bluuebunny/crossref_metadata_2025 huggingface dataset:
(Re)wilding London: Fabric, politics, and aesthetics
Authors: Turnbull Jonathon , Fry Tom , Lorimer Jamie
Published: February 2025

Rewilding has become established as a new mode of nature conservation. There is a growing body of science, ambitious landscape‐scale plans, and increasing political will. But until recently, cities and the urban were neglected; the idea of urban rewilding seen as oxymoronic. Of late, however, there has been a shift, with growing enthusiasm amongst metropolitan authorities, civil society, and citizens in major cities like London, New York, Toronto, and Melbourne, as well as smaller cities around the world. Noting a dearth of research on urban rewilding, this paper proffers an agenda for future geographical research into this emerging mode of urban nature conservation. The paper deploys the spatial‐optical metaphor of diffraction, which originates in feminist science studies, to explore how rewilding as a socioecological practice developed in rural areas is changed when it encounters the urban, drawing on recent work in urban theory to trace how the urban might inform a new model of rewilding better equipped for the novel ecosystems and political configurations of the increasingly urban Anthropocene. It begins by defining how the sociomaterial urban fabric diffracts rewilding's practices and principles. It then examines how the distinct political economy of cities shapes the power dynamics of urban rewilding. The aesthetic dimensions of urban rewilding are then highlighted as important factors shaping how this emerging mode of urban nature recovery unfolds. In conclusion, we note how the nascency of urban rewilding presents opportunities for geographers to prefigure a progressive mode of Anthropocene conservation, and we advocate an affirmative style of critique that allies with practitioners and policy‐makers. We establish future priorities for research into urban rewilding, including exploring minoritarian cultures of nature, comparative analysis of diverse cases, and examination of rural–urban linkages.
Keep Calm and Make a Blacklist: Child Labour and International Trade
Authors: Cifarelli Flavia
Published: February 2025

I investigate how the provision of information regarding the use of child labour in foreign production affects imports, using a staggered adoption design. I exploit a list, published annually by the US Department of Labor, with information on tradable goods produced using child labour in different countries. I find that the inclusion in the list leads to a decrease in US imports from that country equal to 15%, taking into account potential spillover effects. I provide evidence suggesting that this effect is not due to any change in price but is driven by a reduction in both US firms' intermediate input demand and demand by US final consumers, with the former channel being slightly larger.
Preliminary determination of bacterial contamination of whole blood units in a Ghanaian blood bank: Providing evidence to improve transfusion safety
Authors: Aidoo Nana Benyin , Ramirez‐Arcos Sandra , Gillard Laurie , Azumah Daniel Edem , Adatsi Angela
Published: February 2025

Background and Objectives

Transfusion‐associated bacterial sepsis poses a significant risk to patient safety. This study aimed to determine the rate of bacterial contamination of whole blood (WB) collected at the Cape Coast Teaching Hospital (CCTH) as a quality control and quality assurance activity.

Materials and Methods

One‐hundred and three WB units collected between January and April of 2018 were screened for bacterial contamination. Bacteria isolated from positive cultures were identified and subjected to antimicrobial testing. WB recipients were followed up for clinical symptoms.

Results

Of the 103 WB units tested, 33 (32%) were contaminated with bacteria. Gram‐positive organisms accounted for 67% of the isolates, including coagulase‐negative Staphylococcus, Staphylococcus aureus and Bacillus spp., while Gram‐negative bacteria comprised 33% of the isolates, with Citrobacter freundii, Serratia marcescens, Escherichia coli and Providencia stuartii being identified. Resistance to antibiotics varied between species. No septic transfusion events were reported involving WB units tested in this study.

Conclusion

The high percentage of contaminated WB units collected at the CCTH provided evidence‐based data for the implementation of improved donor skin disinfection processes and the use of blood diversion pouches in 2019. These approaches allowed CCTH to comply with Ghanaian regulatory entities.
The Role of Embodied Carbon in Sustainable Construction: A Review of Qatar's Practices and Perspectives
Authors: Rayaru Kandiyil Dilba , Sadique Monower , Lee Denise , Amoako-Attah Joseph , Al Mufti Rafal
Published: February 2025

This study explores the urgent need for an embodied carbon (EC) assessment framework within Qatar's construction sector, driven by the country's rapid development and high carbon intensity in construction materials, such as cement and steel. Employing a systematic literature review through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and Visualization of Similarities (VOS) viewer for bibliometric analysis, this study identifies major gaps in Qatar-specific EC data and regulation. It highlights global best practices, particularly those from countries with mandated EC regulations, and discusses their potential adaptation to Qatar's unique environmental and economic context. This study advocates the establishment of a comprehensive EC database to inform construction practices aligned with Qatar's sustainability goals under its National Vision 2030. The findings suggest that a regionally adapted EC framework would significantly aid Qatar in reducing greenhouse gas emissions, given the country's heavy reliance on energy-intensive materials and its extreme climate. The study concludes with recommendations for the policy integration of EC assessments in Qatar's building sector, aiming to support sustainable urban development and climate resilience in the face of intensifying environmental challenges.
Optimization of Chamber Structure and Ignition of a Rotary Engine Based on the Multi-Population-Subgroup Genetic Algorithm
Authors: Wen Huan , Zou Run , Li Liangyu , Sun Qinghai , Wang Nana , Su Tiexiong
Published: February 2025

