Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds

Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for bridging the gap between these levels of abstraction remain elusive. In this work, we investigate the internal mechanisms of neural networks through the lens of neural population geometry, aiming to provide understanding at an intermediate level of abstraction, as a way to bridge that gap. Utilizing manifold capacity theory (MCT) from statistical physics and manifold alignment analysis (MAA) from high-dimensional statistics, we probe the underlying organization of task-dependent manifolds in deep neural networks and macaque neural recordings. Specifically, we quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models and demonstrate how these geometric analyses are connected to the decodability of task-relevant information. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry, potentially opening up many future research avenues in both machine learning and neuroscience.
GreenScan: Towards large-scale monitoring the health of urban trees using mobile sensing

Healthy urban greenery is a fundamental asset to mitigate climate change phenomenons such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While current visual or instrumented inspection techniques can help take effective measures, they often require a high amount of human labor, making frequent assessments infeasible at a city-wide scale. In this paper, we present GreenScan, a ground-based sensing system designed to provide health assessment of urban trees at high spatio-temporal resolutions, with low costs. The system utilises thermal and multi-spectral imaging sensors fused using custom computer vision models in order to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this approach illustrates the potential of autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low-costs.
Neural-network-based regularization methods for inverse problems in imaging

This review provides an introduction to - and overview of - the current state of the art in neural-network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for regularizing inverse problems in imaging. Distinguishing features of this review are, among others, an easily accessible introduction to learned generators and learned priors, in particular diffusion models, for inverse problems, and a section focusing explicitly on existing results in function space analysis of neural-network-based approaches in this context.
SAVAE: Leveraging the variational Bayes autoencoder for survival analysis

As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored medical data. Current methods often make assumptions about the relations between data that may not be valid in practice. In response, we introduce SAVAE (Survival Analysis Variational Autoencoder), a novel approach based on Variational Autoencoders. SAVAE contributes significantly to the field by introducing a tailored ELBO formulation for survival analysis, supporting various parametric distributions for covariates and survival time (as long as the log-likelihood is differentiable). It offers a general method that consistently performs well on various metrics, demonstrating robustness and stability through different experiments. Our proposal effectively estimates time-to-event, accounting for censoring, covariate interactions, and time-varying risk associations. We validate our model in diverse datasets, including genomic, clinical, and demographic data, with varying levels of censoring. This approach demonstrates competitive performance compared to state-of-the-art techniques, as assessed by the Concordance Index and the Integrated Brier Score. SAVAE also offers an interpretable model that parametrically models covariates and time. Moreover, its generative architecture facilitates further applications such as clustering, data imputation, and the generation of synthetic patient data through latent space inference from survival data.
The Projection Method: a Unified Formalism for Community Detection

We present the class of projection methods for community detection that generalizes many popular community detection methods. In this framework, we represent each clustering (partition) by a vector on a high-dimensional hypersphere. A community detection method is a projection method if it can be described by the following two-step approach: 1) the graph is mapped to a query vector on the hypersphere; and 2) the query vector is projected on the set of clustering vectors. This last projection step is performed by minimizing the distance between the query vector and the clustering vector, over the set of clusterings. We prove that optimizing Markov stability, modularity, the likelihood of planted partition models and correlation clustering fit this framework. A consequence of this equivalence is that algorithms for each of these methods can be modified to perform the projection step in our framework. In addition, we show that these different methods suffer from the same granularity problem: they have parameters that control the granularity of the resulting clustering, but choosing these to obtain clusterings of the desired granularity is nontrivial. We provide a general heuristic to address this granularity problem, which can be applied to any projection method. Finally, we show how, given a generator of graphs with community structure, we can optimize a projection method for this generator in order to obtain a community detection method that performs well on this generator.
Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code

