Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence of Galaxy Assembly Bias in SDSS DR7 Galaxy Samples from Count Statistics

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias, from a novel combination of counts-in-cylinders statistics, 𝑃 ( 𝑁 CIC ) , with the standard measurements of the projected two-point correlation function, 𝑤 p ( 𝑟 p ) , and number density, 𝑛 gal , of galaxies. We measure 𝑛 gal , 𝑤 p ( 𝑟 p ) and 𝑃 ( 𝑁 CIC ) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect signiﬁcant positive central assembly bias for the 𝑀 𝑟 < − 20 . 0 and 𝑀 𝑟 < − 19 . 5 samples. Central galaxies preferentially reside within haloes of high concentration at ﬁxed mass. Positive central assembly bias is also favoured in the 𝑀 𝑟 < − 20 . 5 and 𝑀 𝑟 < − 19 . 0 samples. We ﬁnd no evidence of central assembly bias in the 𝑀 𝑟 < − 21 . 0 sample. We observe only a marginal preference for negative satellite assembly bias in the 𝑀 𝑟 < − 20 . 0 and 𝑀 𝑟 < − 19 . 0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our ﬁndings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.
Snapping for high-speed and high-efficient, butterfly swimming-like soft flapping-wing robot

: Natural selection has tuned many flying and swimming animals across different species to share the same narrow design space for optimal high-efficient and energy-saving locomotion, e.g., their dimensionless Strouhal numbers St that relate flapping frequency and amplitude and forward speed fall within the range of 0.2 < St < 0.4 for peak propulsive efficiency. It is rather challenging to achieve both fast and high-efficient soft-bodied swimming robots with high performances that are comparable to marine animals, due to the observed narrow optimal design space in nature and the compliance of soft body. Here, bioinspired by the wing or fin flapping motion in flying and swimming animals, we report leveraging the generic principle of snapping instabilities in the bistable and multistable flexible pre-curved wings for high-performance, butterfly swimming-like, soft-bodied flapping-wing robots. The soft swimming robot is lightweight (2.8 grams) and demonstrates a record-high speed of 3.74 body length/s (4.8 times faster than the reported fastest soft swimmer), high-efficient (0.2 < St = 0.25 < 0.4), low energy consumption cost, and high maneuverability (a high turning speed of 157 o /s). Its high performances largely outperform the state-of-the-art soft swimming robots and are even comparable to its biological counterparts.
Heterogeneous Acceleration Pipeline for Recommendation System Training

Recommendation systems are unique as they show a conflation of compute and memory intensity due to their deep learning and massive embedding tables. Training these models typically involve a hybrid CPU-GPU mode, where GPUs accelerate the deep learning portion and the CPUs store and process the memory-intensive embedding tables. The hybrid mode incurs a substantial CPU-to-GPU transfer time and relies on main memory bandwidth to feed embeddings to GPU for deep learning acceleration. Alternatively, we can store the entire embeddings across GPUs to avoid the transfer time and utilize the GPU's High Bandwidth Memory (HBM). This approach requires GPU-to-GPU backend communication and scales the number of GPUs with the size of the embedding tables. To overcome these concerns, this paper offers a heterogeneous acceleration pipeline, called Hotline. Hotline leverages the insight that only a small number of embedding entries are accessed frequently, and can easily fit in a single GPU's HBM. Hotline implements a data-aware and model-aware scheduling pipeline that utilizes the (1) CPU main memory for not-frequently-accessed embeddings and (2) GPUs' local memory for frequently-accessed embeddings. Hotline improves the training throughput by dynamically stitching the execution of popular and not-popular inputs through a novel hardware accelerator and feeding to the GPUs. Results on real-world datasets and recommender models show that Hotline reduces the average training time by 3x and 1.8x in comparison to Intel-optimized CPU-GPU DLRM and HugeCTR-optimized GPU-only baseline, respectively. Hotline increases the overall training throughput to 35.7 epochs/hour in comparison to 5.3 epochs/hour for the Intel-optimized DLRM baseline
A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks

Memristive devices are a class of circuit elements that shows great promise as future building block for brain-inspired computing. One influential view in theoretical neuroscience sees the brain as a function-computing device: given input signals, the brain applies a function in order to generate new internal states and motor outputs. Therefore, being able to approximate functions is a fundamental axiom to build upon for future brain research and to derive more efficient computational machines. In this work we apply a novel supervised learning algorithm - based on controlling niobium-doped strontium titanate memristive synapses - to learning non-trivial multidimensional functions. By implementing our method into the spiking neural network simulator Nengo, we show that we are able to at least match the performance obtained when using ideal, linear synapses and - in doing so - that this kind of memristive device can be harnessed as computational substrate to move towards more efficient, brain-inspired computing.
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation

Sign language recognition and translation first uses a recognition module to generate glosses from sign language videos and then employs a translation module to translate glosses into spoken sentences. Most existing works focus on the recognition step, while paying less attention to sign language translation. In this work, we propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the instruction module and the learning-based feature fuse strategy into a Transformer network. In this way, the pre-trained model's language ability can be well explored and utilized to further boost the translation performance. Moreover, by exploring the representation space of sign language glosses and target spoken language, we propose a multi-level data augmentation scheme to adjust the data distribution of the training set. We conduct extensive experiments on two challenging benchmark datasets, PHOENIX-2014-T and ASLG-PC12, on which our method outperforms former best solutions by 1.65 and 1.42 in terms of BLEU-4. Our code is published at https://github.com/yongcaoplus/TIN-SLT.
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems

This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called $\kappa$-distributed control, which lets the agents make control decisions based on the state information within distance $\kappa$ on the underlying graph. This controller can tune its degree of decentralization using the parameter $\kappa$ and thus allows a characterization of the relationship between decentralization and performance. We show that under mild assumptions, including stabilizability, detectability, and a subexponentially growing graph condition, the performance difference between $\kappa$-distributed control and centralized optimal control becomes exponentially small in $\kappa$. This result reveals that distributed control can achieve near-optimal performance with a moderate degree of decentralization, and thus it is an effective controller architecture for large-scale networked systems.
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP

Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today’s state of the art. We show that a wide multi-layer perceptron (MLP) using a Bag-of-Words (BoW) outperforms the recent graph-based models TextGCN and HeteGCN in an inductive text classification setting and is comparable with HyperGAT. Moreover, we fine-tune a sequence-based BERT and a lightweight DistilBERT model, which both outperform all state-of-the-art models. These results question the importance of synthetic graphs used in modern text classifiers. In terms of efficiency, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an \mathcal{O}(N^2) graph, where N is the vocabulary plus corpus size. Finally, since Transformers need to compute \mathcal{O}(L^2) attention weights with sequence length L, the MLP models show higher training and inference speeds on datasets with long sequences.
Multimodal Colored Point Cloud to Image Alignment

Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.
Warwick-Monash Economics Student

The recent ‘News Media Bargaining Code’ has raised controversy as Australia attempts to force digital platforms to pay news publishers for their links and snippets. To understand the impacts of the bargaining code on both the sustainability and quality of journalism, we develop a model where there are two types of news content available to consumers: full news from the news publisher and snippets on the platform. We show that the bargaining code strictly improves the news publisher’s welfare but increases their joint investment incentives if and only if the relative investment in snippets is sufficiently large. We further establish that commercial agreements are a promising alternative that strictly increases both the welfare of news publishers and the quality of their news. Our results suggest that the bargaining code is better used as an indirect threat to promote fair commercial negotiations, rather than used directly.
High Fructose and High Fat Diet Impair Different Types of Memory through Oxidative Stress in a Sex- and Hormone-Dependent Manner

Metabolic syndrome (MetS) contributes to the spread of cardiovascular diseases, diabetes mellitus type 2, and neurodegenerative diseases. Evaluation of sex- and hormone-dependent changes in body weight, blood pressure, blood lipids, oxidative stress markers, and alterations in different types of memory in Sprague–Dawley rats fed with a high fat and high fructose (HFHF) diet were evaluated. After 12 weeks of feeding the male and female rats with HFHF, body weight gain, increase in blood pressure, and generation of dyslipidemia compared to the animals fed with chow diet were observed. Regarding memory, it was noted that gonadectomy reverted the effects of HFHF in the 24 h novel object recognition task and in spatial learning/memory analyzed through Morris water maze, males being more affected than females. Nevertheless, gonadectomy did not revert long-term memory impairment in the passive avoidance task induced by HFHF nor in male or female rats. On the other hand, sex-hormone–diet interaction was observed in the plasma concentration of malondialdehyde and nitric oxide. These results suggest that the changes observed in the memory and learning of MetS animals are sex- and hormone-dependent and correlate to an increase in oxidative stress.
Enhanced-efficiency nitrogen fertilizer boosts cauliflower productivity and farmers’ income: Multi-location and multi-year field trials across Nepal