The rotary engine (RE) is characterized by high-speed and a long and narrow combustion chamber. This leads to problems such as uneven distribution of the fuel–air mixture, difficulty in the ignition, and difficulty in rapid propagation of the flame to the trailing side of the combustion chamber, which in turn has an adverse impact on its combustion efficiency. In view of this, this study proposed a multi-population-subgroup genetic algorithm (MPSGA) with a faster convergence rate and more significant optimization based on the multi-population genetic algorithm. The performance of the optimization method was verified by test functions. This method is applied to the comprehensive matching optimization of chamber structure and ignition of the RE to significantly improve the combustion performance. The results showed that compared to the original scheme, the best matching scheme of the chamber structure parameters and ignition parameters of the RE obtained by using the MPSGA increased the explosive pressure by 39.3% and the thermal efficiency by 9.8%.
Targeting c-MYC and gain-of-function p53 through inhibition or degradation of the kinase LZK suppresses the growth of HNSCC tumors
Authors: Funk Amy L. , Katerji Meghri , Afifi Marwa , Nyswaner Katherine , Woodroofe Carolyn C. , Edwards Zoe C. , Lindberg Eric , Bergman Knickole L. , Gough Nancy R. , Rubin Maxine R. , Karpińska Kamila , Trotter Eleanor W. , Dash Sweta , Ries Amy L. , James Amy , Robinson Christina M. , Difilippantonio Simone , Karim Baktiar O. , Chang Ting-Chia , Chen Li , Xu Xin , Doroshow James H. , Ahel Ivan , Marusiak Anna A. , Swenson Rolf E. , Cappell Steven D. , Brognard John
Published: February 2025

The worldwide annual frequency and lethality of head and neck squamous cell carcinoma (HNSCC) is not improving, and thus, new therapeutic approaches are needed. Approximately 70% of HNSCC cases have either amplification or overexpression of
MAP3K13
, which encodes the kinase LZK. Here, we found that LZK is a therapeutic target in HNSCC and that small-molecule inhibition of its catalytic function decreased the viability of HNSCC cells with amplified
MAP3K13
. Inhibition of LZK suppressed tumor growth in
MAP3K13
-amplified xenografts derived from HNSCC patients. LZK stabilized the transcription factor c-MYC through its kinase activity and gain-of-function mutants of p53 in a kinase-independent manner. We designed a proteolysis-targeting chimera (PROTAC) that induced LZK degradation, leading to decreased abundance of both c-MYC and gain-of-function p53, and reduced the viability of HNSCC cells. Our findings demonstrate that LZK-targeted therapeutics, particularly PROTACs, may be effective in treating HNSCCs with
MAP3K13
amplification.
Review of BCG immunotherapy for bladder cancer
Authors: Liatsos George D. , Mariolis Ilias , Hadziyannis Emilia , Bamias Aristotelis , Vassilopoulos Dimitrios
Published: February 2025

For several decades, intravesical
Bacillus
Calmette–Guérin (iBCG) immunotherapy has been the gold standard adjuvant treatment for high-risk and selected intermediate-risk patients with non-muscle-invasive bladder cancer (NMIBC). In this review, the mechanisms of iBCG immune-mediated anti-cancer activity and resistance are presented. Furthermore, a literature review of short-term and systemic iBCG-related side effects was performed. A high incidence (75.5%) of iBCG-related short-term, self-limiting adverse events was observed, while more severe iBCG-related local/systemic complications (iBCG-rL/SCs) that required medical treatment or hospitalization occurred at a lower rate (2.35%). Disseminated was the most common form of iBCG-rSCs, while two-thirds of the cases were classified as infectious. The implementation of molecular-based techniques resulted in significantly higher diagnostic rates. Anti-tuberculous treatment (ATT) is the mainstay of treatment, while in patients with any iBCG-rL/SC form involving the vasculature, ATT should be combined with surgery. Local and osteoarticular forms have the lowest mortality, but their management necessitates severe and debilitating surgical procedures. The overall iBCG-attributed mortality in patients with iBCG-rL/SC was 7.4%, with disseminated, vascular, and lung involvements exhibiting the highest rates. Given the global shortage of BCG for the last two decades, as well as the paucity of effective options for iBCG-refractory or relapsing NMIBC patients, new therapeutic strategies are being tested with promising early results.
Accessible support at a national scale: the use and value of virtual learning assistants across multiple undergraduate institutions
Authors: Pribbenow Christine M. , Harrington D'Andrew , Rele Chinmay P. , Sandlin Katie M. , Leung Wilson , Lopatto David , Reed Laura K.
Published: February 2025

