke sold on the market were collected and analyzed according to the standard EN ISO 7932: 2004 with slight modification. The presumptive B. cereus isolates were identified by polymerization chain reaction (PCR). In addition, the resistance of B. cereus group spores to hydrothermal reheating of attieke was evaluated. The results showed that B. cereus group was found in 25.2% of attieke on sale and an average load of vegetative cells and spores were 2.4 ± 1.1 log (CFU/g) and 2.3 ± 1.1 log (CFU/g) respectively. B. thuringiensis (56%) and B. cereus sensu stricto (44%) were identified species. The hydrothermal reheating of attieke by steam at 90°C for at least 15 min reduced B. cereus spores count from 4.2 log (CFU/g) in attieke into 1.8 ± 1.0 log (CFU/g). An average time to reduce the initial concentration of one logarithm was 8.5 ± 1.9 min (D 90°C = 8.5 ± 1.9 min). The maximum time limit of consumption of attieke reheated by steam at 90°C was 18 hours. Therefore, it is recommended to reheat attieke by steam for at least 15 min at 90°C before any consumption, if good hygiene practices cannot guarantee his safety.
Automated Delineation of the Agricultural Fields using Multi-Task Deep Learning and Optical Satellite Imagery

Agricultural field boundary information is an essential input for precision agriculture. This paper proposes a Multi-scale Multi-task Boundary Detection Deep Learning (DL) Network (MMBDNet) based on spatial attention mechanisms to delineate agricultural fields using high-resolution optical satellite imagery. The designed DL architecture simultaneously learns three tasks - a major task for field prediction and two auxiliary tasks for boundary prediction and distance estimation. We experimented with the agricultural landscape of Île-de-France, France, using the cloud-free time-series images from PlanetScope satellite that capture key phenological stages of crops. The segmentation results from different months are combined and post-processed using hierarchical watershed segmentation to extract field instances. We compared the MMBDNet with the baseline single-task U-Net and multitask BsiNet models at pixel- and object-level. Our results show that the MMBDNet has the highest pixel-level (above 85%) and object-level (above 70%) accuracy compared to U-Net and BsiNet.
A New Solution to Identify Phase Unwrapping Errors in Redundant Sequences of Small Baseline DInSAR Interferograms

We present in this work a solution to identify Phase Unwrapping (PhU) errors in redundant sequences of small baseline Differential SAR Inteferometry (DInSAR) interferograms like those exploited by the Small Baseline Subset (SBAS) approach, which allows the retrieval of displacement time series. In particular, the temporal information is exploited through the temporal coherence parameter, which represents a point-like quality indicator of the PhU solutions within advanced DInSAR methods like the above mentioned SBAS technique. First of all we show that this parameter needs to be carefully considered because it may lose sensitivity when the number of images of the exploited dataset increases. Subsequently, we explore a different use of the temporal coherence, moving from a single valued parameter to a time-dependent function. This permits to regain sensitivity and to reduce the number of interferograms eligible to be corrected. The last part of the work is devoted to present a case study where the proposed solution allows the identification of PhU errors.
Quantifying Flaring From Space on Multispectral Images Calibrated With In-Situ Flowmeters

Flaring is a process commonly used in the oil and gas industry, and it remains an important source of greenhouse gas emissions. The main method used for flaring on assets is the burning of excess gas during extraction. State of the art studies focused mainly around the VIIRS sensor, offering daily detection. Its low resolution allows an approximative localization of about 400 m which is sufficient for detection, but not enough for quantification. We propose to perform flaring detection at a 20 m resolution using SWIR Sentinel-2 images. The characteristic shape of the detections on the Sentinel-2 images allows us to easily eliminate false positives, mainly clouds, and to increase the reliability of the algorithm. 10-minute flaring flow rate data from four oil and gas facilities is used to build the model. Gas flaring on these sites was detected on 364 images between 2019 and 2022. We used B8, B11 and B12 bands of Sentinel-2 to create the Planck curve fitting of the hot source radiances. It yields temperature (K) and emission scaling factor (ESF), and additional calculations are done to estimate source size (m2) and radiant heat (MW). The retrieved flaring radiant heat is used against reported flaring gas flow rates to get the correlation between these two parameters. Cross validation of the model to the fifth site showed R2 of 0.9 and average error around 6%.
Deep Parallel Structure Network for Multi-Scale Target Detection in Remote Sensing Images

This paper constructs a detection network that combines convolutional neural network and Transformer in parallel to address the challenges of multi-scale target detection in remote sensing images. The network utilizes global and local information interaction to further improve the effectiveness of multi-scale object detection tasks. Additionally, the network introduces both top-down and bottom-up pathways to fuse multi-scale information, and employs coordinate attention mechanism to perform feature selection. The proposed network is compared with some existing networks on the LEVIR remote sensing image dataset, and the results show that the proposed network achieves higher average detection accuracy in terms of multi-scale target detection, particularly for small objects.
How will changes in carbon dioxide and methane modify the mean structure of the mesosphere and thermosphere

A global average model of the coupled mesosphere, thermosphere and ionosphere is used to examine the effect of trace gas variations on the overall structure of these regions. In particular, the variations caused by CO2 and CH4 doublings and halvings from present day mixing ratios are presented. The results indicate that the mesosphre and thermosphere temperatures will cool by about 10K and 50K respectively as the CO2 and CH4 mixing ratios are doubled. These regions are heated by similar amounts when the trace gas mixing ratios are halved. Compositional redistributions also occur in association with changes in the temperature profile. The results show that global change will occur in the upper atmosphere and ionosphere as well as in the lower atmosphere during the 21st century.
Research on Internet Security Assessment Function Based on Neural Network Algorithm

With the wide application of computer network, the network structure becomes more and more complex, and the types of network attacks change, and the internet security problem becomes increasingly prominent. Based on this, based on the meaning, present situation and problems of computer internet security, this article makes an in-depth study of NN algorithm. The application of NN in internet security assessment is discussed, and a internet security assessment model based on NN algorithm is constructed. At the same time, according to the complex network theory and internet security assessment theory, the NN algorithm is studied, and an improved model of NN algorithm is established. Experimental results show that the false alarm rate of this algorithm is low; The detection rate is high, reaching about 95.2%, and the detection time of the algorithm is less than that of other algorithms. The work of this article provides new ideas and methods for comprehensive assessment of computer internet security, especially for the prediction and control of internet security problems in the future.
Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.