Miracle-dz/UniFAR-FTUs huggingface dataset:
TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources

Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Still, it is difficult to efficiently train deep SNNs due to the non-differentiability of its activation function, which disables the typically used gradient descent approaches for traditional artificial neural networks (ANNs). Although the adoption of surrogate gradient (SG) formally allows for the back-propagation of losses, the discrete spiking mechanism actually differentiates the loss landscape of SNNs from that of ANNs, failing the surrogate gradient methods to achieve comparable accuracy as for ANNs. In this paper, we first analyze why the current direct training approach with surrogate gradient results in SNNs with poor generalizability. Then we introduce the temporal efficient training (TET) approach to compensate for the loss of momentum in the gradient descent with SG so that the training process can converge into flatter minima with better generalizability. Meanwhile, we demonstrate that TET improves the temporal scalability of SNN and induces a temporal inheritable training for acceleration. Our method consistently outperforms the SOTA on all reported mainstream datasets, including CIFAR-10/100 and ImageNet. Remarkably on DVS-CIFAR10, we obtained 83$\%$ top-1 accuracy, over 10$\%$ improvement compared to existing state of the art. Codes are available at \url{https://github.com/Gus-Lab/temporal_efficient_training}.
Semidefinite games

We introduce and study the class of semidefinite games, which generalizes bimatrix games and finite $N$-person games, by replacing the simplex of the mixed strategies for each player by a slice of the positive semidefinite cone. For semidefinite two-player zero-sum games, we show that the optimal strategies can be computed by semidefinite programming. Furthermore, we show that two-player semidefinite zero-sum games are almost equivalent to semidefinite programming, generalizing Dantzig's result on the almost equivalence of bimatrix games and linear programming. For general two-player semidefinite games, we prove a spectrahedral characterization of the Nash equilibria. Moreover, we give constructions of semidefinite games with many Nash equilibria. In particular, we give a construction of semidefinite games whose number of connected components of Nash equilibria exceeds the long standing best known construction for many Nash equilibria in bimatrix games, which was presented by von Stengel in 1999.
Measurement of Entangled States Before, During, and After a Proposed Entangled Two-Photon Molecular Excitation

Entangled photons are predicted to linearize and enhance the probability of multiphoton processes. We construct an interferometer to measure the quantum state before, during, and after an entangled excitation and distinguish it from one-photon processes. Comments and questions should be directed to the Optica Conference Papers staff (tel: +1 202.416.6191, e-mail: cstech@optica.org).
Counting Temporal Paths

The betweenness centrality of a vertex v is an important centrality measure that quantifies how many optimal paths between pairs of other vertices visit v. Computing betweenness centrality in a temporal graph, in which the edge set may change over discrete timesteps, requires us to count temporal paths that are optimal with respect to some criterion. For several natural notions of optimality, including foremost or fastest temporal paths, this counting problem reduces to #Temporal Path, the problem of counting all temporal paths between a fixed pair of vertices; like the problems of counting foremost and fastest temporal paths, #Temporal Path is #P-hard in general. Motivated by the many applications of this intractable problem, we initiate a systematic study of the prameterised and approximation complexity of #Temporal Path. We show that the problem presumably does not admit an FPT-algorithm for the feedback vertex number of the static underlying graph, and that it is hard to approximate in general. On the positive side, we proved several exact and approximate FPT-algorithms for special cases.
Effect Identification in Cluster Causal Diagrams

One pervasive task found throughout the empirical sciences is to determine the effect of interventions from non-experimental data. It is well-understood that assumptions are necessary to perform causal inferences, which are commonly articulated through causal diagrams (Pearl, 2000). Despite the power of this approach, there are settings where the knowledge necessary to specify a causal diagram over all observed variables may not be available, particularly in complex, high-dimensional domains. In this paper, we introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) that allows for the partial speciﬁcation of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. A C-DAG speciﬁes relationships between clusters of variables, while the relationships between the variables within a cluster are left unspeciﬁed. We develop the foundations and machinery for valid causal inferences over C-DAGs. In particular, we ﬁrst deﬁne a new version of the d-separation criterion and prove its soundness and completeness. Secondly, we extend these new separation rules and prove the validity of the corresponding do-calculus. Lastly, we show that a standard identi-ﬁcation algorithm is sound and complete to systematically compute causal effects from observational data given a C-DAG.
Blind Reverberation Time Estimation in Dynamic Acoustic Conditions