The Genomics Education Partnership (GEP;
thegep.org
) is a collaboration of more than 260 faculty from over 200 colleges and universities across the continental United States and Puerto Rico, all of whom are engaged in bringing Course-based Undergraduate Research Experiences (CUREs) centered in genomics and bioinformatics to their students. The purpose of the GEP-CURE is to ensure all undergraduate students have access to research experiences in genomics, regardless of the funding and resources available at their institutions. The GEP community provides many resources to facilitate implementation of the genomics curriculum at collaborating institutions, including extensive support for both faculty and undergraduate students. Faculty receive training to implement the curriculum, ongoing professional development, access to updated curriculum, and a community of practitioners. During the COVID-19 pandemic, the GEP developed a virtual learning assistant (LA) program to provide real-time support in GEP activities and research to all students, regardless of their institution, while they were participating in the GEP-CURE. A mixed-methods descriptive study was conducted about this program and draws from quantitative data gathered about the scope and use of the program, as well as the value of the program, as indicated by the undergraduates themselves from their post-course survey responses. Additionally, seven LAs who served in this role between 2021 and 2023 participated in interviews to help the GEP better understand how this resource was used by GEP students, the needs of the students, and to identify the conditions in which this resource could be replicated in other courses.
The DnaJK chaperone of
Bacillus subtilis
post-transcriptionally regulates gene expression through the YlxR(RnpM)/RNase P complex
Authors: Ogura Mitsuo , Kanesaki Yu , Yoshikawa Hirofumi , Haga Koki
Published: February 2025

To survive in harsh natural environments, translation and mRNA metabolism must be tightly and coordinately controlled, as saving biological costs increases fitness. However, the roles of protein chaperones in this control system are unclear. This study proposes the novel aspect of the link between translation and mRNA metabolism, that is, the co-translational DnaJK chaperone activity is involved in changes in mRNA metabolism by RNase P. We found that the expression of
proBA,
which encodes proline biosynthetic enzymes, is regulated by
ylxR
(
rnpM
) through the
proBA
promoter. YlxR(RnpM), which is associated with RNase P, was also involved in the posttranscriptional regulation of
proBA
. To clarify this posttranscriptional regulation, we screened transposon (Tn)-inserted mutants for cells with low
proB::lacZ
expression and identified the DnaJK chaperone as a regulator of
proB
. To explore the possibility that the complex of YlxR(RnpM) and RNase P might work with DnaJK, we performed an epistatic analysis using the
lacZ
fusions, which revealed that the regulation of
proB
by DnaJK/YlxR(RnpM)/RNase P, that is, co-translational chaperone activity, controlled mRNA metabolism. RNA sequencing analysis of cells deficient in the RNA component of RNase P (
rnpB
) revealed that 261 genes were upregulated in the
rnpB
::Tn strain. Among them, we identified
yoyD/yodF, besA
, and
epeXE,
which were also under the control of DnaJK/YlxR(RnpM)/RNase P regulatory cascade. Finally, we performed yeast two-hybrid analysis using DnaK as bait and identified two genes,
spoIVCA
and
nupG
, whose expression was post-transcriptionally regulated by DnaJK but independent of YlxR(RnpM). These results suggest a broader role for posttranscriptional gene regulation by DnaJK.

IMPORTANCE

Bacillus subtilis
lacking the DnaJK chaperone has not been reported to exhibit a distinct phenotype. However, our study revealed proline-dependent growth in a minimal medium in the
dnaJ
::Tn strain. Inhibition of
spoIVCA
expression in this strain was identified as a probable cause of the sporulation deficiency in previous and current studies using a single cell-level analysis. We also observed posttranscriptional regulation of
proBA
by the DnaJK and YlxR(RnpM)/RNase P complex. LacZ analyses of
proB::lacZ
in different backgrounds suggested that the above regulation ultimately functions in mRNA metabolism. In DnaJK-deficient cells, the nascent peptide may be misfolded, and if DnaJK chaperone activity is lost, such a signal may be transferred to RNase P. Therefore,
proBA
mRNA may be degraded in an RNase P-dependent manner if the misfolding of the polypeptide translated from this mRNA is detected. This system is useful for reducing the biological costs of futile mRNA elongation.
Stress adaptation under
in vitro
evolution influences survival and metabolic phenotypes of clinical and environmental strains of
Vibrio cholerae
El-Tor
Authors: Adade Nana Eghele , Ahator Stephen Dela , García-Romero Inmaculada , Algarañás Macarena , Appiah Vincent , Valvano Miguel A. , Duodu Samuel
Published: February 2025

Bacterial adaptation to stress can lead to phenotypic variants with diverse levels of niche competitiveness, pathogenicity, and antimicrobial resistance. In this work, we employed experimental evolution to investigate whether exposure to various stress conditions results in new phenotypic and metabolic properties in clinical and environmental strains of
Vibrio cholerae
. Our findings revealed the emergence of variants with metabolic and genetic variations and enhanced survival under stress compared to the parental isolates. Phenotypic changes in the evolved variants included colony morphology, biofilm formation, and the appearance of proteolytic and hemolytic activities. The variants demonstrated metabolic changes in the preferred use of carbon, nitrogen, phosphorous, and sulfur substrates, while the genetic changes included single nucleotide polymorphisms (SNPs), breakpoints, translocations, and single nucleotide insertions and deletions. Mutations in genes encoding EAL and HD-GYP domain-containing proteins correlated with increased biofilm formation and different colony morphotypes. The combined analysis of the metabolic and genomic data pointed to pathways implicated in stress survival. The environmental strains were generally more pathogenic than the clinical strains in the
Galleria mellonella
infection model prior to the experimental evolution, and these differences did not change in the evolved variants. This study highlights the contribution of stress conditions as drivers for the evolution of genetic modifications and metabolic adaptation in
V. cholerae
, which may explain the continuous evolution of El-Tor biotype strains toward variants with improved survival in the environment.

