SJ-Tree #REFR materializes all the partial matches of subgraphs, leading to a huge storage and index update cost.

Most state-space based representation models (that is, each state represents an intermediate result) use this framework, e.g., Ri #OTHEREFR , VF2++ #OTHEREFR , Graphql #OTHEREFR , CFL #OTHEREFR , CECI #OTHEREFR , and DP-ISO #OTHEREFR .

Due to the effective filtering and sorting methods, they have achieved great improvements in overall performance.

However, these algorithms focus on the problem in a static data graph.

For streaming graphs, it is unacceptable for the real-time requirement to perform subgraph matching over the graphs prior to and after updating respectively. Continuous subgraph matching.

IncIsoMat #OTHEREFR and Graphflow #OTHEREFR expand matches from the updated edges without any additional index structures, showing limited real-time performance.

TurboFlux #OTHEREFR employs a concise structure DCG, a complete multigraph, to represent intermediate results, reducing the storage cost.

Similarly, CEPDG #OTHEREFR employs TreeMat to store the partial matches of trees.

It supports changes in both pattern graphs and data graphs.

However, TurboFlux and CEPDG exploit a spanning tree of the query graph to filter the candidates, leaving the non-tree edges to be maintained by DCG.

SymBi #OTHEREFR introduces an auxiliary data structure DCS to store weak embeddings of directed acyclic graphs as intermediate results.

Fast Continuous Subgraph Matching Over Streaming Graphs Via Backtracking Reduction

Abstract: Streaming graphs are drawing increasing attention in both academic and industrial communities as many graphs in real applications evolve over time. Continuous subgraph matching (shorted as CSM) aims to report the incremental matches of a query graph in such streaming graphs. It involves two major steps, i.e., candidate maintenance and incremental match generation, to answer CSM. Throughout the course of continuous subgraph matching, incremental match generation backtracking over the search space dominates the total cost. However, most previous approaches focus on developing techniques for efficient candidate maintenance, while incremental match generation receives less attention despite its importance in CSM. Aiming to minimize the overall cost, we propose two techniques to reduce backtrackings in this paper. We present a cost-effective index CaLiG that yields tighter candidate maintenance, shrinking the search space of backtracking. In addition, we develop a novel incremental matching paradigm KSS that decomposes the query vertices into conditional kernel vertices and shell vertices. With the matches of kernel vertices, the incremental matches can be produced immediately by joining the candidates of shell vertices without any backtrackings. Benefiting from reduced backtrackings, the elapsed time of CSM decreases significantly. Extensive experiments over real graphs show that our method runs faster than the state-of-the-art algorithm orders of magnitude.

A Selectivity Based Approach To Continuous Pattern Detection In Streaming Graphs

Abstract: Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.


To address this issue, we propose an alternative fine-tuning approach inspired by the concept of gradual unfreezing as introduced in #REFR .

Low amounts of labeled data and short sample lengths (e.g., tweets) can cause unstable Encoder Transformer model fine-tuning in regression tasks #OTHEREFR .

Head-First Fine-Tuning (HeFiT) is a two-step procedure to fine-tune Encoder Transformers used for regression.

We add dropout to the hidden layer of the head.

In the first step the embedding layers are frozen and the regression head is fine-tuned for three epochs.

In the second step, the entire model is unfrozen and fine-tuned for six epochs with the learning rate halved.

The rationale behind the approach is that the random weight initialization of the regression head in the regular fine-tuning may lead to inaccurate signals being propagated back to the embedding layers, resulting in degradation of the pre-trained model. However, our investigation extends beyond HeFiT.

Uzh_Clyp At Semeval-2023 Task 9: Head-First Fine-Tuning And Chatgpt Data Generation For Cross-Lingual Learning In Tweet Intimacy Prediction

Abstract: This paper describes the submission of UZH_CLyp for the SemEval 2023 Task 9 "Multilingual Tweet Intimacy Analysis". We achieved second-best results in all 10 languages according to the official Pearson's correlation regression evaluation measure. Our cross-lingual transfer learning approach explores the benefits of using a HeadFirst Fine-Tuning method (HeFiT) that first updates only the regression head parameters and then also updates the pre-trained transformer encoder parameters at a reduced learning rate. Additionally, we study the impact of using a small set of automatically generated examples (in our case, from ChatGPT) for low-resource settings where no human-labeled data is available. Our study shows that HeFiT stabilizes training and consistently improves results for pretrained models that lack domain adaptation to tweets. Our study also shows a noticeable performance increase in cross-lingual learning when synthetic data is used, confirming the usefulness of current text generation systems to improve zero-shot baseline results. Finally, we examine how possible inconsistencies in the annotated data contribute to cross-lingual interference issues.

Universal Language Model Fine-Tuning For Text Classification

Abstract: Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100x more data. We open-source our pretrained models and code.


Furthermore, our results are non-asymptotic #REFR with explicit probabilistic bounds on the approximation errors.

In particular, when rows are random vectors with a certain prior distribution (e.g., they are i.i.d.

according to a common F -variate distribution), we are in the presence of a Bayesian version of the MMV-CS problem, for which the MMV-AMP algorithm and its variants have been proposed, analyzed, and widely used in applications #OTHEREFR .

In the case U > 1 and F > 1, where the X u 's have row distribution that may differ for different indices u, the estimation problem in (1) is new and does not reduces to any of the previously considered cases.

Our main contribution is the high-dimensional analysis of the AMP algorithm devised for the observation model #OTHEREFR .

Instead of the usual Bolthausen's conditioning proof technique #OTHEREFR , our AMP analysis here is based on the recent AMP proof strategy of #OTHEREFR , which leads to a more straightforward derivation of the state-evolution analysis.

In addition, we have presented the RS (replica-symmetric) predictions of the high-dimensional limits of the normalized input-output mutual information and the MMSE (minimum mean-square-error).

We organize the paper as follows: In Section 2 we present our theoretical contributions mentioned above.

Section 3 is devoted to the problem of unsourced random access in cell-free systems, which is the motivating application of the observation model #OTHEREFR , and provides some numerical results. We conclude the paper in Section 4. The proofs are relegated to Appendix.

Inference In Linear Observations With Multiple Signal Sources: Analysis Of Approximate Message Passing And Applications To Unsourced Random Access In Cell-Free Systems

Abstract: Here we consider a problem of multiple measurement vector (MMV) compressed sensing with multiple signal sources. The observation model is motivated by the application of unsourced random access in wireless cell-free MIMO (multiple-input-multiple-output) networks. We present a novel (and rigorous) high-dimensional analysis of the AMP (approximate message passing) algorithm devised for the model. As the system dimensions in the order, say O(L), tend to infinity, we show that the empirical dynamical order parameters-describing the dynamics of the AMP-converge to deterministic limits (described by a state-evolution equation) with the convergence rate O(L − 1 2). Furthermore, we have shown the asymptotic consistency of the AMP analysis with the replica-symmetric calculation of the static problem. In addition, we provide some interesting aspects on the unsourced random access (or initial access) for cell-free systems, which is the application motivating the observation model.

Finite-Sample Analysis Of Approximate Message Passing

Abstract: This paper studies the performance of Approximate Message Passing (AMP), in the regime where the problem dimension is large but finite. We consider the setting of high-dimensional regression, where the goal is to estimate a high-dimensional vector β 0 from an observation y = Aβ 0 + w. AMP is a low-complexity, scalable algorithm for this problem. It has the attractive feature that its performance can be accurately characterized in the asymptotic large system limit by a simple scalar iteration called state evolution. Previous proofs of the validity of state evolution have all been asymptotic convergence results. In this paper, we derive a concentration result for AMP with i.i.d. Gaussian measurement matrices with finite dimension n × N. The result shows that the probability of deviation from the state evolution prediction falls exponentially in n. Our result provides theoretical support for empirical findings that have demonstrated excellent agreement of AMP performance with state evolution predictions for moderately large dimensions.

explicit probabilistic bounds
approximation errors


This gives rise to a parametrization of the feedback gains as a linear combination of the collected data and allows to perform direct data-driven control design through semi-definite programming (SDP) #REFR .

