Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable, Interpretable, Verifiable&Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables

We propose $\mathcal{T}$ruth $\mathcal{T}$able net ($\mathcal{TT}$net), a novel Convolutional Neural Network (CNN) architecture that addresses, by design, the open challenges of interpretability, formal verification, and logic gate conversion. $\mathcal{TT}$net is built using CNNs' filters that are equivalent to tractable truth tables and that we call Learning Truth Table (LTT) blocks. The dual form of LTT blocks allows the truth tables to be easily trained with gradient descent and makes these CNNs easy to interpret, verify and infer. Specifically, $\mathcal{TT}$net is a deep CNN model that can be automatically represented, after post-training transformation, as a sum of Boolean decision trees, or as a sum of Disjunctive/Conjunctive Normal Form (DNF/CNF) formulas, or as a compact Boolean logic circuit. We demonstrate the effectiveness and scalability of $\mathcal{TT}$net on multiple datasets, showing comparable interpretability to decision trees, fast complete/sound formal verification, and scalable logic gate representation, all compared to state-of-the-art methods. We believe this work represents a step towards making CNNs more transparent and trustworthy for real-world critical applications.
Difformer: Empowering Diffusion Models on the Embedding Space for Text Generation

Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space. In this paper, we conduct systematic studies and analyze the challenges between the continuous data space and the embedding space which have not been carefully explored. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the loss function. Secondly, as the norm of embeddings varies between popular and rare words, adding the same noise scale will lead to sub-optimal results. In addition, we find the normal level of noise causes insufficient training of the model. To address the above challenges, we propose Difformer, an embedding diffusion model based on Transformer, which consists of three essential modules including an additional anchor loss function, a layer normalization module for embeddings, and a noise factor to the Gaussian noise. Experiments on two seminal text generation tasks including machine translation and text summarization show the superiority of Difformer over compared embedding diffusion baselines.
Viscoelasticity of suspension of red blood cells under oscillatory shear flow

We present a numerical analysis of the rheology of a suspension of red blood cells (RBCs) for different volume fractions in a wall-bounded, effectively inertialess, small amplitude oscillatory shear (SAOS) flow for a wide range of applied frequencies. The RBCs are modeled as biconcave capsules, whose membrane is an isotropic and hyperelastic material following the Skalak constitutive law. The frequency-dependent viscoelasticity in the bulk suspension is quantified by the complex viscosity, defined by the amplitude of the particle shear stress and the phase difference between the stress and shear. SAOS flow basically impedes the deformation of individual RBCs as well as the magnitude of fluid-membrane interactions, resulting in a lower specific viscosity and first and second normal stress differences than in steady shear flow. Although it is known that the RBC deformation alone is sufficient to give rise to shear-thinning, our results show that the complex viscosity weakly depends on the frequency-modulated deformations or orientations of individual RBCs, but rather depends on combinations of the frequency-dependent amplitude and phase difference. The effect of the viscosity ratio between the cytoplasm and plasma and of the capillary number are also assessed.
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift

Concept shift is a prevailing problem in natural tasks like medical image segmentation where samples usually come from different subpopulations with variant correlations between features and labels. One common type of concept shift in medical image segmentation is the"information imbalance"between label-sparse samples with few (if any) segmentation labels and label-dense samples with plentiful labeled pixels. Existing distributionally robust algorithms have focused on adaptively truncating/down-weighting the"less informative"(i.e., label-sparse in our context) samples. To exploit data features of label-sparse samples more efficiently, we propose an adaptively weighted online optimization algorithm -- AdaWAC -- to incorporate data augmentation consistency regularization in sample reweighting. Our method introduces a set of trainable weights to balance the supervised loss and unsupervised consistency regularization of each sample separately. At the saddle point of the underlying objective, the weights assign label-dense samples to the supervised loss and label-sparse samples to the unsupervised consistency regularization. We provide a convergence guarantee by recasting the optimization as online mirror descent on a saddle point problem. Our empirical results demonstrate that AdaWAC not only enhances the segmentation performance and sample efficiency but also improves the robustness to concept shift on various medical image segmentation tasks with different UNet-style backbones.
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-Quality Pavement Crack Detection

Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good performance, but ignore detection efficiency in practical applications. In this article, to address the first issue, we collected two new databases (i.e. Rain365 and Sun520) captured on rainy and sunny days respectively, which enrich the data of the open source community. For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet. Specifically, we introduce a novel olive-shaped structure for the encoder network, a lightweight multi-scale block and a new up-sampling method in the decoder network. Numerous experiments show that our model can better balance detection performance and efficiency compared with previous models. Especially, on the Sun520 dataset, our CarNet significantly advances the state-of-the-art performance with ODS F-score from 0.488 to 0.514. Meanwhile, it does so with an improved detection speed (104 frames per second) which is orders of magnitude faster than some recent DCNNs-based algorithms specially designed for crack detection.
Common Fixed Points of One‐Parameter Nonexpansive Semigroups

In this paper, we show that the set of all common fixed points of a one‐parameter nonexpansive semigroup is that of some single nonexpansive mapping. We next compare our result with Bruck's famous fixed‐point theorem. We finally prove very simple convergence theorems to a common fixed point. In our discussion, we assume neither the strict convexity of the underlying space, nor the weak compactness of the domain of a nonexpansive semigroup. 2000 Mathematics Subject Classification 47H20 (primary), 47H10 (secondary).
Modelling Aotearoa New Zealand's COVID-19 protection framework and the transition away from the elimination strategy

For the first 18 months of the COVID-19 pandemic, New Zealand used an elimination strategy to suppress community transmission of SARS-CoV-2 to zero or very low levels. In late 2021, high vaccine coverage enabled the country to transition away from the elimination strategy to a mitigation strategy. However, given negligible levels of immunity from prior infection, this required careful planning and an effective public health response to avoid uncontrolled outbreaks and unmanageable health impacts. Here, we develop an age-structured model for the Delta variant of SARS-CoV-2 including the effects of vaccination, case isolation, contact tracing, border controls and population-wide control measures. We use this model to investigate how epidemic trajectories may respond to different control strategies, and to explore trade-offs between restrictions in the community and restrictions at the border. We find that a low case tolerance strategy, with a quick change to stricter public health measures in response to increasing cases, reduced the health burden by a factor of three relative to a high tolerance strategy, but almost tripled the time spent in national lockdowns. Increasing the number of border arrivals was found to have a negligible effect on health burden once high vaccination rates were achieved and community transmission was widespread.
Exploring enablers and barriers to implementing the Transparency and Openness Promotion Guidelines: a theory-based survey of journal editors

The Transparency and Openness Promotion (TOP) Guidelines provide a framework to help journals develop open science policies. Theories of behaviour change can guide understanding of why journals do (not) implement open science policies and the development of interventions to improve these policies. In this study, we used the Theoretical Domains Framework to survey 88 journal editors on their capability, opportunity and motivation to implement TOP. Likert-scale questions assessed editor support for TOP, and enablers and barriers to implementing TOP. A qualitative question asked editors to provide reflections on their ratings. Most participating editors supported adopting TOP at their journal (71%) and perceived other editors in their discipline to support adopting TOP (57%). Most editors (93%) agreed their roles include maintaining policies that reflect current best practices. However, most editors (74%) did not see implementing TOP as a high priority compared with other editorial responsibilities. Qualitative responses expressed structural barriers to implementing TOP (e.g. lack of time, resources and authority to implement changes) and varying support for TOP depending on study type, open science standard, and level of implementation. We discuss how these findings could inform the development of theoretically guided interventions to increase open science policies, procedures and practices.
Modelling the impact of the Omicron BA.5 subvariant in New Zealand

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.
Toward equitable open research: stakeholder co-created recommendations for research institutions, funders and researchers

Open Research aims to make research more accessible, transparent, reproducible, shared and collaborative. Doing so is meant to democratize and diversify access to knowledge and knowledge production, and ensure that research is useful outside of academic contexts. Increasing equity is therefore a key aim of the Open Research movement, yet mounting evidence demonstrates that the practices of Open Research are implemented in ways that undermine this. In response, we convened a diverse community of researchers, research managers and funders to co-create actionable recommendations for supporting the equitable implementation of Open Research. Using a co-creative modified Delphi method, we generated consensus-driven recommendations that address three key problem areas: the resource-intensive nature of Open Research, the high cost of article processing charges, and obstructive reward and recognition practices at funders and research institutions that undermine the implementation of Open Research. In this paper, we provide an overview of these issues, a detailed description of the co-creative process, and present the recommendations and the debates that surrounded them. We discuss these recommendations in relation to other recently published ones and conclude that implementing ours requires ‘global thinking’ to ensure that a systemic and inclusive approach to change is taken.
The Large-Scale Movement of Saharan Air Outbreaks over the Northern Equatorial Atlantic