IMPORTANCE

How
Vibrio cholerae
, the causative agent of cholera, survives during the periods between outbreaks remains a critical question. Using experimental evolution based on serial bacterial passages in culture media mimicking diverse environmental stress conditions, we investigated whether clinical and environmental isolates of
V. cholerae
develop changes in survival and in their metabolism. The evolved variants exhibited alterations in colony morphology, biofilm formation, and metabolism, including changes in the preferred use of carbon, nitrogen, phosphorous, and sulfur substrates. These changes were accompanied by various genetic modifications, notably in genes encoding second messenger molecules that regulate multiple biochemical pathways implicated in stress survival and increased pathogenic potential. Our results suggest a continuous evolution of
V. cholerae
strains toward variants displaying increased survival under environmental stress conditions that may also be encountered in the human host.
Tp40: a new potential prognostic and diagnostic marker for syphilis
Authors: Yao Jiangchen , Xie Bibo , Ding Xuan , Yu Han , Lin Ting , Duan Junxia , Zhang Xiaohong , Ling Peng , Zhao Feijun
Published: February 2025

The characterization of
Treponema pallidum
proteins is of great significance for the study of the prevention, diagnosis, and pathogenesis of syphilis. The structures and functions of many
T. pallidum
proteins, including the Tp40 (Tp0134) protein, remain unknown. To explore the expression pattern of the Tp40 protein within
T. pallidum
, we established an animal model of syphilis infection to compare the variations in serum Tp40 antibody levels between Live and Inactivated Tp groups. The results indicated that the absorbance of Tp40-enzyme-linked immunosorbent assay (ELISA) did not increase in the Inactivated group and the Untreated group, but it increased in the Live Tp infection group, suggesting that the Tp40 protein is an
in vivo
-induced antigen that is only actively expressed during infection. In addition, the localization of the Tp40 protein was determined by the gel microdrop method. We found that Tp40 may be a transmembrane protein with a signaling peptide present in the intima periplasm of
T. pallidum
. Finally, 468 patients’ sera were collected for diagnostic value evaluation. Tp40-ELISA, LZ-ELISA, and Shanghai Kehua rapid plasma reagin (RPR) reagent kit showed a high degree of consistency in 468 serum samples. This suggests that Tp40 could be a valuable diagnostic antigen. The results of this study provide a new reference for the study of the pathogenesis, protein function, and diagnosis of syphilis.

IMPORTANCE

In recent years, syphilis, as a chronic infectious disease, has once again attracted much attention.
Treponema pallidum
exhibits remarkable infectivity, concealment, and aggressiveness, posing considerable challenges to its prevention and control. The underlying pathogenic mechanisms remain elusive, and during the infection process, the roles of numerous proteins are still unclear. Through protein characterization in this study, it was found that the Tp40 protein is highly likely to be a transmembrane protein with a signal peptide and may be located in the periplasm. Besides, based on experiments with animal models and the detection of human serum samples, we believe that the Tp40 protein is a potential
in vivo
-induced antigen of
T. pallidum
that can be used for serological diagnosis of syphilis. This study conducted a preliminary exploration of the Tp40 protein and provided a meaningful reference for further exploration of the functional mechanism of the Tp40 protein and its significance in clinical diagnosis.
Capsule: An Out-of-Core Training Mechanism for Colossal GNNs
Authors: Xiang Yongan , Ding Zezhong , Guo Rui , Wang Shangyou , Xie Xike , Zhou S. Kevin
Published: February 2025

Cutting-edge platforms of graph neural networks (GNNs), such as DGL and PyG, harness the parallel processing power of GPUs to extract structural information from graph data, achieving state-of-the-art (SOTA) performance in fields such as recommendation systems, knowledge graphs, and bioinformatics. Despite the computational advantages provided by GPUs, these GNN platforms struggle with scalability challenges due to the colossal graphical structures processed and the limited memory capacities of GPUs. In response, this work introduces Capsule, a new out-of-core mechanism for large-scale GNN training. Unlike existing out-of-core GNN systems, which use main or secondary memory as operative memory and use CPU kernels during non-backpropagation computation, Capsule uses GPU memory and GPU kernels. By substantially leveraging the parallelization capabilities of GPUs, Capsule significantly enhances GNN training efficiency. In addition, Capsule can be smoothly integrated to mainstream open-source GNN frameworks, DGL and PyG, in a play-and-plug manner. Through a prototype implementation and comprehensive experiments on real datasets, we demonstrate that Capsule can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems.
Cohesiveness-aware Hierarchical Compressed Index for Community Search on Attributed Graphs
Authors: Wang Yuxiang , Peng Zhangyang , Ke Xiangyu , Xu Xiaoliang , Wu Tianxing , Gao Yuan
Published: February 2025

