Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing Flashiness‐Intensity‐Duration‐Frequency (F‐IDF): A New Metric to Quantify Flash Flood Intensity

Flash flooding is a damaging weather event, yet it remains challenging to quantify its severity. We propose a development—the Flashiness‐Intensity‐Duration‐Frequency (F‐IDF) curve—to quantify flash flood intensity based on the frequency and duration of the event. As a proof‐of‐concept, we mapped Contiguous US (CONUS)‐wide F‐IDF values at 3,722 stream gage locations and explored their relations with basin attributes. It is found that (a) The return periods of flash flood events are highly associated with the return peroids of rainfall events; (b) Climatological precipitation amounts exhibit the most positive correlation with flashiness while a basin's drainage area is the most negatively correlated; (c) Correlation of flashiness with basin attributes decreases with increasing F‐IDF return periods and shorter event durations. Both aspects are attributable to the rainfall signal overwhelming the underlying basin attributes as the intensities become more extreme. This new metric has implications for hydrology and emergency responders.
Treelet Prefetching For Ray Tracing

Ray tracing is traditionally only used in offline rendering to produce images of high fidelity because it is computationally expensive. Recent Graphics Processing Units (GPUs) have included dedicated accelerators to bring ray tracing to real-time rendering for video games and other graphics applications. These accelerators focus on finding the closest intersection between a ray and a scene using a hierarchical tree data structure called a Bounding Volume Hierarchy (BVH) tree. However, BVH tree traversal is still very costly due to divergent rays accessing different parts of the tree, with each ray following a unique pointer-chasing sequence that is difficult to optimize with traditional methods. To address this, we propose treelet prefetching to reduce the latency of ray traversal. Treelets are smaller subtrees created by splitting the BVH tree. When a ray visits a treelet root node, we prefetch the corresponding treelet, enabling deeper levels of the tree to be fetched in advance. This reduces the latency associated with pointer-chasing during tree traversal. Our approach uses a hardware prefetcher with a two-stack treelet based traversal algorithm, maximizing the benefits of treelet prefetching. Our simulation results show treelet prefetching on average improves performance of the baseline RT Unit in Vulkan-Sim by 32.1% while maintaining the same power consumption.CCS CONCEPTS• Computing methodologies → Ray tracing; • Computer systems organization → Single instruction, multiple data.
Simultaneous and Heterogenous Multithreading

The landscape of modern computers is undoubtedly heterogeneous, as all computing platforms integrate multiple types of processing units and hardware accelerators. However, the entrenched programming models focus on using only the most efficient processing units for each code region, underutilizing the processing power within heterogeneous computers.This paper simultaneous and heterogenous multithreading (SHMT), a programming and execution model that enables opportunities for “real” parallel processing using heterogeneous processing units. In contrast to conventional models, SHMT can utilize heterogeneous types of processing units concurrently for the same code region. Furthermore, SHMT presents an abstraction and a runtime system to facilitate parallel execution. More importantly, SHMT needs to additionally address the heterogeneity in data precision that various processing units support to ensure the quality of the result.This paper implements and evaluates SHMT on an embedded system platform with a GPU and an Edge TPU. SHMT achieves up to 1.95× speedup and 51.0% energy reduction compared to GPU baseline.
MAD MAcce: Supporting Multiply-Add Operations for Democratizing Matrix-Multiplication Accelerators

Modern GPUs commonly employ specialized matrix multiplication units (MXUs) to accelerate matrix multiplication, the core computation of deep learning workloads. However, it is challenging to exploit the MXUs for GPGPU applications whose fundamental algorithms do not rely on matrix multiplication. Furthermore, an additional programming effort is necessary to tailor existing code or algorithms using dedicated APIs or libraries to utilize MXUs. Therefore, MXUs are often underutilized even when GPUs hunger for higher throughput.We observe that the intensive multiply-and-add (MAD) instructions often become bottlenecks in compute-intensive applications. Furthermore, such MAD instructions create computations similar to the dot-product operations of MXUs when they have data dependency. By leveraging these observations, we propose a novel MXU architecture called MAD MAcce that can handle both matrix multiplication and MAD operations. In our design, GPU compiler detects target MAD instructions by analyzing the instruction stream and generates new instructions for MAD Macce in a programmer-transparent manner. Then, MAD MAcce executes the newly generated instructions. By offloading MAD operations to the MXUs, GPUs can exploit the high throughput of MXUs for various domains without significant hardware modification or additional programming efforts. In our evaluation, MAD MAcce achieves up to 2.13× speedup and 1.65× average speedup in compute-intensive applications.CCS CONCEPTS• Computer systems organization → Single instruction multiple data.
Spatula: A Hardware Accelerator for Sparse Matrix Factorization