Furthermore, each method may be classified into either a certainty-equivalence (CE) or robust approach, depending on whether uncertainties are taken into account.

The indirect or sysID approach has been long studied in the general setting #OTHEREFR , #OTHEREFR with methods such as subspace identification with broader applications to filtering and state estimation #OTHEREFR .

For optimization-based control, such as the linear quadratic regular (LQR), there are many works that use the indirect approach #OTHEREFR - #OTHEREFR .

Similarly, the direct approach to data-driven LQR has been approached using behavioral methods #OTHEREFR , gradient-based methods #OTHEREFR , #OTHEREFR , and Riccatibased methods #OTHEREFR .

More recently, this problem has been solved using concepts from behavioral systems theory and subspace methods #OTHEREFR using Willems' Fundamental Lemma, which characterizes the trajectory of an LTI system through the range space of the input/output data matrix #OTHEREFR .

To this end, the authors in #OTHEREFR , #OTHEREFR were able to come up with a CE and regularized design, thus bridging the gap between the indirect and direct approaches.

All these optimization-based approaches to direct datadriven LQR design are done in the deterministic setting, assuming a single realization of the state trajectories from x 0 to x f .

To the best of our knowledge, this paper is the first work that looks at the problem where the state is a stochastic process instead of a deterministic trajectory.

Since it is still unclear how to perform data-driven designs in the context of process noise, we limit our analysis to uncertainties in the initial and final states, leaving the case of process noise for future investigation.

Specifically, we assume that the boundary values of the state must follow a normal distribution, and the objective is to steer the entire distribution of states from an initial to a final one.

Data-Driven Covariance Steering Control Design

Abstract: This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowledge of the system dynamics. This data-driven control framework uses data collected from the input and the state and utilizes the seminal work by Willems et al. to construct a databased parametrization of the mean and the covariance control problems. These problems are then solved to optimality as convex programs using standard techniques from the covariance control literature. We also discuss the equivalence of indirect and direct data-driven covariance steering designs, as well as a regularized version of the problem that provides a balance between the two. We illustrate the proposed framework through a set of randomized trials on a double integrator system and show that the results match up almost exactly with the corresponding model-based method in the noiseless case. We then analyze the robustness properties of the data-free and data-driven covariance steering methods and demonstrate the trade-offs between performance and optimality among these methods in the presence of data corrupted with exogenous noise.

Data-Driven Linear Quadratic Regulation Via Semidefinite Programming

Abstract: This paper studies the finite-horizon linear quadratic regulation problem where the dynamics of the system are assumed to be unknown and the state is accessible. Information on the system is given by a finite set of input-state data, where the input injected in the system is persistently exciting of a sufficiently high order. Using data, the optimal control law is then obtained as the solution of a suitable semidefinite program. The effectiveness of the approach is illustrated via numerical examples.

direct data-driven control
semi-definite programming


We represent the loyalty attribute and its value in a matrix form ; ∈ R (2× ) , where is loyalty attribute and is the value and feed it to a CNN to obtain the vector representation #REFR .

So, we are interested to know about our customer/user loyalty information when they buy from our websites.

To identify the loyalty embeddings we focus on users shop, genre, hotel and targeted brand loyalty attribute.

If a user buys an item from a shop more than five times, we include that shop name under user shop loyalty attributes.

That means one user can be loyal to multiple shops, genres and so on.

To capture the loyalty embeddings, we utilize a simple convolutional neural network (CNN) to extract features from the loyalty attributes.

The score function would be ( , , ) = − − ( ; ) and minimize the following objective function, = ( , , ) ∈ log(1 + exp(− ( , , ))).

Here, denotes the set of real loyalty attribute triples and is the user's loyalty view embedding.

Exploring 360-Degree View Of Customers For Lookalike Modeling

Abstract: Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers' different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.

Multi-View Knowledge Graph Embedding For Entity Alignment

Abstract: We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.


Random objects are sampled from LVIS datasets #REFR , combined with a pre-defined template, creating a metaprompt.

The spatial prompts are collected using a pre-defined template, where a wide range of relations is utilized, e.g., "on the right of", "above", and "between." For attribute-binding, two attributes are exploited, i.e., colors and size.

For each hardness level, the number of objects' compositions increased, ranging from 2 to 4.

For the action-level compositional generation, we design prompts with multiple combinations of actions starting from ComCLIP #OTHEREFR .

We combine two sentences from ComCLIP #OTHEREFR for the easy level.

Then, for medium and hard levels, we randomly choose one sentence and feed 'Extend text to let the subject have at least three actions.' and 'Extend text with other subjects doing other actions' into GPT-3.5, respectively, to obtain the final prompt. Detailed examples are demonstrated in Figure 3 . -Bias.

Then, the meta-prompt is fed to GPT-3.5 to produce the final prompt, as depicted in Figure 3 .

To ensure the prompts are agnostic towards the protected attributes, i.e., gender, race, and age, we manually validate them. -Fairness. We adapt the bias prompts.

For gender fairness, we replace gender-agnostic words, such as a person, with gender-specific words, such as man and woman.

Whereas for style fairness, a style indicator is appended to the beginning of the bias prompt, as shown in Figure 3

Hrs-Bench: Holistic, Reliable And Scalable Benchmark For Text-To-Image Models

Abstract: In recent years, Text-to-Image (T2I) models have been extensively studied, especially with the emergence of diffusion models that achieve state-of-the-art results on T2I synthesis tasks. However, existing benchmarks heavily rely on subjective human evaluation, limiting their ability to holistically assess the model's capabilities. Furthermore, there is a significant gap between efforts in developing new T2I architectures and those in evaluation. To address this, we introduce HRS-Bench, a concrete evaluation benchmark for T2I models that is Holistic, Reliable, and Scalable. Unlike existing benchmarks that focus on limited aspects, HRS-Bench measures 13 skills that can be categorized into five major categories: accuracy, robustness, generalization, fairness, and bias. In addition, HRS-Bench covers 50 scenarios, including fashion, animals, transportation, food, and clothes. We evaluate nine recent large-scale T2I models using metrics that cover a wide range of skills. A human evaluation aligned with 95% of our evaluations on average was conducted to probe the effectiveness of HRS-Bench. Our experiments demonstrate that existing models often struggle to generate images with the desired count of objects, visual text, or grounded emotions. We hope that our benchmark help ease future text-to-image generation research. The code and data are available at https://eslambakr.github.io/hrsbench.github.io/.

Lvis: A Dataset For Large Vocabulary Instance Segmentation

Abstract: Progress on object detection is enabled by datasets that focus the research community’s attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced ‘el-vis’): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect 2.2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org.


Examples Of Machine Learning In Multiscale Computational Modeling 2.1 Computational Mechanics 2.1.1 Fluid Modeling

The analysis of fluid mechanics data still relies largely on domain expertise and traditional statistical analysis, which involve many simplifying assumptions #REFR .

The intersection of ML and fluid mechanics presents a natural synergy, owing to their similarity in dealing with large and complex datasets.

In traditional fluid mechanics, one has to deal with a large amount of data coming from various source, including experiments, field measurements or large-scale simulation.

Recently, the rise of ML, especially DL, has been a significant driving force in fluid mechanics research, rendering opportunities for tackling more difficult fluid mechanics problems such as turbulent flows and its modeling.