Community search on attributed graphs (CSAG) is a fundamental topic in graph data mining. Given an attributed graph
G
and a query node
q
, CSAG seeks a structural- and attribute-cohesive subgraph from
G
that contains
q
. Exact methods based on graph traversal are time-consuming, especially for large graphs. Approximate methods improve efficiency by pruning the search space with heuristics but still take hundreds of milliseconds to tens of seconds to respond, hindering their use in time-sensitive applications. Moreover, pruning strategies are typically tailored to specific algorithms and their cohesiveness metrics, making them difficult to generalize. To address this, we study a general approach to accelerate various CSAG methods. We first present a proximity graph-based, cohesiveness-aware hierarchical index that accommodates different cohesiveness metrics. Then, we present two optimizations to enhance the index's navigability and reliability. Finally, we design a compressed storage structure for space-efficient indexing. Experiments on real-world datasets show that integrating our index with existing mainstream CSAG methods results in an average 30.7× speedup while maintaining a comparable or even better attribute cohesiveness.
Constant Optimization Driven Database System Testing
Authors: Zhang Chi , Rigger Manuel
Published: February 2025

Logic bugs are bugs that can cause database management systems (DBMSs) to silently produce incorrect results for given queries. Such bugs are severe, because they can easily be overlooked by both developers and users, and can cause applications that rely on the DBMSs to malfunction. In this work, we propose
Constant-Optimization-Driven Database Testing
(CODDTest) as a novel approach for detecting logic bugs in DBMSs. This method draws inspiration from two well-known optimizations in compilers: constant folding and constant propagation. Our key insight is that for a certain database state and query containing a predicate, we can apply constant folding on the predicate by replacing an expression in the predicate with a constant, anticipating that the results of this predicate remain unchanged; any discrepancy indicates a bug in the DBMS. We evaluated CODDTest on five mature and extensively-tested DBMSs--SQLite, MySQL, CockroachDB, DuckDB, and TiDB--and found 45 unique, previously unknown bugs in them. Out of these, 24 are unique logic bugs. Our manual analysis of the state-of-the-art approaches indicates that 11 logic bugs are detectable only by CODDTest. We believe that CODDTest is easy to implement, and can be widely adopted in practice.
Efficient Index Maintenance for Effective Resistance Computation on Evolving Graphs
Authors: Liao Meihao , Li Cheng , Li Rong-Hua , Wang Guoren
Published: February 2025

In this paper, we study a problem of index maintenance on evolving graphs for effective resistance computation. Unlike an existing matrices-based index, we show that the index can be efficiently maintained by directly preserving samples of random walks and loop-erased walks. This approach not only enables efficient storage and rapid query response but also supports effective maintenance. We propose a novel approach to convert edge updates into
landmark
node updates. Building upon this, we present two new update algorithms for random walk and loop-erased walk samples respectively. Both algorithms update samples without requiring complete resampling, ensuring accuracy and high efficiency. A particularly challenging and innovative technique involves updating loop-erased walks. Here we develop a novel and powerful cycle decomposition technique for loop-erased walks, enabling us to update samples at the cycle level rather than the node level, significantly enhancing efficiency. Furthermore, we show that both of our methods achieve an Õ (1) time complexity per edge update in real-world graphs under a mild assumption. We conduct extensive experiments using 10 large real-world datasets to evaluate the performance of our approaches. The results show that our best algorithm can be up to two orders of magnitude faster than the baseline methods.
Largest Triangle Sampling for Visualizing Time Series in Database
Authors: Rui Lei , Huang Xiangdong , Song Shaoxu , Wang Chen , Wang Jianmin , Cao Zhao
Published: February 2025

In time series visualization, sampling is used to reduce the number of points while retaining the visual features of the raw time series. Area-based Largest Triangle Sampling (LTS) excels at preserving perceptually critical points. However, the heuristic solution to LTS by sequentially sampling points with the locally largest triangle area (a.k.a. Largest-Triangle-Three-Buckets, LTTB) suffers from suboptimal solution and query inefficiency. We address the shortcomings by contributing a novel Iterative Largest Triangle Sampling (ILTS) algorithm with convex hull acceleration. It refines the sampling results iteratively, capturing a broader perspective by integrating more points in each iteration. Remarkably, we prove that the largest triangle can always be found in the precomputed convex hulls, making the iterative sampling still efficient. Experiments demonstrate increased visual quality over state-of-the-art baselines and significant speedups over the brute force approach.
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
Authors: Zhao Pinxue , Zhang Hailin , Fu Fangcheng , Nie Xiaonan , Liu Qibin , Yang Fang , Peng Yuanbo , Jiao Dian , Li Shuaipeng , Xue Jinbao , Tao Yangyu , Cui Bin
Published: February 2025