Solving sparse systems of linear equations is a crucial component in many science and engineering problems, like simulating physical systems. Sparse matrix factorization dominates a large class of these solvers. Efficient factorization algorithms have two key properties that make them challenging for existing architectures: they consist of small tasks that are structured and compute-intensive, and sparsity induces long chains of data dependences among these tasks. Data dependences make GPUs struggle, while CPUs and prior sparse linear algebra accelerators also suffer from low compute throughput.We present Spatula, an architecture for accelerating sparse matrix factorization algorithms. Spatula hardware combines systolic processing elements that execute structured tasks at high throughput with a flexible scheduler that handles challenging data dependences. Spatula enables a novel scheduling algorithm that avoids stalls and load imbalance while reducing data movement, achieving high compute utilization. As a result, Spatula outperforms a GPU running the state-of-the-art sparse Cholesky and LU factorization implementations by gmean 47× across a wide range of matrices, and by up to thousands of times on some challenging matrices.
Does Background Matter? A Comparative Characterization of Mouse Models of Autosomal Retinitis Pigmentosa rd1 and Pde6b-KO

Many retinal degenerative diseases result in vision impairment or permanent blindness due to photoreceptor loss or dysfunction. It has been observed that Pde6brd1 mice (rd1), which carry a spontaneous nonsense mutation in the pde6b gene, have a strong phenotypic similarity to patients suffering from autosomal recessive retinitis pigmentosa. In this study, we present a novel mouse model of retinitis pigmentosa generated through pde6b gene knockout using CRISPR/Cas9 technology. We compare this Pde6b-KO mouse model to the rd1 mouse model to gain insights into the progression of retinal degeneration. The functional assessment of the mouse retina and the tracking of degeneration dynamics were performed using electrophysiological methods, while retinal morphology was analyzed through histology techniques. Interestingly, the Pde6b-KO mouse model demonstrated a higher amplitude of photoresponse than the rd1 model of the same age. At postnatal day 12, the thickness of the photoreceptor layer in both mouse models did not significantly differ from that of control animals; however, by day 15, a substantial reduction was observed. Notably, the decline in the number of photoreceptors in the rd1 model occurred at a significantly faster rate. These findings suggest that the C3H background may play a significant role in the early stages of retinal degeneration.
Suicide prevention curriculum development for health and social care students: Protocol for a scoping review

Suicide has become a serious public health concern and international research has shown that the majority of individuals who die by suicide had receive healthcare in the year prior to their death. This presents an opportunity for suicide prevention by strategically training healthcare students in suicide prevention knowledge and skills. The objective of this scoping review is to identify literature that describes the design, development, implementation and/or evaluation of suicide prevention training for healthcare and/or social care students in higher education settings. Studies will only be considered eligible for inclusion if they describe the design, development, implementation and/or evaluation of suicide prevention curricula being delivered to healthcare or social care degree students in higher education. Quantitative, qualitative, and mixed method studies published between 2011 and 2023 (inclusive) and in the English language will be considered eligible. This scoping review will be conducted according to the PRISMA guidelines for scoping reviews (PRISMA-ScR). The developed search strategy will be implemented across six databases: PubMed, ERIC (Education Resources Information Center), PsycINFO, Embase, CINAHL and Web of Science. Several grey literature sources will also be consulted. Further potential results will be located by hand-searching the reference lists of included articles. The search strategy will include variations of the terms: ‘student’, ‘suicide prevention’ and ‘education’. The search will be limited to titles, abstracts, and keywords in databases that allow it. Two reviewers will complete the screening using the predefined inclusion criteria. A third reviewer will resolve any conflicts during the screening and eligibility appraisal processes. Results will be presented in the form of tabulated results and an accompanying narrative summary, describing key findings and context related to learning outcomes, methodologies employed and implementation of the identified programmes.
WHICH SATELLITE IMAGE SHOULD BE USED FOR MAPPING