The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. In many scenarios, acoustic conditions change over time which in turn requires the estimate to be updated continuously. Previously proposed methods involving deep neural networks were mostly designed and tested under the assumption of static acoustic conditions. In this work, we show that these approaches can perform poorly in dynamically evolving acoustic environments. Motivated by a recent trend towards data-centric approaches in machine learning, we propose a novel way of generating training data and demonstrate, using an existing deep neural network architecture, the considerable improvement in the ability to follow temporal changes in reverberation time.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deﬁciency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for ﬁtting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the eﬀectiveness of PVI in a variety of federated settings.
Truncated LinUCB for Stochastic Linear Bandits

This paper considers contextual bandits with a ﬁnite number of arms, where the contexts are independent and identically distributed d -dimensional random vectors, and the expected rewards are linear in both the arm parameters and contexts. The LinUCB algorithm, which is near minimax optimal for related linear bandits, is shown to have a cumulative regret that is suboptimal in both the dimension d and time horizon T , due to its over-exploration. A truncated version of LinUCB is proposed and termed “Tr-LinUCB”, which follows LinUCB up to a truncation time S and performs pure exploitation afterwards. The Tr-LinUCB algorithm is shown to achieve O ( d log( T )) regret if S = Cd log( T ) for a suﬃciently large constant C , and a matching lower bound is established, which shows the rate optimality of Tr-LinUCB in both d and T under a low dimensional regime. Further, if S = d log κ ( T ) for some κ > 1, the loss compared to the optimal is a multiplicative log log( T ) factor, which does not depend on d . This insensitivity to overshooting in choosing the truncation time of Tr-LinUCB is of practical importance.
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning

Real economies can be modeled as a sequential imperfect-information game with many heterogeneous agents, such as consumers, firms, and governments. Dynamic general equilibrium (DGE) models are often used for macroeconomic analysis in this setting. However, finding general equilibria is challenging using existing theoretical or computational methods, especially when using microfoundations to model individual agents. Here, we show how to use deep multi-agent reinforcement learning (MARL) to find $\epsilon$-meta-equilibria over agent types in microfounded DGE models. Whereas standard MARL fails to learn non-trivial solutions, our structured learning curricula enable stable convergence to meaningful solutions. Conceptually, our approach is more flexible and does not need unrealistic assumptions, e.g., continuous market clearing, that are commonly used for analytical tractability. Furthermore, our end-to-end GPU implementation enables fast real-time convergence with a large number of RL economic agents. We showcase our approach in open and closed real-business-cycle (RBC) models with 100 worker-consumers, 10 firms, and a social planner who taxes and redistributes. We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models. As such, we show that hardware-accelerated MARL is a promising framework for modeling the complexity of economies based on microfoundations.
Complex topological soliton with real energy in particle physics

We summarise the procedure used to find the classical masses of Higgs particle, massive gauge boson and t'Hooft-Polyakov monopole in non-Hermitian gauge field theory. Their physical regions are explored, and the mechanism of the real value of the monopole solution is analysed in different physical regions.
Interpretable Unsupervised Diversity Denoising and Artefact Removal

Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions. Unsupervised diversity restoration, that is, obtaining a diverse set of possible restorations given a corrupted image, is important for ambiguity removal in many applications such as microscopy where paired data for supervised training are often unobtainable. In real world applications, imaging noise and artefacts are typically hard to model, leading to unsatisfactory performance of existing unsupervised approaches. This work presents an interpretable approach for unsupervised and diverse image restoration. To this end, we introduce a capable architecture called Hierarchical DivNoising (HDN) based on hierarchical Variational Autoencoder. We show that HDN learns an interpretable multi-scale representation of artefacts and we leverage this interpretability to remove imaging artefacts commonly occurring in microscopy data. Our method achieves state-of-the-art results on twelve benchmark image denoising datasets while providing access to a whole distribution of sensibly restored solutions. Additionally, we demonstrate on three real microscopy datasets that HDN removes artefacts without supervision, being the first method capable of doing so while generating multiple plausible restorations all consistent with the given corrupted image.
Conditionally Gaussian PAC-Bayes

Recent studies have empirically investigated different methods to train stochastic neural networks on a classification task by optimising a PAC-Bayesian bound via stochastic gradient descent. Most of these procedures need to replace the misclassification error with a surrogate loss, leading to a mismatch between the optimisation objective and the actual generalisation bound. The present paper proposes a novel training algorithm that optimises the PAC-Bayesian bound, without relying on any surrogate loss. Empirical results show that this approach outperforms currently available PAC-Bayesian training methods.
The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms

A major challenge in obtaining evaluations of products or services on e-commerce platforms is eliciting informative responses in the absence of verifiability. This paper proposes the square root agreement rule (SRA): a simple reward mechanism that incentivizes truthful responses to objective evaluations on such platforms. In this mechanism, an agent gets a reward for an evaluation only if the answer matches that of a peer agent, where this reward is inversely proportional to a popularity index of the answer. This index is defined to be the square root of the empirical frequency at which any two agents performing the same evaluation agree on the particular answer across evaluations of similar entities operating on the platform. Rarely agreed-on answers thus earn a higher reward than answers for which agreements are relatively more common. We show that in the many tasks regime, the truthful equilibrium under SRA is strictly payoff-dominant across large classes of natural equilibria that could arise in these settings, thus increasing the likelihood of its adoption. Although there exist other mechanisms achieving such guarantees, they either impose additional assumptions on the response distribution that are not generally satisfied for objective evaluations or they incentivize truthful behavior only if each agent performs a prohibitively large number of evaluations and commits to using the same strategy for each evaluation. SRA is the first known incentive mechanism satisfying such guarantees without imposing any such requirements. Moreover, our empirical findings demonstrate the robustness of the incentive properties of SRA in the presence of mild subjectivity or observational biases in the responses. These properties make SRA uniquely attractive for administering reward-based incentive schemes (e.g., rebates, discounts, reputation scores, etc.) on online platforms. This paper was accepted by Kartik Hosanagar, information systems.
A New Salamander of the Genus Onychodactylus from Central Honshu, Japan (Amphibia, Caudata, Hynobiidae)

Abstract: The number of species of salamanders of the genus Onychodactylus in Japan has increased from one to six nominal species over the past decade, by taxonomic studies based not only on morphological but also molecular phylogenetic data. However, the presence of an additional cryptic species has been suggested by a series of previous studies. Here, we describe a new species, Onychodactylus pyrrhonotus sp. nov., from the Kinki, Chubu and Hokuriku Districts of central Honshu, Japan, based on morphological and genetic evidence. It belongs to the O. japonicus species complex and is the sister species of O. kinneburi from Shikoku Island and westernmost part of Honshu with 5.5% sequence divergence in the mitochondrial cytochrome b gene. The range of the new species completely overlaps with that of O. japonicus sensu stricto, and they are syntopic in at least six known localities. As shown in a previous study, reproductive isolation between these sympatric species is substantial. The new species is morphologically most easily distinguished from congeners by coloration, a sharply defined scarlet to pinkish dorsal stripe or mottling on a black background and dusty white dots on the venter.
Identification of Inertial Parameters of Manipulators Based on Wrist Force Sensor

An on-line method for identification of inertial parameters of a manipulator's end-effector is presented in this paper. The method is based on the output of a 6-axis wrist force sensor. The truly reveals the force transfer relations in a manipulator system, so the manipulator's joint characteristics are considered and the imaginary model problem is solved perfectly with the method. At the same time, the dynamic characteristics of 6-axis wrist force sensor in the manipulator are included. The presented method can also be used for the identification of inertial parameters of unknown workpiece or tool clutched by a manipulator. It is important for a manipulator that often changes its execution object.
Selection of appropriate reference genes in different tissues of Vaccinium dunalianum Wight by quantitative real-time PCR for gene expression studies

Abstract: Quantitative real-time polymerase chain reaction (PCR) is a powerful tool for investigating relevant changes in gene expression. At present, there are no reports on reference genes for Vaccinium dunalianum Wight (V. dunalianum Wight). Here, the expression profiles of 15 frequently used reference genes were used to screen for appropriate reference genes in different tissues of V. dunalianum Wight collected in Yunnan and Wuding, China. The cycle threshold (Ct) values of genes in different samples were compared and analyzed. Furthermore, BestKeeper, NormFinder, and geNorm software were used to evaluate the stability of each candidate reference gene. Analysis using geNorm showed that the expression levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and protein phosphatase 2A-2 (PP2A-2) were the most stable. In turn, the expression level of eukaryotic initiation factor-2 (EIF-4A-2) was the most stable as per analysis by NormFinder, while Bestkeeper revealed that actin-related protein-1 (ARP-1) was the most stably expressed. Comprehensive analysis and validation experiments showed that EIF-4A-2 and PP2A-2 were the most suitable reference genes for V. dunalianum Wight.
Phylogeny of Courtship and Male-male Combat Behavior in Snakes: An Updated Analysis