Nowadays, Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications. However, long context training poses great challenges considering the constraint of GPU memory. It not only leads to substantial activation memory consumption during training, but also incurs considerable memory fragmentation. To facilitate long context training, existing frameworks have adopted strategies such as recomputation and various forms of parallelisms. Nevertheless, these techniques rely on redundant computation or extensive communication, resulting in low Model FLOPS Utilization (MFU). In this paper, we propose MEMO, a novel LLM training framework designed for fine-grained activation memory management. Given the quadratic scaling of computation and linear scaling of memory with sequence lengths when using FlashAttention, we offload memory-consuming activations to CPU memory after each layer's forward pass and fetch them during the backward pass. To maximize the swapping of activations without hindering computation, and to avoid exhausting limited CPU memory, we implement a token-wise activation recomputation and swapping mechanism. Furthermore, we tackle the memory fragmentation issue by employing a bi-level Mixed Integer Programming (MIP) approach, optimizing memory reuse across transformer layers. Empirical results demonstrate that MEMO achieves an average of 1.97x and 1.80x MFU compared to Megatron-LM and DeepSpeed, respectively. This improvement is attributed to MEMO's ability to minimize memory fragmentation, reduce recomputation and intensive communication, and circumvent the delays associated with the memory reorganization process due to fragmentation. By leveraging fine-grained activation memory management, MEMO facilitates efficient training of 7B LLM with 1 million sequence length on just 8 A800 GPUs, achieving an MFU of 52.30%.
VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity
Authors: Li Meng , Chai Huayi , Luo Siqiang , Dai Haipeng , Gu Rong , Zheng Jiaqi , Chen Guihai
Published: February 2025

Learned indexes, which model key-value data structures by machine learning models, have been extensively studied. However, the fastest immutable learned indexes (e.g., RMI) do not provide the same tight lookup bounds as classical indexes such as B-trees. There are learned indexes that provide tight bounds (e.g., PGM) but those fall short in query performance. This gives rise to an interesting open question: whether there exists a learned index that simultaneously achieves state-of-the-art empirical performance and matching complexity?

In this paper, we give a positive answer to this standing problem.We propose two new online model-building policies: (1) simplifying distribution by the adoption of a proper granularity (i.e., grouping multiple keys together for model-building) and (2) actively tuning distribution through key repositioning. Additionally, we introduce a general framework that combines these two policies for performance optimization under a given memory budget. We put everything together to design VEGA, a learned index that simultaneously achieves competitive theoretical and empirical performance compared to state-of-the-art learned indexes. We conducted extensive evaluations, demonstrating VEGA achieves both better lookup and building performance.
Improving Domain Generalization for Image Captioning with Unsupervised Prompt Learning
Authors: Wei Hongchen , Chen Zhenzhong
Published: February 2025

Pretrained Visual Language Models (PVLMs) have demonstrated impressive zero-shot abilities in image captioning when accompanied by prompt prefixes. However, PVLMs using fixed prompt prefixes may suffer from mode collapse when dealing with multiple target domains. Some studies have attempted to mitigate this issue by constructing instruction datasets and injecting domain prior knowledge through hand-crafted prompts. However, this approach inevitably incurs high manual and time costs. In this paper, we propose an unsupervised prompt learning method to improve domain Generalization for Image Captioning (GeneIC), which learns a domain-specific prompt vector for the target domain without requiring annotated data. GeneIC aligns visual and language modalities with a pre-trained CLIP model, thus optimizing the domain-specific prompt vector from two aspects: attribute and semantic consistency. Specifically, GeneIC first generates attribute-transferred images with differing attributes while retaining semantic similarity with the original images. Then, GeneIC uses CLIP to measure the similarity between the images and the generated sentences. By exploring the variable and invariant features in the original and attribute-transferred images, attribute consistency constrains the attribute change direction of both images and sentences to learn domain-specific knowledge. The semantic consistency directly measures the similarity between the generated sentences and images to ensure the accuracy and comprehensiveness of the generated sentences. Consequently, GeneIC only optimizes the prompt vectors, which effectively retains the knowledge in the large model and introduces domain-specific knowledge. Experiments show that GeneIC exhibits superior generalization performance compared to state-of-the-art methods on multiple target domain datasets.
Explainable Benchmarking for Iterative Optimization Heuristics
Authors: van Stein Niki , Vermetten Diederick , V. Kononova Anna , Bäck Thomas
Published: February 2025

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a very limited number of scenarios, algorithm configurations and hyper-parameter settings are explored, leading to incomplete and often biased insights and results. This paper presents a novel approach that we call explainable benchmarking. We introduce the IOHxplainer software library, for systematic analysing the performance of various optimization algorithms and the impact of their different components and hyperparameters. We showcase the methodology in the context of two modular optimization implementations. Through this library, we examine the impact of different algorithmic components and configurations, offering insights into their performance across diverse scenarios. We provide a systematic method for evaluating and interpreting the behaviour and efficiency of iterative optimization heuristics in a more transparent and comprehensible manner, aiming to improve future benchmarking and algorithm design practices.
Communication-Efficient Federated Learning for Heterogeneous Clients
Authors: Li Ying , Wang Xingwei , Li HaoDong , Donta Praveen Kumar , Huang Min , Dustdar Schahram
Published: February 2025