Abstract. Today, topographical mapping based on satellite images is a standard method. With the large number of very high-resolution optical satellites, it only a question of the Ground Sampling Distance (GSD) and the map scale to be generated. But the classical large-format satellite images are expensive. With the today’s variety of the classical small satellites (601kg to 1200kg) to Nano-satellites (1.1kg to 10kg) of 3U (10cm × 10cm × 30cm), various options are available that influence the economic solutions. An overview of the accessible optical satellites is given, with some specific information on the mini-satellites that offer new economical solutions for topographic mapping. Significantly more optical satellites are currently in operation, but their images are used only for military purposes or they are restricted for national use due to lack of image storage and limited download possibilities.
Transcription attenuation in synthetic promoters in tandem formation

Closely spaced promoters are ubiquitous in prokaryotic and eukaryotic genomes. How their structure and dynamics relate remains unclear, particularly for tandem formations. To study their transcriptional interference, we engineered two pairs and one trio of synthetic promoters in non-overlapping, tandem formation, in single-copy plasmids. From in vivo measurements in E. coli cells, we found that promoters in tandem formation have attenuated transcription rates. The attenuation strength can be widely fine-tuned by the promoters’ positioning, natural regulatory mechanisms, and other factors, including the antibiotic rifampicin, which hampers RNAP promoter escape. From this, and supported by in silico models, we concluded that the attenuation emerges from premature terminations generated by collisions between RNAPs elongating from upstream promoters and RNAPs occupying downstream promoters. Moreover, we found that these collisions can cause one or both RNAPs to fall-off. The broad spectrum of possible, externally regulated, attenuation strengths in synthetic tandem promoters should make these structures valuable internal regulators of future synthetic circuits.
Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space

Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.
Rigorous Evaluation of Computer Processors with Statistical Model Checking

Experiments with computer processors must account for the inherent variability in executions. Prior work has shown that real systems exhibit variability, and random effects must be injected into simulators to account for it. Thus, we can run multiple executions of a given benchmark and generate a distribution of results. Prior work uses standard statistical techniques that are not suitable. While the result distributions may take any forms that are unknown a priori, many works naively assume they are Gaussian, which can be far from the truth. To allow rigorous evaluation for arbitrary result distributions, we introduce statistical model checking (SMC) to the world of computer architecture. SMC is a statistical technique that is used in research communities that depend heavily on statistical guarantees. SMC provides a rigorous mathematical methodology that employs experimental sampling for probabilistic evaluation of properties of interest, such that one can determine with a desired confidence whether a property (e.g., System X is 1.1x faster than System Y) is true or not. SMC alone is not enough for computer architects to draw conclusions based on their data. We create an end-to-end framework called SMC for Processor Analysis (SPA) which utilizes SMC techniques to provide insightful conclusions given experimental data.CCS CONCEPTS• Computing methodologies → Simulation evaluation.
Fast, Robust, and Transferable Prediction for Hardware Logic Synthesis

The increasing complexity of computer chips and the slow logic synthesis process have become major bottlenecks in the hardware design process, also hindering the ability of hardware generators to make informed design decisions while considering hardware costs. While various models have been proposed to predict physical characteristics of hardware designs, they often suffer from limited domain adaptability and open-source hardware design data scarcity.In this paper, we present SNS v2, a fast, robust, and transferable hardware synthesis predictor based on deep learning models. Inspired by modern natural language processing models, SNS v2 adopts a three-phase training approach encompassing pre-training, fine-tuning, and domain adaptation, enabling it to leverage more abundant unlabeled and off-domain training data. Additionally, we propose a novel contrastive learning approach based on circuit equivalence to enhance model robustness. Our experiments demonstrate that SNS v2 achieves two to three orders of magnitude faster speed compared to conventional EDA tools, while maintaining state-of-the-art prediction accuracy. We also show that SNS v2 can be seamlessly integrated into hardware generator frameworks for real-time cost estimation, resulting in higher quality design recommendations in a significantly reduced time frame.CCS CONCEPTS• Hardware → Integrated circuits; High-level and register-transfer level synthesis; Logic synthesis; • Computing methodologies → Machine learning.
MAICC : A Lightweight Many-core Architecture with In-Cache Computing for Multi-DNN Parallel Inference

