Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Proximal Gradient Algorithms for Joint Graphical Lasso

We consider learning as an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning joint graphical lasso (JGL). We propose proximal gradient procedures with and without a backtracking option for the JGL. These procedures are first-order methods and relatively simple, and the subproblems are solved efficiently in closed form. We further show the boundedness for the solution of the JGL problem and the iterates in the algorithms. The numerical results indicate that the proposed algorithms can achieve high accuracy and precision, and their efficiency is competitive with state-of-the-art algorithms.
Deep Learning to Ternary Hash Codes by Continuation

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.
A Comparison of Modern General-Purpose Visual SLAM Approaches

Advancing maturity in mobile and legged robotics technologies is changing the landscapes where robots are being deployed and found. This innovation calls for a transformation in simultaneous localization and mapping (SLAM) systems to support this new generation of service and consumer robots. No longer can traditionally robust 2D lidar systems dominate while robots are being deployed in multi-story indoor, outdoor unstructured, and urban domains with increasingly inexpensive stereo and RGB-D cameras. Visual SLAM (VSLAM) systems have been a topic of study for decades and a small number of openly available implementations have stood out: ORB-SLAM3, OpenVSLAM and RTABMap.This paper presents a comparison of these 3 modern, feature rich, and uniquely robust VSLAM techniques that have yet to be benchmarked against each other, using several different datasets spanning multiple domains negotiated by service robots. ORB-SLAM3 and OpenVSLAM each were not compared against at least one of these datasets previously in literature and we provide insight through this lens. This analysis is motivated to find general purpose, feature complete, and multi-domain VSLAM options to support a broad class of robot applications for integration into the new and improved ROS 2 Nav2 System as suitable alternatives to traditional 2D lidar solutions.
Temporal-aware Language Representation Learning From Crowdsourced Labels

Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose TACMA, a temporal-aware language representation learning heuristic for crowdsourced labels with multiple annotators. The proposed approach (1) explicitly models the intra-observer variability with attention mechanism; (2) computes and aggregates per-sample confidence scores from multiple workers to address the inter-observer disagreements. The proposed heuristic is extremely easy to implement in around 5 lines of code. The proposed heuristic is evaluated on four synthetic and four real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage the reproducible results, we make our code publicly available at https://github.com/CrowdsourcingMining/TACMA.
Linear Programming Bounds for Almost-Balanced Binary Codes

We revisit the linear programming bounds for the size vs. distance trade-off for binary codes, focusing on the bounds for the almost-balanced case, when all pairwise distances are between $d$ and $n-d$, where $d$ is the code distance and $n$ is the block length. We give an optimal solution to Delsarte's LP for the almost-balanced case with large distance $d\geq(n-\sqrt{n})/2+1$, which shows that the optimal value of the LP coincides with the Grey-Rankin bound for self-complementary codes. We also show that a limitation of the asymptotic LP bound shown by Samorodnitsky, namely that it is at least the average of the first MRRW upper bound and Gilbert-Varshamov bound, continues to hold for the almost-balanced case.
CCVS: Context-aware Controllable Video Synthesis

This presentation introduces a self-supervised learning approach to the synthesis of new video clips from old ones, with several new key elements for improved spatial resolution and realism: It conditions the synthesis process on contextual information for temporal continuity and ancillary information for fine control. The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module. Adversarial training of the autoencoder in the appearance and temporal domains is used to further improve the realism of its output. A quantizer inserted between the encoder and the transformer in charge of forecasting future frames in latent space (and its inverse inserted between the transformer and the decoder) adds even more flexibility by affording simple mechanisms for handling multimodal ancillary information for controlling the synthesis process (eg, a few sample frames, an audio track, a trajectory in image space) and taking into account the intrinsically uncertain nature of the future by allowing multiple predictions. Experiments with an implementation of the proposed approach give very good qualitative and quantitative results on multiple tasks and standard benchmarks.
Advancing hybrid quantum-classical algorithms via mean operators