By leveraging the computational efficiency of ML, advancements in fluid flow modeling can be achieved, which can have a profound impact on a wide range of applications, such as biomedical engineering, aerospace engineering, and aerial/hydrorobotics and autonomous systems, among others.

In fact, DL techniques such as artificial neural networks (ANN) have been employed to simulate fluid dynamics very early, since the 1990s #OTHEREFR , #OTHEREFR and are still being developed #OTHEREFR .

Along with the breakthrough in DL architecture development, the progress in fluid flow modeling has been significantly accelerated.

ML approaches such as recurrent neural networks (RNNs) have been utilized to model flows in extreme environments #OTHEREFR , #OTHEREFR Unlike traditional feedforward neural networks, RNNs have loops that allow information to be passed from one step of the network to the next, enabling them to maintain information about previous inputs and influence the current output.

Challenges And Opportunities For Machine Learning In Multiscale Computational Modeling

Abstract: Many mechanical engineering applications call for multiscale computational modeling and simulation. However, solving for complex multiscale systems remains computationally onerous due to the high dimensionality of the solution space. Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods. Pioneering work has demonstrated that ML provides solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. These high-speed, high-fidelity estimations can facilitate the solving of complex multiscale systems by providing a better initial solution to traditional solvers. This paper provides a perspective on the opportunities and challenges of using ML for complex multiscale modeling and simulation. We first outline the current state-of-the-art ML approaches for simulating multiscale systems and highlight some of the landmark developments. Next, we discuss current challenges for ML in multiscale computational modeling, such as the data and discretization dependence, interpretability, and data sharing and collaborative platform development. Finally, we suggest several potential research directions for the future.

Machine Learning For Fluid Mechanics

Abstract: The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.


As a result, centroid mapping in Euclidean manifold alone can achieve good results in PubMed, and hyperbolic manifold performs better in CiteSeer while spherical manifold suit CORA, which support the conclusion in #REFR .

Meanwhile, it also reflects that the larger the coreset, the more redundant information in the absorbed space.

Given the introduction of GCN in the spherical manifold, we conduct parallel-space ablation experiments on our model.

Experiments are performed on only one of the three spaces and discarding one.

The experimental results over classic citation networks are shown in Table 5 .

It also shows that Euclidean, hyperbolic and spherical manifolds partially capture the primary information of the nodes on these three graphs.

In addition, any combination of manifolds can increase the experimental result since the information each manifold captures is not the same.

Finally, we can conclude that the information on the three spaces needs to be collected simultaneously to get better performance. Fig. 8 .

ACC of NC task distribution along with coreset size, where the X-axis denotes the step of the size of the coreset size, and the Y-axis denotes the ACC of FMGNN.

Fmgnn: Fused Manifold Graph Neural Network

Abstract: Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks. Most existing works embed graph data in Euclidean space, while recent works extend the embedding models to hyperbolic or spherical spaces to achieve better performance on graphs with complex structures, such as hierarchical or ring structures. Fusing the embedding from different manifolds can take advantage of the embedding capabilities over different graph structures. However, existing embedding fusion methods mainly focus on concatenating or summing up the output embeddings without considering interacting and aligning the embeddings of the same vertices on different manifolds, which can lead to distortion and imprecision in the final fusion results. Besides, it is also challenging to fuse the embeddings of the same vertices from different coordinate systems. In the face of these challenges, we propose the Fused Manifold Graph Neural Network (FMGNN). This novel GNN architecture embeds graphs into different Riemannian manifolds with interaction and alignment among these manifolds during training and fuses the vertex embeddings through the distances on different manifolds between vertices and selected landmarks, geometric coresets. Our experiments demonstrate that FMGNN yields superior performance over strong baselines on the benchmarks of node classification and link prediction tasks.

Constant Curvature Graph Convolutional Networks

Abstract: Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e.g. scale-free, hierarchical or cyclical. However, the popular graph neural networks are currently limited in modeling data only via Euclidean geometry and associated vector space operations. Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. We do this by i) introducing a unified formalism that can interpolate smoothly between all geometries of constant curvature, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean concept of the center of mass. Our class of models smoothly recover their Euclidean counterparts when the curvature goes to zero from either side. Empirically, we outperform Euclidean GCNs in the tasks of node classification and distortion minimization for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature.


Two Fair Federated Learning (FFL) techniques, q-Fair Federated Learning (q-FFL) and Tilted Empirical Risk Minimization (TERM), proposed by Li et al. #REFR and Li et al.

Edge devices provide computational power and data for Machine Learning tasks; however, minimising communication costs while using them can be challenging. Federated Learning (FL) was introduced by McMahan et al.

#OTHEREFR to enable model training on client devices without sharing data.

However, the FL model accuracy may be underwhelming on clients with unusual data and even worse than a local model, reducing the incentive for participation.

The existing body of research on balancing global and local performance has proposed several approaches.

#OTHEREFR respectively, aim to improve the accuracy of the worst-performing clients by prioritising those with large losses during FL. Alternatively, Yu et al. #OTHEREFR and Mansour et al.

#OTHEREFR recommend using personalisation (local adaptation) methods such as Freezebase (FB), Elastic Weight Consolidation (EWC), and Knowledge Distillation (KD) for fine-tuning.

In this work, relative accuracy refers to the difference in local client test set accuracy between a federated and local model.

While the sets of potential use cases for fairness and personalisation are not identical-e.g., personalisation would be inappropriate for clients with few samples-FFL could construct a fairer relative accuracy distribution as a starting point.

For FFL to reduce the need for personalisation, it would have to lower the number of underperforming clients or improve their average relative accuracy.

Can Fair Federated Learning Reduce The Need For Personalisation?

Abstract: Federated Learning (FL) enables training ML models on edge clients without sharing data. However, the federated model's performance on local data varies, disincentivising the participation of clients who benefit little from FL. Fair FL reduces accuracy disparity by focusing on clients with higher losses while personalisation locally fine-tunes the model. Personalisation provides a participation incentive when an FL model underperforms relative to one trained locally. For situations where the federated model provides a lower accuracy than a model trained entirely locally by a client, personalisation improves the accuracy of the pre-trained federated weights to be similar to or exceed those of the local client model. This paper evaluates two Fair FL (FFL) algorithms as starting points for personalisation. Our results show that FFL provides no benefit to relative performance in a language task and may double the number of underperforming clients for an image task. Instead, we propose Personalisation-aware Federated Learning (PaFL) as a paradigm that pre-emptively uses personalisation losses during training. Our technique shows a 50% reduction in the number of underperforming clients for the language task while lowering the number of underperforming clients in the image task instead of doubling it. Thus, evidence indicates that it may allow a broader set of devices to benefit from FL and represents a promising avenue for future experimentation and theoretical analysis.

Fair Resource Allocation In Federated Learning

Abstract: Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (i.e., more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.


Compared to the NNQS results reported in #REFR , we see that the IS method with the symmetric function ansatz is able to better predict the overlap of the above state with the |GHZ n + .

In an experiment involving symmetric states, first studied in #OTHEREFR , the fidelity of |GHZ n + is estimated with respect to another mixed state of the form, ρ n,p = (1 − p) |GHZ n + GHZ n

+ | + p |GHZ n − GHZ n − | for p ∈ [0, 1]

It was been reported that for this mixed state example, generative models learned using the Neural Net Quantum States (NNQS) ansatz [7, #OTHEREFR struggled to find the correct fidelity as p got closer to 1.

The results of applying our method on this problem with a symmetric function ansatz are given in Fig. 4d .

7 We see that symmetric function ansatz performs much better than neural-net anastz learned with NeurISE #OTHEREFR without any symmetry assumptions. Both models are learned using 10 6 state measurements.