We present a method for systematically evaluating the correctness and robustness of instruction-tuned large language models (LLMs) for code generation via a new benchmark, Turbulence. Turbulence consists of a large set of natural language $\textit{question templates}$, each of which is a programming problem, parameterised so that it can be asked in many different forms. Each question template has an associated $\textit{test oracle}$ that judges whether a code solution returned by an LLM is correct. Thus, from a single question template, it is possible to ask an LLM a $\textit{neighbourhood}$ of very similar programming questions, and assess the correctness of the result returned for each question. This allows gaps in an LLM's code generation abilities to be identified, including $\textit{anomalies}$ where the LLM correctly solves $\textit{almost all}$ questions in a neighbourhood but fails for particular parameter instantiations. We present experiments against five LLMs from OpenAI, Cohere and Meta, each at two temperature configurations. Our findings show that, across the board, Turbulence is able to reveal gaps in LLM reasoning ability. This goes beyond merely highlighting that LLMs sometimes produce wrong code (which is no surprise): by systematically identifying cases where LLMs are able to solve some problems in a neighbourhood but do not manage to generalise to solve the whole neighbourhood, our method is effective at highlighting $\textit{robustness}$ issues. We present data and examples that shed light on the kinds of mistakes that LLMs make when they return incorrect code results.
Do On-demand Ride-sharing Services Aﬀect Traﬃc Congestion? Evidence from Uber Entry

Sharing economy platforms leverage information technology (IT) to provide services that re-distribute unused or underutilized assets to individuals who are willing to pay for the services. Its creative business models have disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to match demand with supply in real time. In this research, we investigate the impact of Uber, a peer-to-peer mobile on-demand ride-sharing platform, on traﬃc congestion in the urban areas of the United States. Based on a unique data set combining data of Uber entry and the Urban Mobility Report, we empirically examine whether the entry of Uber on-demand ride-sharing services aﬀects traﬃc congestion using a diﬀerence-in-diﬀerences framework. Our ﬁndings provide evidence that after entering an urban area, ride-sharing services such as Uber signiﬁcantly decrease traﬃc congestion time, congestion costs, and excessive fuel consumption. To further assess the robustness of the main results, we perform additional analyses including the use of alternative measures, instrumental variables, placebo tests, heterogeneous eﬀects, and a relative time model with more granular data. We discuss a few plausible mechanisms to explain our ﬁndings as well as their implications for the platform-based sharing economy.
A Concise Concurrent B+-Tree for Persistent Memory

Persistent memory (PM) presents a unique opportunity for designing data management systems that offer improved performance, scalability, and instant restart capability. As a widely used data structure for managing data in such systems, B+-Tree must address the challenges presented by PM in both data consistency and device performance. However, existing studies suffer from significant performance degradation when maintaining data consistency on PM. To settle this problem, we propose a new concurrent B+-Tree, CC-Tree, optimized for PM. CC-Tree ensures data consistency while providing high concurrent performance, thanks to several technologies, including partitioned metadata, log-free split, and lock-free read. We conducted experiments using state-of-the-art indices, and the results demonstrate significant performance improvements, including approximately 1.2-1.6x search, 1.5-1.7x insertion, 1.5-2.8x update, 1.9-4x deletion, 0.9-10x range scan, and up to 1.55-1.82x in hybrid workloads.
Synaptotagmin 7 docks synaptic vesicles for Doc2α -triggered asynchronous neurotransmitter release

The molecular basis of asynchronous neurotransmitter release remains enigmatic despite decades of intense study. Synaptotagmin (syt) 7 and Doc2 have both been proposed as Ca 2+ sensors that trigger this mode of exocytosis, but conflicting findings have led to controversy. Here, we demonstrate that at excitatory mouse hippocampal synapses, Doc2α is the major Ca 2+ sensor for asynchronous release, while syt7 supports this process through activity-dependent docking of synaptic vesicles. In synapses lacking Doc2α, asynchronous release after single action potentials is strongly reduced, while deleting syt7 has no effect. However, in the absence of syt7, docked vesicles cannot recover on millisecond timescales. Consequently, both synchronous and asynchronous release depress from the second pulse on during repetitive activity. By contrast, synapses lacking Doc2α have normal activity -dependent docking, but continue to exhibit decreased asynchronous release after multiple stimuli. Moreover, disruption of both Ca 2+ sensors is non-additive. These findings result in a new model whereby syt7 drives activity-dependent docking, thus ‘feeding’ synaptic vesicles to Doc2 for asynchronous release during on-going transmission.
Exploring Visual Relationships via Transformer-based Graphs for Enhanced Image Captioning