Abstract The intense and prolonged heating of air passing over the Sahara during the summer and early fall months forms a deep mixed layer which extends up to 15–20,000 ft during July, the warmest month. The dust-laden heated air emerges from West Africa as a series of large-scale anticyclonic eddies which move westward over the tropical Atlantic above the trade-wind moist layer, principally in the layer between 5000 and 15,000 ft (600–800 mb). Measurements made during BOMEX show that this Saharan air is characterized by high values of potential temperature, dust and radon-222 which confirm a desert origin. As the parcels of air within the layer proceed across the Atlantic the continuous fallout of particulate matter and the mixing at the base of the layer cause dust to be transferred to the lower levels where its concentration may become sufficiently great to produce dense haze at the surface over wide areas over the Atlantic and Caribbean in the latitude belt 10–25N. Nevertheless, measurements indicate .
Origin and context of C. T. R. Wilson's ideas on electron runaway in thunderclouds

[1] The publications, research notebooks and personal correspondence of C.T. R. Wilson are reviewed to understand his thinking on the idea of electron runaway in thunderclouds. The expansion cloud chamber, developed and refined over a period of nearly fifty years, is centrally important. Wilson's habit of research is characterized by a pattern of alternating between laboratory work with the cloud chamber and field observations of thunderclouds and the global electrical circuit. The evidence that the ionization tracks of electrons in the cloud chamber straightened with increasing energy was key to the idea of electron runaway, published in 1925. Despite numerous negative results by Wilson's colleagues Schonland, Halliday and others in searching for energetic particles in the vicinity of thunderclouds, Wilson supported the idea from its inception to his final publication in 1956. Wilson's notebooks indicate a conceptual awareness of runaway breakdown in thunderclouds 60 years in advance of contemporary theoretical publications. Definitive observational results on runaway breakdown in contemporary research were likely delayed for 60 years as a result of the unfortunate negative experimental results with inadequate equipment.
Effect of incorporation of bead-beating during DNA extraction for quantitative polymerase chain reaction-based detection of Trichuris trichiura in stool samples in community settings: a systematic review

Abstract This meta-analysis was designed to assess the effect of the addition of a bead-beating (BB) step during DNA extraction to effectively isolate Trichuris trichiura DNA from stool samples for quantitative polymerase chain reaction (qPCR)-based diagnosis. qPCR-based molecular studies comparing the inclusion of a bead-beating step during the DNA extraction from stool samples with extraction without the step were included in the analysis. Studies using real patient samples in community settings were included. The PubMed database and Google search engine were searched in December 2019. Risk of bias and applicability were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 checklist. Odds ratios (ORs) for individual studies were combined to estimate the random effects model OR. A total of six independent sub-studies were gathered from two published original articles. The division of the two major studies into six sub-studies was indispensable due to the nature of the study carried out. 128 of the total 192 samples (in all studies) were positive for T. trichiura when BB was used during DNA extraction compared to 108/192 when BB was excluded. The combined OR was 1.66 (95% confidence interval: 1.059 to 2.602). Though only two articles were included in the study, six exclusive individual sub-studies were analyzed. Inherent differences in the background prevalence of helminths in the study population could impact the sensitivity of qPCR. It was found that the inclusion of the BB step during DNA extraction significantly increased the sensitivity of the test. This study was not registered in any database.
Assessment of Local Blowing and Suction in a Turbulent Boundary Layer

Effects of local blowing or suction from a spanwise slot on a turbulent boundary layer flow are investigated using the direct numerical simulation technique. Three different blowing or suction velocities are imposed on the slot keeping blowing or suction flow rate constant. The recoveries of mean wall pressure for the different blowing velocities collapse well on the same recovery line. However, each wall pressure recovers just after the slot for suction. In the blowing case, the relaxation of rms wall pressure fluctuations and pressure gradient after the slot is seen and the relaxation distance from the slot center is nearly constant for three different blowing velocities. In the suction case, the flow recovers from the immediate rear of the slot. These features are also observed in three-dimensional views of the near-wall vortices.
Hardware-Software Codesign of DNN Accelerators using Approximate Posit Multipliers