The neural net has 1043 trainable parameters while the symmetric function has 840 parameters (d) Comparison of the fidelity between ρn,p for p ∈ [0, 1] with |GHZ n + estimated from a symmetric EBM for ρn,p.

The symmetric EBM is able to produce the correct fidelity behavior here even when p → 1.

It has been reported that for this benchmark, NNQS methods which compute a classical upper bound for quantum fidelity, are not able to reproduce the correct behavior when p gets close to 1 [7, #OTHEREFR .

All the models here are learned using 6 × 10 4 samples.

Learning Energy Based Representations Of Quantum Many-Body States

Abstract: Efficient representation of quantum many-body states on classical computers is a problem of enormous practical interest. An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest. A number of machine learning approaches have been recently used to construct such classical representations [1-6] which enable predictions of observables [7] and accounts for physical symmetries [8]. However, the structure of a quantum state gets typically lost unless a specialized ansatz is employed based on prior knowledge of the system [9-12]. Moreover, most such approaches give no information about what states are easier to learn in comparison to others. Here, we propose a new generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets, and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the hardness of learning for a given class of quantum states.

Predicting Many Properties Of A Quantum System From Very Few Measurements

Abstract: Predicting properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a classical shadow, can be used to predict many different properties: order $\log M$ measurements suffice to accurately predict $M$ different functions of the state with high success probability. The number of measurements is independent of the system size, and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables, and the energy variance of many-body local Hamiltonians, which allows applications to speedup variational quantum algorithms. The numerical results highlight the advantages of classical shadows relative to previously known methods.


Kudugunta and Ferrara #REFR exploits user metadata and proposes SMOTENN to combat imbalanced datasets. Miller et al.

Bots with malicious goals often harm the integrity of online discourse and reduce search quality.

Existing Twitter bot detection methods can be often categorized into three types: feature-based, text-based, and graph-based approaches #OTHEREFR .

These methods conduct feature engineering based on handcrafted user features derived from metadata #OTHEREFR and user textual information #OTHEREFR .

These features are then combined with traditional classification algorithms #OTHEREFR to identify Twitter bots.

#OTHEREFR leverages the feature of tweet content and performs anomaly detection to identify bots. Hayawi et al.

#OTHEREFR use hybrid types of numerical, binary, and textural features as high dimensional tensors for classification.

However, as noted by Cresci #OTHEREFR , evolving bots can evade the detection of feature-based approaches by creating deceptive accounts with manipulated metadata and stolen tweets.

The methods use NLP techniques to detect Twitter bots with their tweets and descriptions.

Botmoe: Twitter Bot Detection With Community-Aware Mixtures Of Modal-Specific Experts

Abstract: Twitter bot detection has become a crucial task in efforts to combat online misinformation, mitigate election interference, and curb malicious propaganda. However, advanced Twitter bots often attempt to mimic the characteristics of genuine users through feature manipulation and disguise themselves to fit in diverse user communities, posing challenges for existing Twitter bot detection models. To this end, we propose BotMoE 1 , a Twitter bot detection framework that jointly utilizes multiple user information modalities (metadata, textual content, network structure) to improve the detection of deceptive bots. Furthermore, BotMoE incorporates a communityaware Mixture-of-Experts (MoE) layer to improve domain generalization and adapt to different Twitter communities. Specifically, BotMoE constructs modal-specific encoders for metadata features, textual content, and graph structure, which jointly model Twitter users from three modal-specific perspectives. We then employ a community-aware MoE layer to automatically assign users to different communities and leverage the corresponding expert networks. Finally, user representations from metadata, text, and graph perspectives are fused with an expert fusion layer, combining all three modalities while measuring the consistency of user information. Extensive experiments demonstrate that BotMoE significantly advances the state-of-the-art on three Twitter bot detection benchmarks. Studies also confirm that BotMoE captures advanced and evasive bots, alleviates the reliance on training data, and better generalizes to new and previously unseen user communities. CCS CONCEPTS • Information systems → Social networks.

Deep Neural Networks For Bot Detection

Abstract: The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that caused health epidemics. Most techniques proposed to date detect bots at the account level, by processing large amount of social media posts, and leveraging information from network structure, temporal dynamics, sentiment analysis, etc. In this paper, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as auxiliary input to LSTM deep nets processing the tweet text. Another contribution that we make is proposing a technique based on synthetic minority oversampling to generate a large labeled dataset, suitable for deep nets training, from a minimal amount of labeled data (roughly 3,000 examples of sophisticated Twitter bots). We demonstrate that, from just one single tweet, our architecture can achieve high classification accuracy (AUC>96%) in separating bots from humans. We apply the same architecture to account-level bot detection, achieving nearly perfect classification accuracy (AUC>99%). Our system outperforms previous state of the art while leveraging a small and interpretable set of features yet requiring minimal training data.


Quantitative comparisons with other baselines in terms of PSNR, SSIM, and LPIPS #REFR are reported in Table 1 .

Our hybrid neural rendering design "Ours (H)" outperforms previous methods by enhancing the quality of neural 3D representations.

However, the PSNR and SSIM drop in the full version of our method "Ours".

This is because our blur-handling modules mimic blurriness effects and down weight blur images, enabling the model to learn from clean supervision.

However, since this differs from the original training data distribution, the model may not fit the evaluation metric well.

Moreover, Deblur-NeRF delivers a low PSNR because it tends to introduce misalignment between rendered and reference images.

Hybrid Neural Rendering For Large-Scale Scenes With Motion Blur

Abstract: Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed qualityaware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-theart point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/ Hybrid-Rendering-ProjectPage/.

The Unreasonable Effectiveness Of Deep Features As A Perceptual Metric

Abstract: While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on the ImageNet classification task has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called"perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new Full Reference Image Quality Assessment (FR-IQA) dataset of perceptual human judgments, orders of magnitude larger than previous datasets. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by huge margins. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.


Entity Typing Following ERNIE #REFR , we first insert a pair of special tokens to enclose and emphasize the given span in the input sequence.

The fine-tuning methods for different task:

After PLM's encoding, we adopt the final representation of the [CLS] token for classification.

Named Entity Recognition Following Devlin et al.

(2019), we regard the task as a sequence labeling task and apply a token-level classifier to tokens' contextual representations to predict their IOB2 tags #OTHEREFR .

Relation Extraction Following ERNIE #OTHEREFR , we insert two pairs of special tokens to enclose and highlight the subject entity and the object entity in the input sequence respectively.

After that, we add a relation classifier layer on the [CLS] token's final representation for predictions.

Unter: A Unified Knowledge Interface For Enhancing Pre-Trained Language Models

Abstract: Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge injection methods are either applicable to structured knowledge or unstructured knowledge, lacking a unified usage. In this paper, we propose a UNified knowledge inTERface, UNTER, to provide a unified perspective to exploit both structured knowledge and unstructured knowledge. In UNTER, we adopt the decoder as a unified knowledge interface, aligning span representations obtained from the encoder with their corresponding knowledge. This approach enables the encoder to uniformly invoke span-related knowledge from its parameters for downstream applications. Experimental results show that, with both forms of knowledge injected, UNTER gains continuous improvements on a series of knowledge-driven NLP tasks, including entity typing, named entity recognition and relation extraction, especially in low-resource scenarios.

Ernie: Enhanced Language Representation With Informative Entities

Abstract: Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The source code of this paper can be obtained from this https URL.


Wasserstein Lifting And Behavioural Metrics

We build on #REFR , where an approach is proposed for canonically defining a behavioural pseudometric for coalgebras of a functor F : Set → Set, that is, for functions of the form ξ : X → F X where X is a set.

In this section we show how the framework for fixpoint checking described before can be used to deal with coalgebraic behavioural metrics.

Intuitively ξ specifies a transition system whose branching type is given by F .