Entanglement in quantum many-body systems is the key concept for future technology and science, opening up a possibility to explore uncharted realms in an enormously large Hilbert space. The hybrid quantum-classical algorithms have been suggested to control quantum entanglement of many-body systems, and yet their applicability is intrinsically limited by the numbers of qubits and quantum operations. Here we propose a theory which overcomes the limitations by combining advantages of the hybrid algorithms and the standard mean-field-theory in condensed matter physics, named as mean-operator-theory. We demonstrate that the number of quantum operations to prepare an entangled target many-body state such as symmetry-protected-topological states is significantly reduced by introducing a mean-operator. We also show that a class of mean-operators is expressed as time-evolution operators and our theory is directly applicable to quantum simulations with $^{87}$Rb neutral atoms or trapped $^{40}$Ca$^+$ ions.
Beyond Goldfish Memory: Long-Term Open-Domain Conversation

Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. In contrast, the long-term conversation setting has hardly been studied. In this work we collect and release a human-human dataset consisting of multiple chat sessions whereby the speaking partners learn about each other’s interests and discuss the things they have learnt from past sessions. We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better. In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state of the art.
Ranking labs-of-origin for genetically engineered DNA using Metric Learning

With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also perform various other tasks, like clustering both DNA sequences and labs and using them as features for Machine Learning models applied to solve other problems. This work demonstrates that our method outperforms the classic training method for this task while generating other helpful information.
Revisiting IoT Device Identification

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, while leveraging approaches previously proposed by other researchers. We compare the accuracy of four different previously proposed machine learning models (tree-based and neural network-based) for identifying IoT devices. We use packet trace data collected over a period of six months from a large IoT test-bed. We show that, while all models achieve high accuracy when evaluated on the same dataset as they were trained on, their accuracy degrades over time, when evaluated on data collected outside the training set. We show that on average the models' accuracy degrades after a couple of weeks by up to 40 percentage points (on average between 12 and 21 percentage points). We argue that, in order to keep the models' accuracy at a high level, these need to be continuously updated.
Transparency in Healthcare Reporting: The Case of External Contractors and Consultants in New Zealand’s Healthcare System

This study investigates the quality of reporting around the spending related to the use of external consultant and contractors in New Zealand’s 20 District Health Boards (DHBs). We make use of the publicly available annual reviews conducted by the New Zealand Parliament Health Select Committee (HSC) as well as DHB data which were retrieved using Official Information Act (OIA) requests. The quality of reporting was judged on the differences and discrepancies observed in the HSC reports each year as well as the DHB internal data. Perhaps, unsurprisingly, total spending on external consultants and contractors has been increasing over the years while the quality of reporting has been decreasing. Our analysis highlighted a number of quality issues—mistakes, discrepancies and an overall lack of standardised reporting in almost all of the DHBs. Some of these discrepancies included failure to provide information required by the HSC, differences in yearly total amounts in consecutive reports and differences between information provided to the HSC and to the authors of this article. It is hoped that this research and the prospective areas for improvement highlighted here are used as a guide to improve the quality of healthcare financial reporting.
Glioblastoma Cell Migration is Directed by Electrical Signals

Background: Electric field (EF) directed cell migration (electrotaxis) is known to occur in glioblastoma multiforme (GBM) and neural stem cells, with key signaling pathways frequently dysregulated in GBM. One such pathway is EGFR/PI3K/Akt, which is down-regulated by peroxisome proliferator activated receptor gamma (PPARγ) agonists. We investigated the effect of electric fields on GBM differentiated and stem cell migration and whether this was affected by treatment with the PPARγ agonist pioglitazone. Methods Primary GBM cell lines were cultured as differentiated and glioma stem cells (GSCs) and then exposed to EFs using electrotaxis chambers and imaged with time lapse microscopy. Cells were then treated with varying concentrations of pioglitazone and/or its inhibitor GW9662 and their responses to EFs examined. Results We demonstrated that GBM differentiated and GSCs have opposing preferences for anodal and cathodal migration, respectively. Pioglitazone treatment resulted in significantly decreased directed cell migration in both cell types. Western blot analysis did not demonstrate any change in PPARγ expression with and without exposure to EF. Conclusions Opposing EF responses in primary GBM differentiated cells and GSCs can be inhibited chemically by pioglitazone, implicating GBM EF modulation as a potential target in preventing tumour recurrence.
Factors determining the serum 25‐hydroxyvitamin D response to vitamin D supplementation: Data mining approach