Summary Enhanced-efficiency nitrogen (N) fertilizers (EENFs) such as slow-release polymer-coated urea (PCU) and deep placement of urea briquettes (UBs) improve nitrogen use efficiency (NUE) by reducing N losses and increasing nitrogen uptake by plants. Multilocation field trials (81) with cauliflower were conducted across two agroecological regions covering seven districts during two crop-growing seasons between 2018 and 2020 to assess the potential of three EENFs, i.e., PCU, sulphur-coated urea (SCU) and UB for increasing curd yields, agronomic NUE (AEN) and economic benefits over conventional urea (CU). Results were compared with farmers’ current nutrient management practice (FP): applying CU at 58.5 kg N ha−1 (ranging from 33 to 88 kg N ha−1). The N rates in three EENF treatments were 33% lower (100 kg N ha−1), considering their higher N use efficiency, than for CU (150 kg N ha−1). We hypothesize that EENFs produce similar or even higher yields compared with CU. For both years, all three EENFs resulted in significantly (p < 0.05) higher curd yields than CU (36.7 ± 1.1 t ha−1). PCU, SCU and UB increased yields by 21, 21 and 24% over those for CU. The yield increment was much higher (PCU, 44%; SCU, 43%; UB, 46%) than for FP. Similarly, PCU, SCU and UB increased the partial factor productivity of N (PFPN) by 91, 90 and 94% and the AEN by 133, 129 and 138%, respectively, compared with CU. The gross margins of all three EENFs were similar: an average 25% more than with CU and 51% more than with FP. These results suggest that EENFs could help increase productivity and farmer income while considerably reducing N input, compared to use of CU. The government of Nepal should promote these EENFs by removing barriers to access for the associated fertilizers and foster their use through extension.
Differential Tolerance of Cowpea Cultivars to Osmotic Stress in Germinative Phase: A Multivariate Approach

: This study aimed to characterize the behavior of different cultivars of Vigna unguiculata L. Walp. to osmotic stress, from germination to vigor parameters. The experimental design used was completely randomized in a 14 × 4 factorial arrangement, with fourteen cultivars and four levels of osmotic potential (0, − 0.1, − 0.2, and − 0.4 MPa) of the germination solution. BRS-Novaera and BRS-Paje ú cultivars were characterized with possible tolerance at both the − 0.1 and − 0.2 MPa levels. The study pointed to the BRS-Pujante cultivar as the most sensitive to the − 0.4 MPa level. The multivariate technique used allowed for a satisfactory characterization of the treatments adopted.
Red Imported Fire Ant, Solenopsis invicta (Burden) (Hymenoptera: Formicidae), Abundance and Arthropod Community Diversity Affected by Pasture Management

Abstract The red imported fire ant, Solenopsis invicta (Buren) (Hymenoptera: Formicidae), is one of the most prolific invasive species in the southeastern US. These invaders preferentially colonize highly disturbed land and grassland habitat. Management of livestock in pasture systems can have a profound impact on the level of disturbance in grassland habitats, and we hypothesized that adaptive multi-paddock pasture management would significantly increase S. invicta abundance in southeastern US pastures where arthropod diversity would decrease as S. invicta abundance increases. We studied the effects that adaptive multi-paddock pasture management systems (based on stocking density, rotation frequency, and insecticide/anthelmintic [wormer] application rates) have on fire ant mound abundance and arthropod diversity for the soil, foliar, and dung communities. Solenopsis invicta mounds and mound areas were documented along transect lines in 6 pastures. Soil and foliar arthropod communities were collected along the same transect lines, and dung communities were sampled from pats within the pasture system. Pastures managed under adaptive multi-paddock practices had 3.4× more S. invicta mounds and 4.6× more mound area than their conventionally managed counterparts. However, arthropod diversity did not correlate with S. invicta abundance in any of the 3 arthropod communities sampled. This study shows adaptive multi-paddock pasture management can increase S. invicta mound abundance, but arthropod communities in adaptive multi-paddock pastures do not suffer decreased diversity from increased abundance of S. invicta.
Bees forage on bahiagrass spikelets