Abstract: To elucidate phylogenetic patterns in courtship and male-male combat (MMC) behaviors in snakes, I plotted behavioral characters (BCs) onto phylogenetic trees of snakes. For courtship, I plotted 33 BCs onto a phylogenetic tree of 155 snake species in 12 families. For MMC, I plotted 21 BCs onto a phylogenetic tree of 128 snake species in 11 families. For courtship, results suggest the following. Spur Contact and Spur Movement are ancestral for the clade Pythonidae+Boidae and were present as early as the Paleocene Epoch. The Jerk, Chin-Rub, and Dorsal Advance are ancestral for the clade Endoglyptodonta and were present by the late Oligocene. A coital Bite was added in the colubrid clade Zamenis+Lampropeltini by the early Miocene. For MMC, results suggest the following. The Coil, Hook, Type 2 Head Raise, and Downward Push are ancestral for the clade Afrophidia and were present by the early Paleocene. The Type 1 Head Raise replaced the Type 2 Head Raise in Micrurus by the mid-Miocene and in the Lampropeltini by the early Miocene. In the Lampropeltini, Body Bowing was added and Pinning the Head replaced the Type 2 Head Raise by the early Miocene. In addition to written literature, this study utilized a large amount of documentation in the form of video footage on YouTube. For many species, the footage documents behaviors that were unrecorded in the written literature. The results demonstrate that social media can be a highly useful data source in the scientific study of animal behavior.
Neural Sociometrics: Toward Early Screening of Infant Psychosocial and Brain Health to Improve Lifelong Mental Well-Being

Infant development depends on warm, responsive social interactions that richly stimulate the senses, acting through multiple pathways to orchestrate healthy maturation of the neonatal brain, mind, and body. Conversely, adverse early experiences seed vulnerabilities for poor cognition and emotional instability. Although we routinely measure many aspects of infant physical health (hearing, weight), no clinical tools currently exist to measure early psychosocial health and brain development. Here, neural sociometrics (real-time multi-sensor imaging of adult–infant social interactive behavior and neurophysiology) is discussed as one possible precision measurement framework. Early psychosocial health screening, paired with precision therapeutics, could fundamentally alter a child's development trajectory toward lifelong mental well-being and productivity. Further, population-level measurements of social brain health could forecast mental capital growth (and deficits) for entire communities and generations. This article calls for the prioritized development of early scalable diagnostic instruments to reveal the status of infant mental wellbeing and brain health.
Poly(lactic acid-co-glycolic acid)-based celecoxib extended-release microspheres for the local treatment of traumatic heterotopic ossification

Traumatic heterotopic ossification (THO) is a serious and common clinical post-traumatic complication for which there is no effective and safe drug treatment. Routine administration of nonsteroidal anti-inflammatory drugs (NSAIDs) after injury is extensively used approach for THO. However, serious adverse events can occur in the event of an overdose of NSAIDs. In our study, we have developed a poly(lactic acid-co-glycolic acid) (PLGA) microsphere by emulsifying solvent volatilization for the prolonged slow delivery of celecoxib (CLX). Three groups of celecoxib-poly(lactic acid-co-glycolic acid) microspheres (CLX–PLGA MPs) were prepared with particle sizes of 3.75±1.28 μm, 49.56±17.15 μm, and 94.98±42.53 μm. Meanwhile, related parameters of microspheres in each group were studied: drug loading (DL), encapsulation rate (EE), and slow-release behavior. The DL and EE of the 3 CLX–PLGA MPs did not vary significantly, and subsequently, we selected the second drug loading microspheres with a retardation period of about 70 days for subsequent experiments. Moreover, cellular and animal experiments suggest that the microspheres are biocompatible and can be safely applied to localized trauma tissue. Finally, it is demonstrated that CLX–PLGA MPs have an effect on inhibiting the osteogenic differentiation of bone marrow mesenchymal stem cells and have the potential to inhibit ectopic bone formation of the THO model in Sprague-Dawley rat. Therefore, this study suggests that CLX–PLGA MPs are expected to be applied topically in the early post-traumatic period to prevent the development of THO.