Federated learning stands out as a promising approach within the domain of edge computing, providing a framework for collaborative training on distributed datasets without necessitating data sharing. However, federated learning involves the frequent transmission of machine learning model updates between the server and clients, resulting in high communication costs. Additionally, heterogeneous clients can further complicate the Federated Learning process and deteriorate performance. To address these challenges, we propose
adaptive self-knowledge distillation-based quality- and reputation-aware cross-device federated learning
(ASDQR) - an efficient communication and inference framework designed for heterogeneous clients. ASDQR initiates the process by selecting high-reputation and high-quality clients to be involved in federated learning, significantly impacting communication efficiency and inference effectiveness. ASDQR also introduces a model of adaptive local self-knowledge distillation that incorporates multiple local personalized historical knowledge for more accurate inference, allowing the historical level to be dynamically adjusted across time. Finally, we present an inference-effective aggregation scheme that assigns higher weights to important and reliable local model updates based on clients’ contribution degrees when performing global model aggregation. ASDQR consistently outperforms baseline methods across all datasets and communication rounds, achieving 9.0% higher accuracy than FedAvg, 6.59% higher than MOON, 0.29% higher than FedProx, 0.2% higher than PFedSD, and 0.08% higher than FedMD on the MNIST dataset at 100 communication rounds. Similar improvements are observed on CIFAR, HAR, and WISDM datasets, demonstrating the robustness and efficiency of ASDQR in federated learning with non-IID data.
21st Human-Competitive Results Awards ("Humies") 2024
Authors: Goodman Erik
Published: February 2025

The GECCO 2024 Conference was held July 14--18 this year, once again in hybrid mode. Nearly 400 people came to Melbourne, Australia to attend in person, and an additional 275 or so registered to attend virtually. The finals of the Humies competition (see www.human-competitive.org for the Call for Entries) was a plenary session on Wednesday, July 17, that was attended by about 150 people. The eight finalists earlier selected by the judges (from among 16 entries, and based on the papers and entry forms they submitted) presented their work in 10-minute talks, or, in one case, a pre-recorded video.
Automated Scheduling of GECCO 2023
Authors: Kheiri Ahmed , Pylyavskyy Yaroslav , Jacko Peter
Published: February 2025

In this article, the authors describe the process they used for the automated scheduling of talks and other sessions in GECCO 2023, using a hyper heuristic-based optimisation algorithm.
Modeling Protection Systems in Smart Active Distribution Grids for a Performance Analysis
Authors: Patil Amit Dilip , De Meer Hermann , Heegaard Poul Einar , Helvik Bjarne Emil
Published: February 2025

Distributed renewable energy sources increase the fault level in distribution grids, impacting protection systems and potentially causing misoperations like blinding, which increases the fault clearing time. High impedance faults result in low fault currents, further delaying protection tripping. Communication-based adaptive protection systems can improve protection sensitivity but are prone to delays, affecting fault clearing times. Therefore, this research addresses how the impact of distributed renewable energy sources, fault impedance and communication delay on protection systems can be quantified in terms of operation time. A discrete event simulation model is proposed to study protection performance in active distribution grids. Time distribution assumptions are investigated to capture the circuit breaker trip time accurately. The relative fault level of distribution and external grid is formalized. The impact of the relative fault level and communication delay on the protection system is determined by measuring fault clearing times using discrete event simulation. This impact is further quantified using a new protection performance index. Results show that for the system studied, protection blinding is critical for low impedance faults in grids with high fault levels, while high impedance faults are critical in grids with low fault levels. Moreover, sympathetic tripping is seen at increased distribution grid fault levels and fault impedance. Furthermore, while communication systems reduce fault clearing times, increased delays harm protection systems.
Utilizing Autonomous Airport Parking Facilities as Virtual Power Plants to Provide Frequency Containment Reserve
Authors: Vereno Dominik , Schütz René , Polanec Katharina , Maldonato Francesco , Mielke-Sulz Florian , Kögler Damien , Nicodemus Rolf , Weber Johanna , Gross Jounes-Alexander , Neureiter Christian , Khodaei Amin , Lehnhoff Sebastian
Published: February 2025

With the rise of inverter-based renewables, grid stability is increasingly strained. Electric vehicles (EVs) offer unique potential in enhancing this stability through intelligent (dis-)charging control; by aggregation, they can provide significant balancing services to network operators. To exploit this opportunity, airport parking is particularly well suited due to the large number of vehicles with predictable departure times. By using the novel Automated Valet Charging (AVC) technology, parking facilities can optimally schedule EVs---autonomously maneuvering cars to and from charge points and connecting them robotically. We propose using AVC-capable parking facilities as virtual power plants (VPPs), providing fast-acting frequency containment reserve (FCR). This study's key contributions are: 1) presenting the AVC VPP concept and formalizing its two core optimization tasks, 2) developing a modular co-simulation architecture for comprehensive evaluation, 3) performing preliminary experiments, to assess feasibility and to identify simulation challenges, and 4) outlining strategic recommendations for practical AVC VPP implementation. The results suggest the concept's potential in utilizing airport EV parking for FCR, promising additional revenue for parking operators without sacrificing customer satisfaction. However, for conclusive assessment, extended simulation models are necessary. Furthermore, prequalification emerges as a significant obstacle to profitable real-world applications. Ultimately, AVC VPPs may help with transforming EVs from being a burden on the grid to a valuable asset.
Monitoring Germany's Core Energy System Dataset: A Data Quality Analysis of the Marktstammdatenregister
Authors: Kotthoff Florian , Muschner Christoph , Tepe Deniz , Vogt Esther , Hülk Ludwig
Published: February 2025

The energy system in Germany consists of a large number of distributed facilities, including millions of PV plants, wind turbines, and biomass plants. To understand and manage this system efficiently, accurate and reliable information about all facilities is essential. In Germany, the Marktstammdatenregister (MaStR) serves as a central registry for units of the energy system. The reliability of this data is critical for the registry's usefulness, but few validation studies have been published.

