Miracle-dz/UniFAR-FTUs huggingface dataset:
POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes

Maintaining an up-to-date map to reflect recent changes in the scene is very important, particularly in situations involving repeated traversals by a robot operating in an environment over an extended period. Undetected changes may cause a deterioration in map quality, leading to poor localization, inefficient operations, and lost robots. Volumetric methods, such as truncated signed distance functions (TSDFs), have quickly gained traction due to their real-time production of a dense and detailed map, though map updating in scenes that change over time remains a challenge. We propose a framework that introduces a novel probabilistic object state representation to track object pose changes in semi-static scenes. The representation jointly models a stationarity score and a TSDF change measure for each object. A Bayesian update rule that incorporates both geometric and semantic information is derived to achieve consistent online map maintenance. To extensively evaluate our approach alongside the state-of-the-art, we release a novel real-world dataset in a warehouse environment. We also evaluate on the public ToyCar dataset. Our method outperforms state-of-the-art methods on the reconstruction quality of semi-static environments.
PSCNN: A 885.86 TOPS/W Programmable SRAM-based Computing-In-Memory Processor for Keyword Spotting

Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of small CIM macros and bad programmablity for model execution. This paper proposes a programmable CIM processor with a single large sized CIM macro instead of multiple smaller ones for power efficient computation and a flexible instruction set to support various binary 1-D convolution Neural Network (CNN) models in an easy way. Furthermore, the proposed architecture adopts the pooling write-back method to support fused or independent convolution/pooling operations to reduce 35.9% of latency, and the flexible ping-pong feature SRAM to fit different feature map sizes during layer-by-layer execution. The design fabricated in TSMC 28nm technology achieves 150.8 GOPS throughput and 885.86 TOPS/W power efficiency at 10 MHz when executing our binary keyword spotting model, which has higher power efficiency and flexibility than previous designs.
Efficient Score Computation and Expectation-Maximization Algorithm in Regime-Switching Models

This study proposes an efficient algorithm for score computation for regime-switching models, and derived from which, an efficient expectation-maximization (EM) algorithm. Different from existing algorithms, this algorithm does not rely on the forward-backward filtering for smoothed regime probabilities, and only involves forward computation. Moreover, the algorithm to compute score is readily extended to compute the Hessian matrix.
Embedding Hallucination for Few-shot Language Fine-tuning

Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language model can cause severe over-fitting. In this paper, we propose an Embedding Hallucination (EmbedHalluc) method, which generates auxiliary embedding-label pairs to expand the fine-tuning dataset. The hallucinator is trained by playing an adversarial game with the discriminator, such that the hallucinated embedding is indiscriminative to the real ones in the fine-tuning dataset. By training with the extended dataset, the language learner effectively learns from the diverse hallucinated embeddings to overcome the over-fitting issue. Experiments demonstrate that our proposed method is effective in a wide range of language tasks, outperforming current fine-tuning methods. Further, we show that EmbedHalluc outperforms other methods that address this over-fitting problem, such as common data augmentation, semi-supervised pseudo-labeling, and regularization.
Diffusion Mechanism in Residual Neural Network: Theory and Applications

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy. Many existing deep learning approaches directly impose the fusion loss when training neural networks. In this work, inspired by the convection-diffusion ordinary differential equations (ODEs), we propose a novel diffusion residual network (Diff-ResNet), internally introduces diffusion into the architectures of neural networks. Under the structured data assumption, it is proved that the proposed diffusion block can increase the distance-diameter ratio that improves the separability of inter-class points and reduces the distance among local intra-class points. Moreover, this property can be easily adopted by the residual networks for constructing the separable hyperplanes. Extensive experiments of synthetic binary classification, semi-supervised graph node classification and few-shot image classification in various datasets validate the effectiveness of the proposed method.
Efﬁciently Generating Independent Replicates Directly from the Posterior Distribution for a Large Class of Bayesian Generalized Linear Mixed Effects Models

Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a signiﬁcant computational burden. This motivates us to consider ﬁnding closed-form expressions of the posterior distribution that are computationally straightforward to obtain independent replicates from directly. We focus on a broad class of Bayesian generalized linear mixed-effects models (GLMM) that allows one to jointly model data of different types. First, we derive a new class of distributions we refer to as the generalized conjugate multivariate (GCM) distribution. The GCM distribution’s theoretical development is similar to that of the CM distribution with two main dif-ferences; namely, (1) the GCM allows for multiple data types and prior distributional assumptions, and (2) the GCM explicitly accounts for hyperparameters through marginalization. The development of GCM is needed for to obtain independent replicates directly from the exact posterior distribution, which have an efﬁcient projection/regression form. Hence, we refer to our method as Exact Posterior Regression (EPR). Illustrative examples are provided including simulation studies and an analysis of multivariate spatial data from the U.S. Census Bureau’s American Community Survey (ACS).
Partial maximum correntropy regression for robust electrocorticography decoding

The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.
Applying Digital Rights Management to Corporate Information Systems

To fit the globalised economical environment, enterprises, and mostly SMEs, have to develop new networked and collaborative strategies, focusing on networked value creation (instead of the classical value chain vision), fitting the blue ocean context for innovative products and service development. Such collaborative networks are by now often based on trusted and well known communities. Developing large scale networked and collaborative strategies involve increasing both enterprise and information system agility and interoperability in order to allow their interconnection. This requires paying attention on an end-to end security and on the way information and process are used during their full life-cycle. As traditional security approaches and methodologies provide only an “instant” and rather static protection, they do not fit the dynamicity nor the life-cycle long protection constraints involved by such collaborative organisation. To overcome this limit, we propose to adapt the Digital Right Management approach (first defined for multimedia contents) to collaborative information systems. After proposing a semi-distributed architecture used to manage usage rights, we propose a security policy model including both usage rights and related obligations. This leads us to extend the security policy descriptions, including a dedicated syntax and semantics to model both policy organisation, usage and obligations before paying attention on the “collaborative environment constraints”. Paying attention on the way collaborative organisations are set and evolve, we have proposed an integration algebra to manage the way security and usage policies are composed depending on the way partners join and quit the collaborative context. This composition process and integration algebra analyse the collaborative business processes to identify the way policies are composed and negotiated. Lastly, we implement parts of our architecture to validate our proposals, mostly regarding the negotiation engine (using «SUN XACML implementation»), the aggregation engine(built upon JAVA DOM et JDOM) and a context analysis component.
DOMINANT FACTOR MODELLING METHOD FOR THE THERMAL SYSTEM OF POWER STATION

How to improve the economy during the operation of power plant is a big problem encountered by the electricity generation company.To solve this problem,establishing a unit economy analyzes system is an effective way.In this article,by introducinging dominant factor method,the general mechanism modeling method of thermal systems with the specific operating data of the object system is combined to build up an off-design mathematical model for the thermal system of a power plant,which can precisely simulate the object system's thermodynamic character under the condition of large scale off-design operation.This model not only provides a reliable standard for monitoring the performance of the object system,but could also be used to diagnose the system's fault.The modeling practice with the thermal system of a 135MW power plant proved the effectiveness of the proposed method.
Epidemics on multilayer simplicial complexes

Simplicial complexes describe the simple fact that in social networks a link can connect more than two individuals. As we show here, this has far-reaching consequences for epidemic spreading, in particular in the context of a multilayer network model, where one layer is a virtual social network and the other one is a physical contact network. The social network layer is responsible for the transmission of information via pairwise or higher order 2-simplex interactions among individuals, while the physical layer is responsible for the epidemic spreading. We use the microscopic Markov chain approach to derive the probability transition equations and to determine epidemic outbreak thresholds. We further support these results with Monte Carlo simulations, which are in good agreement, thus confirming the analytical tractability of the proposed model. We find that information transmission rates are frequently low when actual disease transmission rates in the physical network are low or medium, and we show that this can be mitigated effectively by introducing 2-simplex interactions in the social network. The relative ease of introducing higher-order interactions in virtual social networks means that this could be exploited to inhibit epidemic outbreaks.
No major cost of evolved survivorship in Drosophila melanogaster populations coevolving with Pseudomonas entomophila