Given such a coalgebra ξ, the idea is to endow X with a pseudo-metric d ξ : X × X → M defined as the least fixpoint of the map

d → d F • (ξ × ξ) where F lifts a metric d : X × X → M to a metric d F : F X × F X → M.

Here we focus on the so-called Wasserstein lifting and show how approximations of the functions involved in the definition of the pseudometric can be determined.

A Monoidal View On Fixpoint Checks

Abstract: Fixpoints are ubiquitous in computer science as they play a central role in providing a meaning to recursive and cyclic definitions. Bisimilarity, behavioural metrics, termination probabilities for Markov chains and stochastic games are defined in terms of least or greatest fixpoints. Here we show that our recent work which proposes a technique for checking whether the fixpoint of a function is the least (or the largest) admits a natural categorical interpretation in terms of gs-monoidal categories. The technique is based on a construction that maps a function to a suitable approximation and the compositionality properties of this mapping are naturally interpreted as a gs-monoidal functor. This guides the realisation of a tool, called UDEfix that allows to build functions (and their approximations) like a circuit out of basic building blocks and subsequently perform the fixpoints checks. We also show that a slight generalisation of the theory allows one to treat a new relevant case study: coalgebraic behavioural metrics based on Wasserstein liftings.

Coalgebraic Behavioral Metrics

Abstract: We study different behavioral metrics, such as those arising from both branching and linear-time semantics, in a coalgebraic setting. Given a coalgebra $\alpha\colon X \to HX$ for a functor $H \colon \mathrm{Set}\to \mathrm{Set}$, we define a framework for deriving pseudometrics on $X$ which measure the behavioral distance of states. ::: A crucial step is the lifting of the functor $H$ on $\mathrm{Set}$ to a functor $\overline{H}$ on the category $\mathrm{PMet}$ of pseudometric spaces. We present two different approaches which can be viewed as generalizations of the Kantorovich and Wasserstein pseudometrics for probability measures. We show that the pseudometrics provided by the two approaches coincide on several natural examples, but in general they differ. ::: If $H$ has a final coalgebra, every lifting $\overline{H}$ yields in a canonical way a behavioral distance which is usually branching-time, i.e., it generalizes bisimilarity. In order to model linear-time metrics (generalizing trace equivalences), we show sufficient conditions for lifting distributive laws and monads. These results enable us to employ the generalized powerset construction.


The observed decreasing trend in the SR I , however, contradicts previous studies of the development of this indicator over time: #REFR , in fact, report an average increase of +5.2 p.p.

The decreasing trend in both SR I and SR II which characterizes most of the countries shows that, in most of the cases, the overall citation impact of countries has grown more than the proportion of citation impact generated by domestic authors, i.e., that the denominator of both indicators has increased more than their numerator (see Section 3.2).

The faster increase in citations may be related in turn to the overall growth of scientific production and how it impacts the length of the reference lists of scientific publications.

According to #OTHEREFR , in fact, the world scientific production exhibits 4% annual growth in publications and 1.8% annual growth in the number of references per publication.

Combined, these dynamics produce a 12-year doubling period in the total amount of references, which results in turn in a generalized increase in citations #OTHEREFR .

The decreasing trends, thus, may be simply due to the different rates of growth of the numerator and the denominator of the indicators used here: country self-citations of Type I or Type II grow less than bibliographic references.

in the SR I of G10 countries between 2000 and 2016.

Namely, the discrepancy between the present results and #OTHEREFR depends on the different way of computing the SR I indicator.

Indeed, #OTHEREFR used a nonfixed citation window, which included all the years from the publication year to 2016.

For example, for the cited items published in the year 2000, a 17year citation window was used, by summing up all citations from 2000 to 2016; whereas, for the year 2006, the citation window was 11 years long, including citations from 2006 to 2016; for 2016, the citation window included only 1 year, i.e., only citations from 2016 itself were counted.

Since self-citations are in general younger than external citations #OTHEREFR , they tend to represent a higher proportion of total citations for the years when the citation window is shorter.

A Global Exploratory Comparison Of Country Self-Citations 1996-2019

Abstract: Self-citations are a key topic in evaluative bibliometrics because they can artificially inflate citation-related performance indicators. Recently, selfcitations defined at the largest scale, i.e., country self-citations, have started to attract the attention of researchers and policymakers. According to a recent research, in fact, the anomalous trends in the country self-citation rates of some countries, such as Italy, have been induced by the distorting effect of citation metrics-centered science policies. In the present study, we investigate the trends of country self-citations in 50 countries over the world in the period 1996-2019 using Scopus data. Results show that for most countries country selfcitations have decreased over time. 12 countries (

Citation Gaming Induced By Bibliometric Evaluation: A Country-Level Comparative Analysis

Abstract: It is several years since national research evaluation systems around the globe started making use of quantitative indicators to measure the performance of researchers. Nevertheless, the effects on these systems on the behavior of the evaluated researchers are still largely unknown. For investigating this topic, we propose a new inwardness indicator able to gauge the degree of scientific self-referentiality of a country. Inwardness is defined as the proportion of citations coming from the country over the total number of citations gathered by the country. A comparative analysis of the trends for the G10 countries in the years 2000-2016 reveals a net increase of the Italian inwardness. Italy became, both globally and for a large majority of the research fields, the country with the highest inwardness and the lowest rate of international collaborations. The change in the Italian trend occurs in the years following the introduction in 2011 of national regulations in which key passages of professional careers are governed by bibliometric indicators. A most likely explanation of the peculiar Italian trend is a generalized strategic use of citations in the Italian scientific community, both in the form of strategic author self-citations and of citation clubs. We argue that the Italian case offers crucial insights on the constitutive effects of evaluation systems. As such, it could become a paradigmatic case in the debate about the use of indicators in science-policy contexts.


We compared our results to the Pruned Non-Local Means (PNLM) algortihm presented in #REFR , which supports our claim that the accuracy of recovery in Cloud K-SVD is on par with SOTA in the field.

When the noise is severe, a lot of detail have to be removed, however when it is less severe, less detail need be removed.

Details in images can be recovered better by decreasing data size and atom count, M and N , and increasing the number of nonzero elements, K.

Cloud K-SVD was originally made for image classification, but we have made a novel application of the algorithm and some practical improvements, e.g.

used Simultaneous Orthogonal Matching Pursuit (SOMP) and adjusted the algorithm to account for node failures.

We show, that our modified version of Cloud K-SVD can successfully conduct distributed AWGN denoising of images and simultaneously learn a global dictionary.

Table 6 Average execution times of the OMP and K-SVD step for Cloud K-SVD (tp = 3, tc = 5) when recovering Face from noisy patches with different sparsity K, data size M and AWGN level σ 2 . We set α = 1.

Cloud K-Svd For Image Denoising

Abstract: Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data. We present a novel application of the algorithm as we use it to recover both noiseless and noisy images from overlapping patches. We implement a node network in Kubernetes using Docker containers to facilitate Cloud K-SVD. Results show that Cloud K-SVD can recover images approximately and remove quantifiable amounts of noise from benchmark gray-scaled images without sacrificing accuracy in recovery; we achieve an SSIM index of 0.88, 0.91 and 0.95 between clean and recovered images for noise levels (µ = 0, σ 2 = 0.01, 0.005, 0.001), respectively, which is similar to SOTA in the field. Cloud K-SVD is evidently able to learn a mutual dictionary across multiple nodes and remove AWGN from images. The mutual dictionary can be used to recover a specific image at any of the nodes in the network.