In this work we provide a review of existing literature that relies on data from the MaStR and thereby show the registry's importance. We then build a data and testing pipeline for relevant data of the registry, with a focus on the two aspects of facility's location and size. All test results are published online in a reproducible workflow. Hence, this work contributes to a reliable data foundation for the German energy system and starts an open validation process of the Marktstammdatenregister from an academic perspective.
An Algorithm for Modelling Rolling Intrinsic Battery Trading on the Continuous Intraday Market
Authors: Semmelmann Leo , Dresselhaus Jannik , Miskiw Kim K. , Ludwig Jan , Weinhardt Christof
Published: February 2025

We introduce a novel rolling intrinsic algorithm to model battery-based trading on the continuous intraday market. Our approach leverages a discretization method with a subsequent step-wise optimization based on continuous price updates and complete uncertainty about future prices. We show the high potential of generating additional profits in the continuous intraday market in comparison to using an ID3-based representation of trading, which is frequently used in literature. Thereby, we realistically model actual trading behavior on the intraday market. We publish our code open-source to contribute to the further analysis of battery storage systems on the intraday market.
Hardware-in-the-Loop Simulation Environment for Validating Distribution System Control Applications
Authors: Otte Marcel , Krüger Carsten , Das Pratyush , Rohjans Sebastian , Lehnhoff Sebastian
Published: February 2025

Customer premises are becoming increasingly important for the power system as their flexibility potential is growing with the ongoing roll-out of electric vehicles, storage systems, photovoltaics, and heat pumps. However, the flexibility has the potential to be used not only within the customer's premises but also to serve different markets and grid operators, leading to use cases with multiple actors and systems. Thus, the validation of distribution system control applications with a focus on cross-system and cross-actor communication is needed. This work conceptualises a suitable hardware-in-the-loop simulation environment for validating distribution system control applications with the customer premises in a hardware-in-the-loop setup. The realisation shows how grid operators, field devices, a meter operator with smart meters, the customer, and market participants can be integrated into the simulation environment. Accordingly, suitable standards and market roles are identified and implemented. Derived from that concept, this work gives an outlook on future use cases to be validated using the simulation environment.
Computing Resultant of Quaternionic Polynomials
Authors: Koprowski Przemysław
Published: February 2025

There are a number of ways to generalize the notion of the resultant to non-commutative polynomials, in particular, to quaternionic polynomials. We show that Erić's definition of the resultant admits similar optimization as the standard commutative resultant. Hence, it can be effectively computed.
Semi-Algebraic Structure and Central Paths in Nonlinear Optimization
Authors: Basu Saugata , Mohammad-Nezhad Ali
Published: February 2025

We consider the concept of so-called central path in nonlinear optimization that lies at the heart of interior point methods. Using singularity theory and real algebraic geometry, we provide conditions for the existence, convergence, and analyticity of central paths of nonlinear optimization problems. We also prove quantitative bounds on the convergence rate and analyticity of the central path for the special case of semi-definite optimization.
CompEd Announcement
Authors: Craig Michelle , Mann Samuel , Tshukdu Ethel
Published: February 2025

An ACM SIGCSE conference, CompEd is held outside of North America and Europe every other year. ACM CompEd 2025 will be hosted by the University of Botswana.
Equity Spotlight: Benjamin Xie
Authors: Smith Julie M. , Wallace Charles , Xie Benjamin
Published: February 2025

Dr. Benjamin Xie is a Postdoctoral Fellow at the Stanford Human-Computer Interaction Group & Graduate School of Education and an Incoming Assistant Professor at the University of Denver Computer Science Department
My Unexpected Adventure Pursuing a Career in Motion
Authors: Hagan John
Published: February 2025

My interest in criminology grew as the Vietnam War escalated. I applied to two Canadian graduate schools and flipped a coin. The coin recommended the University of Toronto, but I chose the University of Alberta, which had a stronger criminology program. I wrote a dissertation about criminal sentencing, which led to an Assistant Professorship at the University of Toronto. Dean Robert Pritchard of Toronto's Law School encouraged my work and later successfully nominated me for a Distinguished University Professorship. My interests continued to grow in international criminal law. A MacArthur Distinguished Professorship at Chicago's Northwestern University and the American Bar Foundation facilitated my research at the Tribunal for the Former Yugoslavia. I followed this by studying the crime of genocide in Sudan and later the trial of Chicago's Detective Jon Burge. Burge oversaw the torture of more than 100 Black men on Chicago's South Side. US Attorney Patrick Fitzgerald prosecuted Burge when Illinois prosecutors would not. Despite many good things about Chicago, the periodic corruption of the government and police was not among them.