The growing complexity and diversity of neural networks in the fields of autonomous driving and intelligent robots have facilitated the research of many-core architectures, which can offer sufficient programming flexibility to simultaneously support multi-DNN parallel inference with different network structures and sizes compared to domain-specific architectures. However, due to the tight constraints of area and power consumption, many-core architectures typically use lightweight scalar cores without vector units and are almost unable to meet the high-performance computing needs of multi-DNN parallel inference. To solve the above problem, we design an area- and energy-efficient many-core architecture by integrating large amounts of lightweight processor cores with RV32IMA ISA. The architecture leverages the emerging SRAM-based computing-in-memory technology to implement vector instruction extensions by reusing memory cells in the data cache instead of conventional logic circuits. Thus, the data cache in each core can be reconfigured as the memory part and the computing part with the latter tightly coupled with the core pipeline, enabling parallel execution of the basic RISC-V instructions and the extended multi-cycle vector instructions. Furthermore, a corresponding execution framework is proposed to effectively map DNN models onto the many-core architecture by using intra-layer and inter-layer pipelining, which potentially supports multi-DNN parallel inference. Experimental results show that the proposed MAICC architecture obtains a 4.3× throughput and 31.6× energy efficiency over CPU (Intel i9-13900k). MAICC also achieves a 1.8× energy efficiency over GPU (RTX 4090) with only 4MB on-chip memory and 28 mm2 area.
Intelligent Detection Technology for Deformation Rate of Underground Drainage Pipeline Network Based On Deep Learning

In order to capture the underground drainage network's shadowy environment, poor-quality photos are often captured. These images typically include little information, have low conventional pipe deformation rate detection accuracy, and have significant 3D modeling constraints. Picture quality and resolution may be greatly enhanced by using the resolution reconstruction and three-dimensional fast modeling techniques. In addition, the recognition effect can ensure the current state of the project, and the speed with which it can determine the deformation rate of pipes at every level. A guarantee for the construction control of pipeline deformation and safe operation is provided by this result, which may be applied to comparable projects.
A New BiRNN-SVA Method for Side Lobe Suppression

The spatially variant apodization (SVA) algorithm, a classic super-resolution method for synthetic aperture radar (SAR) images, can suppress side lobes while maintain the resolution of the main lobe. To address the problem of residual side lobes or loss of main lobe energy in improved SVA algorithms, the article proposes a new side lobe suppression method combining the bidirectional recurrent neural network (BiRNN) and the SVA algorithm, employing BiRNN to extract the main lobe and side lobe features of radar data to achieve side lobe suppression at any Nyquist sampling rate. The land flight experiment data of the fully polarized microwave scatterometer is used to quantitatively evaluate the side lobe suppression performance and the main lobe energy in order to verify the effectiveness of the BiRNN-SVA method. The experimental results demonstrate that the BiRNN-SVA method can be applied to data at any Nyquist sampling rate and has superior PSLR and ISLR compared to the GSVA algorithm and MSVA algorithm. The image processed with the proposed method retains more fine details and edge features. In comparison to the GSVA algorithm and MSVA algorithm, the image contrast and focus have increased by 31.6% and 3.6%, respectively, and by 4.4% and 1.1%.
State-dependent circuit dynamics of superficial and deep CA1 pyramidal cells in macaques

Diverse neuron classes in hippocampal CA1 have been identified through their heterogeneity in cellular/molecular composition. How these classes relate to the hippocampal function and network dynamics that support cognition in primates remains unclear. Here we report functional cell groups in CA1 of freely-behaving macaques that show a range of spectral phase preferences and varied responses to local network states like the sharp-wave ripple. Segregating superficial-from deep-layer pyramidal cells uncovered differences in firing rate, burstiness, and sharp-wave ripple associated firing. They also showed strata-specific interactions with the inhibitory cell groups. CA1 ensemble recordings revealed the first observations of cell assemblies in the macaque hippocampus, and show that the stratification of pyramidal cells in primates is a major organizing principle of assemblies. These results suggest a sublayer-specific circuit organization in hippocampal CA1 of the freely-behaving macaque that may support dissociable contributions across cognitive and behavioral processes in the primate.
On the Feasibility of Unleveled Fully-Homomorphic Signatures

We build the first unleveled fully homomorphic signature scheme in the standard model. Our scheme is not constrained by any a-priori bound on the depth of the functions that can be homomorphically evaluated, and relies on subexponentially-secure indistinguishability obfuscation, fully-homomorphic encryption and a non-interactive zero-knowledge (NIZK) proof system with composable zero-knowledge. Our scheme is also the first to satisfy the strong security notion of context-hiding for an unbounded number of levels, ensuring that signatures computed homomorphically do not leak the original messages from which they were computed. All building blocks are instantiable from falsifiable assumptions in the standard model, avoiding the need for knowledge assumptions. The main difficulty we overcome stems from the fact that bootstrapping, which is a crucial tool for obtaining unleveled fully homomorphic encryption (FHE), has no equivalent for homomorphic signatures, requiring us to use novel techniques.
New Technologies in Alfalfa

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