Emerging data intensive AI/ML workloads encounter memory and power wall when run on general-purpose compute cores. This has led to the development of a myriad of techniques to deal with such workloads, among which DNN accelerator architectures have found a prominent place. In this work, we propose a hardware-software co-design approach to achieve system-level benefits. We propose a quantized data-aware POSIT number representation that leads to a highly optimized DNN accelerator. We demonstrate this work on SOTA SIMBA architecture, extendable to any other acceler-ator. Our proposal reduces the buffer/storage requirements within the architecture and reduces the data transfer cost between the main memory and the DNN accelerator. We have investigated the impact of using integer, IEEE floating point, and posit multipliers for LeNet, ResNet and VGG NNs trained and tested on MNIST, CIFAR10 and ImageNet datasets, respectively. Our system-level analysis shows that the proposed approximate-fixed-posit multiplier when implemented on SIMBA architecture, achieves on average ~2.2x speed up, consumes ~3.1x less energy and requires ~3.2x less area, re-spectively, against the baseline SOTA architecture, without loss of accuracy (~ ± 1 %)
Beyond von Neumann Era: Brain-inspired Hyperdimensional Computing to the Rescue

Breakthroughs in deep learning (DL) continuously fuel innovations that profoundly improve our daily life. However, DNNs overwhelm conventional computing architectures by their massive data movements between processing and memory units. As a result, novel computer architectures are indispensable to improve or even replace the decades-old von Neumann architecture. Nevertheless, going far beyond the existing von Neumann principles comes with profound reliability challenges for the performed computations. This is due to analog computing together with emerging beyond-CMOS technologies being inherently noisy and inevitably leading to unreliable computing. Hence, novel robust algorithms become a key to go beyond the boundaries of the von Neumann era. Hyper-dimensional Computing (HDC) is rapidly emerging as an attractive alternative to traditional DL and ML algorithms. Unlike conventional DL and ML algorithms, HDC is inherently robust against errors along a much more efficient hardware implementation. In addition to these advantages at hardware level, HDC's promise to learn from little data and the underlying algebra enable new possibilities at the application level. In this work, the robustness of HDC algorithms against errors and beyond von Neumann architectures are discussed. Further, the benefits of HDC as a machine learning algorithm are demonstrated with the example of outlier detection and reinforcement learning.
Automatic Generation of Complete Polynomial Interpolation Design Space for Hardware Architectures

Hardware implementations of elementary functions regularly de-ploy piecewise polynomial approximations. This work determines the complete design space of piecewise polynomial approximations meeting a given accuracy specification. Knowledge of this design space determines the minimum number of regions required to approximate the function accurately enough and facilitates the generation of optimized hardware which is competitive against the state of the art. Designers can explore the space of feasible architec-tures without needing to validate their choices. A heuristic based decision procedure is proposed to generate optimal ASIC hard-ware designs. Targeting alternative hardware technologies simply requires a modified decision procedure to explore the space. We highlight the difficulty in choosing an optimal number of regions to approximate the function with, as this is input width dependent.
RIMAC: An Array-level ADC/DAC-Free ReRAM-Based In-Memory DNN Processor with Analog Cache and Computation

By directly computing in analog domain, processing-in-memory (PIM) is emerging as a promising alternative to overcome the memory bottleneck of traditional von-Neuman architecture, especially for deep neural networks (DNNs). However, the data outside PIM macros in most existing PIM accelerators are stored and operated as digital signals that require massive expensive digital-to-analog (D/A) and analog-to-digital (A/D) converters. In this work, an array-level ADC/DAC-free ReRAM-based in-memory DNN processor named RIMAC is proposed, which accelerates various DNNs in pure analog-domain with analog cache and analog computation modules to eliminate the expensive D/A and A/D conversions. Our experiment result shows the peak energy efficiency is improved by about $34.8\times, 97.6\times, 10.7\times$, and $14.0\times$ compared to PRIME, ISAAC, Lattice, and 21'DAC for various DNNs on ImageNet, respectively.