Pruned Non-Local Means

Abstract: In non-local means (NLM), each pixel is denoised by performing a weighted averaging of its neighbouring pixels, where the weights are computed using image patches. The authors demonstrate that the denoising performance of NLM can be improved by pruning the neighbouring pixels, namely, by rejecting neighbouring pixels whose weights are below a certain threshold.. While pruning can potentially reduce pixel averaging in uniform-intensity regions, they demonstrate that there is generally an overall improvement in the denoising performance. In particular, the improvement comes from pixels situated close to edges and corners. The success of the proposed method strongly depends on the choice of the global threshold., which in turn depends on the noise level and the image characteristics. They show how Stein's unbiased estimator of the mean-squared error can be used to optimally tune., at a marginal computational overhead. They present some representative denoising results to demonstrate the superior performance of the proposed method over NLM and its variants.


Adapting general-purpose language models to a particular domain by subjecting it to additional self-supervised training using domain-specific text has been shown to boost performance for certain tasks #REFR .

It represents an official interpretation of the patient's images and aims to provide actionable information for future care.

Given the vast number of information-rich radiology reports stored in clinical imaging databases, it is no surprise that artificial intelligence (AI)-based language models have begun to find an increasing number of applications in radiology.

Language models can extract training labels from reports #OTHEREFR , summarize reports #OTHEREFR , generate reports from images #OTHEREFR , and more #OTHEREFR .

Despite their impressive performance in various domains, language models trained on large generic text corpora can be suboptimal for applications in radiology and nuclear medicine.

Large pre-trained language models, such as Bidirectional Encoder Representations from Transformers (BERT) #OTHEREFR , are typically developed using selfsupervised training, which involves predicting occluded words or adjacent sentences within the training corpus.

For example, bioBERT was adapted from BERT through additional pre-training on biomedical corpora from PubMed #OTHEREFR .

Domain-adapted bioBERT outperformed BERT when applied to downstream biomedical tasks.

Other studies have demonstrated the benefit of domain adaption #OTHEREFR .

Adaptation of language models to the nuclear medicine domain, however, has been understudied.

Nuclear medicine reports contain unique terms that are not used more broadly in radiology.

Domain-Adapted Large Language Models For Classifying Nuclear Medicine Reports

Abstract: With the growing use of transformer-based language models in medicine, it is unclear how well these models generalize to nuclear medicine which has domain-specific vocabulary and unique reporting styles. In this study, we evaluated the value of domain adaptation in nuclear medicine by adapting language models for the purpose of 5-point Deauville score prediction based on clinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively retrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from 2008-2018 in our clinical imaging database. Deauville scores were removed from the reports and then the remaining text in the reports was used as the model input. Multiple general-purpose transformer language models were used to classify the reports into Deauville scores 1-5. We then adapted the models to the nuclear medicine domain using masked language modeling and assessed its impact on classification performance. The language models were compared against vision models, a multimodal vision language model, and a nuclear medicine physician with seven-fold Monte Carlo cross validation, reported are the mean and standard deviations. Domain adaption improved all language models. For example, BERT improved from 61.3 ± 2.9% fiveclass accuracy to 65.7 ± 2.2% following domain adaptation. The best performing model (domain-adapted RoBERTa) achieved a five-class accuracy of 77.4 ± 3.4%, which was better than the physician's performance (66%), the best vision model's performance (48.1 ± 3.5%), and was similar to the multimodal model's performance (77.2 ± 3.2%). Domain adaptation improved the performance of large language models in interpreting nuclear medicine text reports.

Biobert: A Pre-Trained Biomedical Language Representation Model For Biomedical Text Mining

Abstract: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. We make the pre-trained weights of BioBERT freely available at this https URL, and the source code for fine-tuning BioBERT available at this https URL.

Adapting general-purpose language
domain-specific text


This is different from partial domain adaptation problems, where importance sampling was used to apply small weights to data from missing classes #REFR .

, which is an appropriate assumption since the trained domain classifier can often estimate the domain label accurately only from z in practical cases.

In addition to the reformulation described above, Here, all terms can be calculated by the available data.

Note that in the second term, the data x is sampled from the source anomalous data, but in lVAE (x, c = A, d = T) the domain label is d = T.

Seen anomaly Unseen anomaly   Case 1  1, 2, 3  4, 5, 6  7, 8, 9  Case 2  4, 5, 6  7, 8, 9  1, 2, 3  Case 3  7, 8, 9  1, 2, 3  4, 5, 6 Thus, we input d = T to the decoder to calculate this term, although the data x will be sampled from the source domain.

As a result, #OTHEREFR applies large weights to data from missing classes if it is regarded to be similar to target domain data.

Zero-Shot Domain Adaptation Of Anomalous Samples For Semi-Supervised Anomaly Detection

Abstract: Semi-supervised anomaly detection (SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer adapting to domain shifts, since anomalous data are unlikely to be available for the target domain in the training phase. To solve this problem, we propose a domain adaptation method for SSAD where no anomalous data are available for the target domain. First, we introduce a domain-adversarial network to a variational auto-encoder-based SSAD model to obtain domain-invariant latent variables. Since the decoder cannot reconstruct the original data solely from domaininvariant latent variables, we conditioned the decoder on the domain label. To compensate for the missing anomalous data of the target domain, we introduce an importance sampling-based weighted loss function that approximates the ideal loss function. Experimental results indicate that the proposed method helps adapt SSAD models to the target domain when no anomalous data are available for the target domain.

Learning Transferable Features With Deep Adaptation Networks

Abstract: Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multikernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.


More recently Graph Markov Neural Networks (GMNN) #REFR were introduced as genuine probabilistic models which include label correlations in graphs by combining the strength of GNNs and those of conditional random fields (CRF) while avoiding their limitations.

Inductive classification on the other hand refers to the most common setting in machine learning in which nodes to be labeled are not known ahead of time #OTHEREFR .

A number of architectures have been proposed over the years which deal with specific issues occurring with GNNs.

Some combat over-smoothing, (which is the tendency for deep GNNs to predict the same labels for all nodes) #OTHEREFR , some deal with assortativity or heterophily (which refers to situations in which neighboring nodes are likely to have different labels) #OTHEREFR and others still try to learn the connection weights from data using an appropriate attention mechanism #OTHEREFR .

Despite their diversity, these models all have one important shortcoming.

Namely they assume that labels can be predicted independently for each node in the graph. In other words they neglect label dependencies altogether.

These are the models we shall focus on in this work.

The accuracy of the GMNN model was evaluated for node classification and link prediction tasks in #OTHEREFR on the classical benchmark datasets Cora, Pubmed and Citeseer #OTHEREFR using the public splits defined in #OTHEREFR .

Under these settings a clear improvement was demonstrated when comparing the GMNN model to existing baselines that do not account for label dependencies.

However, as a number of recent works #OTHEREFR have pointed out, a fair evaluation of the performance of GNNs requires a procedure which performs a systematic randomization over train-arXiv:2304.01235v1 [cs.

LG] 3 Apr 2023 validation-test set partitions and makes clear separation between model selection and model assessment.

Fair Evaluation Of Graph Markov Neural Networks

Abstract: Graph Markov Neural Networks (GMNN) have recently been proposed to improve regular graph neural networks (GNN) by including label dependencies into the semi-supervised node classification task. GMNNs do this in a theoretically principled way and use three kinds of information to predict labels. Just like ordinary GNNs, they use the node features and the graph structure but they moreover leverage information from the labels of neighboring nodes to improve the accuracy of their predictions. In this paper, we introduce a new dataset named WikiVitals which contains a graph of 48k mutually referred Wikipedia articles classified into 32 categories and connected by 2.3M edges. Our aim is to rigorously evaluate the contributions of three distinct sources of information to the prediction accuracy of GMNN for this dataset: the content of the articles, their connections with each other and the correlations among their labels. For this purpose we adapt a method which was recently proposed for performing fair comparisons of GNN performance using an appropriate randomization over partitions and a clear separation of model selection and model assessment.