Vitamin D supplementation has been shown to prevent vitamin D deficiency, but various factors can affect the response to supplementation. Data mining is a statistical method for pulling out information from large databases. We aimed to evaluate the factors influencing serum 25‐hydroxyvitamin D levels in response to supplementation of vitamin D using a random forest (RF) model. Data were extracted from the survey of ultraviolet intake by nutritional approach study. Vitamin D levels were measured at baseline and at the end of study to evaluate the responsiveness. We examined the relationship between 76 potential influencing factors on vitamin D response using RF. We found several features that were highly correlated to the serum vitamin D response to supplementation by RF including anthropometric factors (body mass index [BMI], free fat mass [FFM], fat percentage, waist‐to‐hip ratio [WHR]), liver function tests (serum gamma‐glutamyl transferase [GGT], total bilirubin, total protein), hematological parameters (mean corpuscular volume [MCV], mean corpuscular hemoglobin concentration [MCHC], hematocrit), and measurement of insulin sensitivity (homeostatic model assessment of insulin resistance). BMI, total bilirubin, FFM, and GGT were found to have a positive relationship and homeostatic model assessment for insulin resistance, MCV, MCHC, fat percentage, total protein, and WHR were found to have a negative correlation to vitamin D concentration in response to supplementation. The accuracy of RF in predicting the response was 93% compared to logistic regression, for which the accuracy was 40%, in the evaluation of the correlation of the components of the data set to serum vitamin D.
A kinetic model for magnetostriction of a ferrogel with physical networking

Kinetics of magnetostriction of ferrogel with physical networking based on natural polysaccharide guar gum with embedded strontium hexaferrite magnetic particles were studied in the uniform magnetic field 420 mT. An ellipsoidal sample was elongated by 37% along the applied field and contracted by 15% in the transverse direction, while its volume was kept constant. The characteristic time of magnetostriction was 440 s. Dynamic mechanical analysis in an oscillatory mode showed that the deformation of ferrogel is mostly elastic rather than viscous. Its storage modulus was almost constant in a frequency range of 0.1–100 Hz and by at least an order of magnitude larger than the loss modulus. Meanwhile, a developed theoretical model based on the elasto-viscous behaviour of the ferrogel failed to estimate correctly the experimental value of its magnetostriction. Calculated values of the elongation of ferrogel in the field were several orders of magnitude lower than those observed in the experiment for the ferrogel with physical networking. Consistency between the experiment and the theory was achieved using the alternative consideration based on the deformation of a liquid droplet of ferrofluid. The applicability of such an approach was discussed concerning structural relaxation properties of the ferrogel with physical networking. This article is part of the theme issue ‘Transport phenomena in complex systems (part 1)’.
The hodograph equation for slow and fast anisotropic interface propagation

Using the model of fast phase transitions and previously reported equation of the Gibbs–Thomson-type, we develop an equation for the anisotropic interface motion of the Herring–Gibbs–Thomson-type. The derived equation takes the form of a hodograph equation and in its particular case describes motion by mean interface curvature, the relationship ‘velocity—Gibbs free energy’, Klein–Gordon and Born–Infeld equations related to the anisotropic propagation of various interfaces. Comparison of the present model predictions with the molecular-dynamics simulation data on nickel crystal growth (obtained by Jeffrey J. Hoyt et al. and published in Acta Mater. 47 (1999) 3181) confirms the validity of the derived hodograph equation as applicable to the slow and fast modes of interface propagation. This article is part of the theme issue ‘Transport phenomena in complex systems (part 1)’.
Chemiosemiosis and Complex Patterned Signals: A Chemosemiotic Hypothesis of Language Evolution