Summary Bahiagrass (Paspalum notatum Flüggé; Poaceae) is primarily a pasture grass in the southern US. It is also lawn grass found in residential settings, public parks, and planted in golf courses. Bahiagrass is principally wind-pollinated, but the pollination contribution of insect pollinators is not documented. A survey was conducted on bahiagrass patches in central Georgia, USA, to determine the visitation of foraging pollinators. The results showed that European honey bees, bumblebees, and sweat bees forage on bahiagrass spikelets. Of the total bees observed, 10.6% (n = 94) were foraging on bahiagrass, compared to 46.8% on white clover, 27.7% on dandelion, and 14.8% on buckhorn plantain. This is the first report showing bee foraging on bahiagrass. The results suggest that bahiagrass spikelets may be a supplemental resource for bees and essential for bee conservation programs in rural and suburban areas. More research is warranted to determine how bahiagrass spikelets play a role in meeting the nutritional needs of bees to help sustain their colony and provide pollination service.
Neural Architecture Search Survey: A Hardware Perspective

We review the problem of automating hardware-aware architectural design process of Deep Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design has led to advancements in many fields, such as computer vision, virtual reality, and autonomous driving. The end-to-end design process of a CNN is a challenging and time-consuming task, as it requires expertise in multiple areas such as signal and image processing, neural networks, and optimization. At the same time, several hardware platforms, general- and special-purpose, have equally contributed to the training and deployment of these complex networks in a different setting. Hardware-Aware Neural Architecture Search (HW-NAS) automates the architectural design process of DNNs to alleviate human effort and generate efficient models accomplishing acceptable accuracy-performance tradeoffs. The goal of this article is to provide insights and understanding of HW-NAS techniques for various hardware platforms (MCU, CPU, GPU, ASIC, FPGA, ReRAM, DSP, and VPU), followed by the co-search methodologies of neural algorithm and hardware accelerator specifications.
The site-perimeter of compositions

Abstract Compositions of n are finite sequences of positive integers (σi)i=1k $\begin{array}{} (\sigma_i)_{i = 1}^k \end{array} $ such that σ1+σ2+⋯+σk=n. $$\begin{array}{} \sigma_1+\sigma_2+\cdots +\sigma_k = n. \end{array} $$ We represent a composition of n as a bargraph with area n such that the height of the i-th column of the bargraph equals the size of the i-th part of the composition. We consider the site-perimeter which is the number of nearest-neighbour cells outside the boundary of the polyomino. The generating function that counts the total site-perimeter of compositions is obtained. In addition, we rederive the average site-perimeter of a composition by direct counting. Finally we determine the average site-perimeter of a bargraph with a given semi-perimeter.
Validation of the algorithm for ice-on-pipe brine thermal storage systems

The purpose of this paper is to present results generated from the IceTES computer program, which was developed to predict the dynamic performance of ice-on-pipe brine thermal energy storage systems. A set of parametric studies is presented to show the predicted trends during both charge and discharge cycles. The qualitative behavior for the brine outlet temperature, charge or discharge rate, and state of charge is displayed for a typical thermal storage system. To validate the model, performance predictions generated with IceTES are compared to experimental data from a national laboratory for two ice thermal storage systems. The predicted and measured results for the brine exit temperatures and load profiles are in good agreement throughout most of the charge and discharge cycles. Additional results are presented to show a complete system simulation for a typical day in which the thermal energy storage component is coupled with a chiller and load.
Impact of Ethical Training on Auditors’ Ethical Decision Making in Morocco

Abstract The purpose of this study is to investigate the moderating role that can play ethical training in increasing auditors’ level of ethics. Therefore, this research will test any significant differences between the two groups through a multi-group moderation analysis. The first group represents auditors who took an ethics course before, and the other group represents auditors who did not previously take any course. This comparison will be performed based on the relationships between the variables in our model, namely ethical judgment, ethical intention, moral identity centrality, and perceived moral intensity. The sample of this study is mainly external senior auditors operating in audit firms in Morocco. We could obtain 125 usable responses, and we processed data with SmartPLS software. The results show that auditors’ moral identity impacts their ethical judgment positively and significantly, and the effect is more substantial for the group of auditors who took an ethics course before. Moreover, taking an ethics course seems to moderate also the positive and significant relationship between auditors’ ethical judgment and their ethical intention. However, the results were opposite to what we expected concerning the relationship between perceived moral intensity and auditors’ ethical judgment. Thus, ethical training seemed to moderate the positive relationship negatively.