Rapid exaggeration of host and pathogen traits via arms race dynamics is one possible outcome of host–pathogen coevolution. However, the exaggerated traits are expected to incur costs in terms of resource investment in other life-history traits. The current study investigated the costs associated with evolved traits in a host–pathogen coevolution system. We used the Drosophila melanogaster (host)–Pseudomonas entomophila (pathogen) system to experimentally derive two selection regimes, one where the host and pathogen both coevolved, and the other, where only the host evolved against a non-evolving pathogen. After 17 generations of selection, we found that hosts from both selected populations had better post-infection survivorship than controls. Even though the coevolving populations tended to have better survivorship post-infection, we found no clear evidence that the two selection regimes were significantly different from each other. There was weak evidence for the coevolving pathogens being more virulent than the ancestral pathogen. We found no major cost of increased post-infection survivorship. The costs were not different between the coevolving hosts and the hosts evolving against a non-evolving pathogen. We found no evolved costs in the coevolving pathogens. Thus, our results suggest that increased host immunity and pathogen virulence may not be costly.
A micromechanical model for phase-change composites

Phase-change composites have a wide range of tunable mechanical properties caused by temperature-driven phase transition, and have been widely applied in many cutting-edge fields like soft robotics. Previous studies on the effective mechanical properties of phase-change composites mostly use experimental methods, and there have been few theoretical approaches. In this work, we develop a micromechanical framework capable of tracking the effective mechanical properties of phase-change composites throughout the entire phase transition. The phase-change materials embedded in the composites are modelled as inclusions, and the non-phase-change materials are modelled as the matrix. This allows us to determine the effective mechanical properties of phase-change composites via the energy equivalency approach. Moreover, since the new phase will be generated inside the phase-change inclusions in the form of sub-inclusions during the phase transition, the inclusions are modelled as two-phase composites, and their effective mechanical properties are then determined using the Mori–Tanaka method. Finally, by comparing theoretical predictions with experimental data, the accuracy and reliability of the present model are verified. We believe that the proposed model can serve as a powerful tool for evaluating the effective mechanical properties of phase-change composites and provide theoretical guidelines for the design of advanced devices with tunable mechanical performance.
Tracking sexual dimorphism of facial width-to-height ratio across the lifespan: implications for perceived aggressiveness

The facial width-to-height ratio (FWHR) influences social judgements like perceived aggression. This may be because FWHR is a sexually dimorphic feature, with males having higher FWHR than females. However, evidence for sexual dimorphism is mixed, little is known about how it varies with age, and the relationship between sexual dimorphism and perceived aggressiveness is unclear. We addressed these gaps by measuring FWHR of 17 607 passport images of male and female faces across the lifespan. We found larger FWHR in males only in young adulthood, aligning with the stage most commonly associated with mate selection and intrasexual competition. However, the direction of dimorphism was reversed after 48 years of age, with females recording larger FWHRs than males. We then examined how natural variation in FWHR affected perceived aggressiveness. The relationship between FWHR and perceived aggressiveness was strongest for males at 27–33 and females at 34–61. Raters were most sensitive to differences in FWHR for young adult male faces, pointing to enhanced sensitivity to FWHR as a cue to aggressiveness. This may reflect a common mechanism for evaluating male aggressiveness from variability in structural (FWHR) and malleable (emotional expression) aspects of the face.
Exosome-derived small non-coding RNAs reveal immune response upon grafting transplantation in Pinctada fucata (Mollusca)

Exosomes, a subset of small extracellular vesicles, carry various nucleic acids, proteins, lipids, amino acids and metabolites. They function as a mode of intercellular communication and molecular transfer. Exosome cargo molecules, including small non-coding RNAs (sncRNAs), are involved in the immune response in various organisms. However, the role of exosome-derived sncRNAs in immune responses in molluscs remains unclear. Here, we aimed to reveal the sncRNAs involved in the immune response during grafting transplantation by the pearl oyster Pinctada fucata. Exosomes were successfully extracted from the P. fucata haemolymph during graft transplantation. Abundant microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs) were simultaneously discovered in P. fucata exosomes by small RNA sequencing. The expression patterns of the miRNAs and piRNAs at the grafting and initial stages were not substantially different, but varied significantly between the initial and later stages. Target prediction and functional analysis indicate that these miRNAs and piRNAs are related to immune response upon grafting transplantation, whereas piRNAs may also be associated with transposon silencing by targeting with genome transposon elements. This work provides the basis for a functional understanding of exosome-derived sncRNAs and helps to gain further insight into the PIWI/piRNA pathway function outside of germline cells in molluscs.
Research Exposed: How Empirical Social Science Gets Done in the Digital Age