Abstract Olfaction, as a semiotic modality, receives relatively less attention than other sensory modalities. However, chemiosemiosis and semiochemicals are fundamental components of zoosemiosis, occurring across animal taxonomic groups. Indeed, olfaction is thought to be one of the most ancient sensory modalities from an evolutionary perspective and significantly, even unicellular organisms, such as the bacterium Escherichia coli, utilize a form of chemiosemiosis when foraging for nutrients, as part of a process known as ‘chemotaxis’. Further, many taxonomic groups have evolved to produce dedicated ‘semiochemicals’ (often known as pheromones or allomones) which have the sole purpose of being diffused into the environment as a social signal. In this paper, I highlight the importance of Umwelt theory when studying animal communication, by reviewing the less conspicuous and intuitive chemiosemiotic modality, across animal taxa. I then go on to discuss chemiosemiosis within a linguistic framework and argue that complex pattern recognition underpins linguistic theory. Thus, I explore the concept that chemiosemiosis has features in common with language, when the factor of time, in the transmission and decoding of a signal, is taken into account. Moreover, I provide discursive evidence in support of a unified theory of sensory perception, based on structural and functional aspects of signal transmission and cognitive complex pattern recognition. I conclude by proposing a chemosemiotic hypothesis of language evolution.
Effects of Slag Addition and Mechanical Pre-Processing on the Properties of Recycled Concrete in Terms of Compressive Strength and Workability

Abstract Concrete waste as crushed concrete aggregates (CCA) in structural concrete prolongs the technical life of the reference concrete accomplishing closed loop recycling. CCA concrete reaches the reference concrete compressive strength and workability by the densification of CCA and cement paste. Our previous study demonstrates CCA densification by mechanical pre-processing, aggregate quality improvements discerned by increased packing density giving reference concrete strength and workability. This study addresses paste densification with blast furnace slag (GGBS) to replace 30 (wt.%) of Portland cement at reference concrete w/b ratio 0.5 and a lower w/b 0.42. Two CCA replacements are investigated: fine aggregates, CCA50; overall aggregate replacement, CCA100. Compressive strength results show that both CCA50, CCA100 mixes achieve reference values at w/b 0.42, only CCA100 achieves reference value at w/b 0.5 as a climate-optimized concrete. The CCA50 mix-w/b 0.5 reaches reference strength when paste densification by GGBS is combined with CCA densification from mechanical pre-processing of aggregates. The 7-day strength of CCA100 with GGBS increases by 11% by mixing with pre-soaked GGBS. Statistical analysis of CCA100 strength results shows significant improvements with GGBS compared to mechanical pre-processing. Significant improvements are possible in CCA50 mix for a combination of mechanical pre-processed aggregates and GGBS replacement.
To school through the screens: the use of screen devices to support young children's education and learning during the COVID-19 pandemic

The aim of the current study was to investigate how children in Ireland adapted to the change in schooling, and particularly their use of screen devices to support their learning at home, during a period of lockdown due to the COVID-19 crisis in early 2020. This research draws on data from 506 parents of children aged 1–10 years in Ireland who completed an online survey during lockdown in May and June 2020. Parents responded to a series of questions for the Play and Learning in the Early Years (PLEY) Survey about their child's play, learning and development, including questions about their child's education and use of screen devices for educational purposes. The results indicate that screentime increased for most children during lockdown, and they spent significantly more time on schoolwork if they had access to a computer or laptop. They were also more likely to spend time watching educational TV programmes or playing educational screen-based games if their parent believed screen content had educational value. The findings highlight the impact of lockdown on children's lives both for their education, and how they adapted to schooling at home using screen devices. The implications for education and future research are considered.
Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention

ABSTRACT Due to the complex background and spatial distribution, it brings great challenge to object detection in high-resolution remote sensing images. In view of the characteristics of various scales, arbitrary orientations, shape variations, and dense arrangement, a multiscale object detection method in high-resolution remote sensing images is proposed by using rotation invariance deep features driven by channel attention. First, a channel attention module is added to our feature fusion and scaling-based single shot detector (FS-SSD) to strengthen the long-term semantic dependence between objects for improving the discriminative ability of the deep features. Then, an oriented response convolution is followed to generate feature maps with orientation channels to produce rotation invariant deep features. Finally, multiscale objects are predicted in a high-resolution remote sensing image by fusing various scale feature maps with multiscale feature module in FS-SSD. Five experiments are conducted on NWPU VHR-10 dataset and achieve better detection performance compared with the state-of-the-art methods.