Gmnn: Graph Markov Neural Networks

Abstract: This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.


The trends are nearly identical for other layers, as well as layers from ResNet-50 #REFR .

We therefore compare : : with varying group size to less (unstructured and : ) and more (blocked) structured sparsity on individual tensors.

To do this, we compare the energy, a novel metric we define to be the ratio ∥ˆ∥ 1 /∥ ∥ 1 , where is the original tensor andˆis the pruned version.

The energy ranges between 0 and 1, and captures the intuition that we want to preserve larger-magnitude values.

7 , we show the energy for a weight tensor from the Hug-gingFace #OTHEREFR bert-base-uncased model.

Unstructured sparsity preserves the maximum possible energy, followed by : .

Our : : sparsity achieves almost the same energy as : when = 16, with quality decreasing slightly with smaller .

This is because increasing group size tends to be less restrictive: as chunks are larger, it is easier to find more : patterns. Blocked sparsity performs the worst.

Hence, we can see that : : sparsity preserves energy nearly as well as : sparsity, while its additional structure can be taken advantage of for performance.

Sten: Productive And Efficient Sparsity In Pytorch

Abstract: As deep learning models grow, sparsity is becoming an increasingly critical component of deep neural networks, enabling improved performance and reduced storage. However, existing frameworks offer poor support for sparsity. Specialized sparsity engines focus exclusively on sparse inference, while general frameworks primarily focus on sparse tensors in classical formats and neglect the broader sparsification pipeline necessary for using sparse models, especially during training. Further, existing frameworks are not easily extensible: adding a new sparse tensor format or operator is challenging and time-consuming. To address this, we propose STen, a sparsity programming model and interface for PyTorch, which incorporates sparsity layouts, operators, and sparsifiers, in an efficient, customizable, and extensible framework that supports virtually all sparsification methods. We demonstrate this by developing a high-performance grouped : sparsity layout for CPU inference at moderate sparsity. STen brings high performance and ease of use to the ML community, making sparsity easily accessible.

Deep Residual Learning For Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.


In contrast to the aforementioned datasets and test scenarios, long video object segmentation has numerous real-world applications, such as video summarization, human-computer interaction, and autonomous vehicles #REFR .

The current state-of-the-art semi-supervised and specifically online VOS methods #OTHEREFR perform well on VOS datasets with short videos (up to a few seconds or 100 frames in length) such as DAVIS16 #OTHEREFR , DAVIS17 #OTHEREFR , and YouTube-VOS18 #OTHEREFR .

However, most of these methods do not retain their expected performance on long videos, such as those in the Long Videos dataset #OTHEREFR as shown in the XMem paper #OTHEREFR .

The question of the poor performance of online VOS on long videos has not been investigated in the VOS field, nor addressed through continual learning.

Continual learning methods are typically tested on classification datasets, like MNIST #OTHEREFR , CIFAR10 #OTHEREFR , and Imagenet #OTHEREFR , or on datasets specifically designed for continual learning, such as Core50 #OTHEREFR .

The classification dataset is fed to the model as a sequential stream of data in online continual learning methods #OTHEREFR .

In this paper, we formulate and address the inefficient performance of the online VOS approaches on long videos as an online continual learning problem.

Moreover, we propose a new long-video object segmentation dataset for continual learning (CLVOS23), as a much more realistic and significantly greater challenge for testing VOS methods on long videos.

As a baseline, we propose a Regularizationbased (prior-focused) Continual Learning (RCL) solution to improve online VOS.

Clvos23: A Long Video Object Segmentation Dataset For Continual Learning

Abstract: Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continual learning challenges particularly present themselves in working on long video sequences. In this article, we first formulate the problem of semi-supervised VOS, specifically online VOS, as a continual learning problem, and then secondly provide a public VOS dataset, CLVOS23, focusing on continual learning. Finally, we propose and implement a regularizationbased continual learning approach on LWL, an existing online VOS baseline, to demonstrate the efficacy of continual learning when applied to online VOS and to establish a CLVOS23 baseline. We apply the proposed baseline to the Long Videos dataset as well as to two short video VOS datasets, DAVIS16 and DAVIS17. To the best of our knowledge, this is the first time that VOS has been defined and addressed as a continual learning problem. The proposed CLVOS23 dataset has been released at https: //github.com/Amir4g/CLVOS23.

Video Object Segmentation And Tracking: A Survey

Abstract: Object segmentation and object tracking are fundamental research area in the computer vision community. These two topics are diffcult to handle some common challenges, such as occlusion, deformation, motion blur, and scale variation. The former contains heterogeneous object, interacting object, edge ambiguity, and shape complexity. And the latter suffers from difficulties in handling fast motion, out-of-view, and real-time processing. Combining the two problems of video object segmentation and tracking (VOST) can overcome their respective difficulties and improve their performance. VOST can be widely applied to many practical applications such as video summarization, high definition video compression, human computer interaction, and autonomous vehicles. This article aims to provide a comprehensive review of the state-of-the-art tracking methods, and classify these methods into different categories, and identify new trends. First, we provide a hierarchical categorization existing approaches, including unsupervised VOS, semi-supervised VOS, interactive VOS, weakly supervised VOS, and segmentation-based tracking methods. Second, we provide a detailed discussion and overview of the technical characteristics of the different methods. Third, we summarize the characteristics of the related video dataset, and provide a variety of evaluation metrics. Finally, we point out a set of interesting future works and draw our own conclusions.


A.5. Additional Qualitative Evaluation On Label-Head

We fine-tune the pre-trained head model MetaHead-F on CelebAMask-HQ #REFR , in which each image has a seg-mentation mask of 19-class facial attributes.

Performance comparison on Gaze360 #OTHEREFR and MPIIFaceGaze #OTHEREFR using real and synthetic data. Our synthetic data significantly reduce the gaze estimation error. Figure 15 . Results of local head editing using semantic labels.

LabelHead allows users to perform locally 3D fine-grained head shape control and editing in a disentangled and view-consistent manner. Please refer to Appendix A.5 for details.

Head Shape Control Using Semantic Labels As is classified in CelebAMask-HQ #OTHEREFR , each pixel in the head semantic mask falls into 19 distinct categories including skin, eyebrows, ears, mouth, lip, etc.

Given a head image, we flatten the semantic category corresponding to each pixel into a latent representation as the semantic label.

We then embed the semantic label feature into the feature design space of MetaHead-F.

After training, given the reference head image, we randomly initialize the label values of all features including semantic label, then using the photometric loss between the test image and the generated head of MetaHead-F to optimize the label value, thus getting the semantic mask estimation.

We conduct interactive head shape manipulation by locally editing(drawing) on the obtained semantic mask and leverage MetaHead-F to generate the corresponding modified free-view 3D heads. As is shown in Fig.

15 , the head shape control using semantic labels is disentangled and view-consistent.

Metahead: An Engine To Create Realistic Digital Head

Abstract: Collecting and labeling training data is one important step for learning-based methods because the process is time-consuming and biased. For face analysis tasks, although some generative models [25, 10, 45, 6, 5, 11, 20] can be used to generate face data, they can only achieve a subset of generation diversity, reconstruction accuracy, 3D consistency, high-fidelity visual quality, and easy editability. One recent related work is the graphics-based generative method [48], but it can only render low realism head with high computation cost. In this paper, we propose MetaHead, a unified and full-featured controllable digital head engine, which consists of a controllable head radiance field(MetaHead-F) to super-realistically generate or reconstruct view-consistent 3D controllable digital heads and a generic top-down image generation framework LabelHead to generate digital heads consistent with the given customizable feature labels. Experiments validate that our controllable digital head engine achieves the state-of-the-art generation visual quality and reconstruction accuracy. Moreover, the generated labeled data can assist real training data and significantly surpass the labeled data generated by graphics-based methods in terms of training effect. The project page is available at: https://ustc3dv.github.io/MetaHead/.