Bourdieu, Pierre. 1986. ‘‘The Forms of Capital. ’’ Pp. 241–58 in Handbook of Theory and Research for the Sociology of Education, edited by J. Richardson. Westport, CT: Greenwood. Durkheim, Émile. 2008. The Elementary Forms of Religious Life. Abridged edition. Oxford: Oxford University Press. Habermas, Jürgen. 1974. ‘‘The Public Sphere: An Encyclopedia Article (1964). ’’ New German Critique 3:49–55. Zuboff, Shoshana. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: Public Affairs.
Android Wireless Application Development Volume I: Android Essentials

Android Wireless Application Development has earned a reputation as the most useful real-world guide to building robust, commercial-grade Android apps. Now, authors Lauren Darcey and Shane Conder have systematically revised and updated this guide for the latest Android SDK 4.0. To accommodate their extensive new coverage, theyve split the book into two volumes. Volume I focuses on Android essentials, including setting up your development environment, understanding the application lifecycle, designing effective user interfaces, developing for diverse devices, and optimizing your mobile app development process--from design through publishing. Every chapter has been thoroughly updated for the newest APIs, tools, utilities, and hardware. All sample code has been overhauled and tested on leading devices from multiple companies, and many new examples have been added. Drawing on decades of in-the-trenches experience as professional mobile developers, Darcey and Conder provide valuable new best practices--including powerful techniques for constructing more portable apps. This new edition contains full chapters on Android manifest files, content providers, effective app design, and testing; an all-new chapter on tackling compatibility issues; coverage of todays most valuable new Android tools and utilities; and even more exclusive tips and tricks. An indispensable resource for every Android development team member.
The pre-farrowing behaviour of sows with straw and space for locomotion

Pre-farrowing behaviour in sows consists of a phase of increased restlessness and locomotion, and a phase of nest-building. The aim of the study described here was to determine how the presentation of straw, a relevant nest-building material, affected the expression of pre-farrowing restlessness. This would provide information on the relative contribution of internal and external stimuli to the expression of pre-fatrowing behaviour which may allow us to understand better how confinement around farrowing affects the welfare of sows. In treatment 1, six sows were given access to straw on the floor of a 2 X 2 m pen (which had previously been shown to be a preferred farrowing site) in one comer of a 6.5 X 7 m test arena. In treatment 2, twelve sows were given access to straw in a hopper in the opposite comer of the test arena from the home pen. All sows were observed for 8 h per day from 4 days before farrowing was expected until farrowing actually occurred. In treatment 1, sows did not show a significant increase in locomotion compared to previous days (P 2 0.05). However in treatment 2, when straw was provided in a hopper, the sows showed a significant increase in locomotion on the day before farrowing (P s 0.01). In treatment 2, five of the sows carried straw from the hopper to another location to use in their nest-building. The pattern of locomotion in the hours before farrowing shown by sows that carried straw was different from those that did not (P S 0.0011, although there was no overall difference in the distance walked (P 2 0.05). The results indicate that both internal and external factors affect the performance of pre-farrowing locomotion. In addition, it was also concluded that the motivation to perform nest-building behaviour is not particularly substrate-specific, and appears to be redirected at pen fixtures in the absence of more suitable material.
Redescription of Diastolaimus grossus (Truskova & Eroshenko, 1977) Andrássy, 1984 (Rhabditida, Chambersiellidae) from Europe and comments on its phylogenetic position

Abstract Two populations of the species Diastolaimus grossus have been obtained from bark of trees in Bosnia and Herzegovina and the Czech Republic. The species is described and characterized in detail using morphological techniques (light and electron scanning microscopy) and morphometrical (Gower General Similarity coefficient of morphological characters) and molecular analyses, including phylogenetic analysis of all related and already sequenced species of the family Chambersiellidae. Morphological and molecular analyses, based on 18S and 28S ribosomal DNA sequences, show that the family Chambersiellidae is polyphyletic, being the subfamily Chambersiellinae related with Cephalobomorpha and Tylenchomorpha, and the subfamily Macrolaiminae is located into Panagrolaimomorpha. The genus Diastolaimus, previously belonging to Macrolaiminae, is transferred to Chambersiellinae. Diastolaimus mexicanus is proposed as a junior synonym of D. grossus.