Maskgan: Towards Diverse And Interactive Facial Image Manipulation

Abstract: Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ.


We select one scene from the Nuscenes #REFR dataset in which the ego-vehicle is stationary at a traffic light (see Figure 10 ).

Scene-oriented attacks are primarily aimed at fusion models with global sensitivity.

Such models are vulnerable to adversarial patches placed upon non-object background scenes, as evidenced in Section 6.2.

In contrast to the regional patch-on-road attack that we previously assessed, scene-oriented attacks are not restricted to the per-frame level but can affect the detection of arbitrary objects in a given scene, even those that were not initially present during the patch generation.

Therefore, this type of attack poses a more significant and practical threat in real-world scenarios as attackers can effortlessly paste generated adversarial patches onto the ground, rendering victim vehicles in close proximity blind.

This could pose a significant risk to pedestrians and surrounding vehicles. Experimental setup.

Note that a Nuscenes scene represents a driving clip that lasts about one minute.

This scene includes 490 frames of multi-modal data (multiview images, 360-degree LiDAR point clouds, and object annotations), and the object types and locations vary across different frames.

We split the scene into two subsets: the first 245 frames are used as the "training set" to generate

Fusion Is Not Enough: Single-Modal Attacks To Compromise Fusion Models In Autonomous Driving

Abstract: Multi-sensor fusion (MSF) is widely adopted for perception in autonomous vehicles (AVs), particularly for the task of 3D object detection with camera and LiDAR sensors. The rationale behind fusion is to capitalize on the strengths of each modality while mitigating their limitations. The exceptional and leading performance of fusion models has been demonstrated by advanced deep neural network (DNN)based fusion techniques. Fusion models are also perceived as more robust to attacks compared to single-modal ones due to the redundant information in multiple modalities. In this work, we challenge this perspective with single-modal attacks that targets the camera modality, which is considered less significant in fusion but more affordable for attackers. We argue that the weakest link of fusion models depends on their most vulnerable modality, and propose an attack framework that targets advanced camera-LiDAR fusion models with adversarial patches. Our approach employs a two-stage optimization-based strategy that first comprehensively assesses vulnerable image areas under adversarial attacks, and then applies customized attack strategies to different fusion models, generating deployable patches. Evaluations with five state-ofthe-art camera-LiDAR fusion models on a real-world dataset show that our attacks successfully compromise all models. Our approach can either reduce the mean average precision (mAP) of detection performance from 0.824 to 0.353 or degrade the detection score of the target object from 0.727 to 0.151 on average, demonstrating the effectiveness and practicality of our proposed attack framework.

Nuscenes: A Multimodal Dataset For Autonomous Driving

Abstract: Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image-based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first published dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online at this http URL.


We have also trained an EfficientNet model with PointRend head #REFR and scaled loss with a factor of 2.

We reduced it in the training process with the help of the ReduceLROnPlateau [44] algorithm with patience 3 by 0.5 factor monitoring validation dice loss.

Initially, the constructed pipeline was a multiclass model with 5 channels -one for each organ.

However, as only one class of organ FTUs was present at any given image we reformulated the task as a binary semantic segmentation with a single channel containing all the masks no matter what organ was present on an image.

Such an approach allowed for improved generalization and better scores across all organs.

To improve model robustness we have used a mixture of four losses: binary Cross-Entropy, Dice Loss, Focal Loss #OTHEREFR , and Jacquard Loss #OTHEREFR .

While we didn't notice a meaningful performance boost from the PointRend alone we think that its main contribution was in adding diversity to our model ensemble as well as some regularization.

We have used PyTorch #OTHEREFR built-in mixed precision training in order to reduce GPU memory consumption which allowed us to use a batch size of 32 samples on A100 GPUs.

Semi-Supervised Segmentation Of Functional Tissue Units At The Cellular Level

Abstract: We present a new method for functional tissue unit segmentation at the cellular level, which utilizes the latest deep learning semantic segmentation approaches together with domain adaptation and semi-supervised learning techniques. This approach allows for minimizing the domain gap, class imbalance, and captures settings influence between HPA and HubMAP datasets. The presented approach achieves comparable with state-of-the-art-result in functional tissue unit segmentation at the cellular level.

Pointrend: Image Segmentation As Rendering

Abstract: We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches.


Namely, the discrepancy between the present results and #REFR depends on the different way of computing the SR I indicator.

According to #OTHEREFR , in fact, the world scientific production exhibits 4% annual growth in publications and 1.8% annual growth in the number of references per publication.

Combined, these dynamics produce a 12-year doubling period in the total amount of references, which results in turn in a generalized increase in citations #OTHEREFR .

The decreasing trends, thus, may be simply due to the different rates of growth of the numerator and the denominator of the indicators used here: country self-citations of Type I or Type II grow less than bibliographic references.

The observed decreasing trend in the SR I , however, contradicts previous studies of the development of this indicator over time: #OTHEREFR , in fact, report an average increase of +5.2 p.p.

in the SR I of G10 countries between 2000 and 2016.

Indeed, #OTHEREFR used a nonfixed citation window, which included all the years from the publication year to 2016.

For example, for the cited items published in the year 2000, a 17year citation window was used, by summing up all citations from 2000 to 2016; whereas, for the year 2006, the citation window was 11 years long, including citations from 2006 to 2016; for 2016, the citation window included only 1 year, i.e., only citations from 2016 itself were counted.

Since self-citations are in general younger than external citations #OTHEREFR , they tend to represent a higher proportion of total citations for the years when the citation window is shorter.

Hence, #OTHEREFR registered an inflation of SR I for more recent years, as the citation window shortens.

The present study, by contrast, does not suffer from this problem as it is based on a fixed citation window, i.e., only a fixed number of years after the target year is considered (see Section 3.2).

A Global Exploratory Comparison Of Country Self-Citations 1996-2019

Abstract: Self-citations are a key topic in evaluative bibliometrics because they can artificially inflate citation-related performance indicators. Recently, selfcitations defined at the largest scale, i.e., country self-citations, have started to attract the attention of researchers and policymakers. According to a recent research, in fact, the anomalous trends in the country self-citation rates of some countries, such as Italy, have been induced by the distorting effect of citation metrics-centered science policies. In the present study, we investigate the trends of country self-citations in 50 countries over the world in the period 1996-2019 using Scopus data. Results show that for most countries country selfcitations have decreased over time. 12 countries (

Citation Gaming Induced By Bibliometric Evaluation: A Country-Level Comparative Analysis

Abstract: It is several years since national research evaluation systems around the globe started making use of quantitative indicators to measure the performance of researchers. Nevertheless, the effects on these systems on the behavior of the evaluated researchers are still largely unknown. For investigating this topic, we propose a new inwardness indicator able to gauge the degree of scientific self-referentiality of a country. Inwardness is defined as the proportion of citations coming from the country over the total number of citations gathered by the country. A comparative analysis of the trends for the G10 countries in the years 2000-2016 reveals a net increase of the Italian inwardness. Italy became, both globally and for a large majority of the research fields, the country with the highest inwardness and the lowest rate of international collaborations. The change in the Italian trend occurs in the years following the introduction in 2011 of national regulations in which key passages of professional careers are governed by bibliometric indicators. A most likely explanation of the peculiar Italian trend is a generalized strategic use of citations in the Italian scientific community, both in the form of strategic author self-citations and of citation clubs. We argue that the Italian case offers crucial insights on the constitutive effects of evaluation systems. As such, it could become a paradigmatic case in the debate about the use of indicators in science-policy contexts.