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial aspect of IC is the accurate depiction of visual relations among image objects. Visual relations encompass two primary facets: content relations and structural relations. Content relations, which comprise geometric positions content (i.e., distances and sizes) and semantic interactions content (i.e., actions and possessives), unveil the mutual correlations between objects. In contrast, structural relations pertain to the topological connectivity of object regions. Existing Transformer-based methods typically resort to geometric positions to enhance the visual relations, yet only using the shallow geometric content is unable to precisely cover actional content correlations and structural connection relations. In this paper, we adopt a comprehensive perspective to examine the correlations between objects, incorporating both content relations (i.e., geometric and semantic relations) and structural relations, with the aim of generating plausible captions. To achieve this, firstly, we construct a geometric graph from bounding box features and a semantic graph from the scene graph parser to model the content relations. Innovatively, we construct a topology graph that amalgamates the sparsity characteristics of the geometric and semantic graphs, enabling the representation of image structural relations. Secondly, we propose a novel unified approach to enrich image relation representations by integrating semantic, geometric, and structural relations into self-attention. Finally, in the language decoding stage, we further leverage the semantic relation as prior knowledge to generate accurate words. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our model, with improvements of CIDEr from 128.6% to 136.6%. Codes have been released at https://github.com/CrossmodalGroup/ER-SAN/tree/main/VG-Cap.
Cross‐Market Integration and Sabotage

Sabotage activities often raise controversies and regulatory concerns due to the potential negative effects on competition and social welfare. These concerns are amplified when firms serving complementary markets integrate due to the integrated firm’s capability to engage in multi‐market sabotage. We note that the integrated firm’s incentive to engage in sabotage activities and the potential impact on social welfare has not been examined, and this drives the primary focus of our study. Interestingly, we find that the integrated firm may not have the incentive to engage in sabotage at all. We show that the integrated firm prefers to engage in sabotage (single‐market or multi‐market) only when it has cost advantages over its rivals in at least one of the markets. At the extreme, a certain level of sabotage actions could even force some product combinations out of the market and thus, there might be a need for regulatory intervention. A counter‐intuitive result is that under certain market conditions, the integrated firm’s sabotage activities correspond to those that would optimize social welfare.
Psychological, endocrine and polygenic predictors of emotional well-being during the COVID-19 pandemic in a longitudinal birth cohort

Abstract The COVID-19 pandemic severely affected the lives of families and the well-being of both parents and their children. Various factors, including prenatal stress, dysregulated stress response systems, and genetics may have influenced how the stress caused by the pandemic impacted the well-being of different family members. The present work investigated if emotional well-being during the COVID-19 pandemic could be predicted by developmental stress-related and genetic factors. Emotional well-being of 7–10 year-old children (n = 263) and mothers (n = 241) (participants in a longitudinal German birth cohort (POSEIDON)) was assessed during the COVID-19 pandemic using the CRISIS questionnaire at two time periods (July 2020–October 2020; November 2020–February 2021). Associations of the children’s and mothers’ well-being with maternal perceived stress, of the children’s well-being with their salivary and morning urine cortisol at 45 months, and polygenic risk scores (PRSs) for depression, schizophrenia, loneliness were investigated. Lower emotional well-being was observed in both children and mothers during compared to before the pandemic, with the children’s but not the mothers’ emotional well-being improving over the course of the pandemic. A positive association between the child and maternal emotional well-being was found. Prenatally assessed maternal perceived stress was associated with a lower well-being in children, but not in mothers. Cortisol measures and PRSs were not significantly associated with the children’s emotional well-being. The present study confirms that emotional well-being of children and mothers are linked, and were negatively affected by the COVID-19 pandemic, with differences in development over time.
High CD44 expression identifies rare chemoresistant leukemic cells endowed with enhanced E-Selectin binding in T-ALL

T-cell acute lymphoblastic leukemia (T-ALL) is a hematopoietic malignancy characterized by an increased proliferation and incomplete maturation of T-cell progenitors. Despite therapeutic improvements, relapses are often of bad prognosis. Therapeutic vulnerabilities and chemoresistance mechanisms arising from cell plasticity induced by the bone marrow (BM) microenvironment remain an important field of investigation. Employing single cell RNA sequencing (scRNAseq) of human T-ALL cells recovered from adipocyte-rich and -poor BM, a distinct leukemic stem cell (LSC) population defined by quiescence and elevated CD44 level (Ki67neg/lowCD44high) expression is identified in both territories. In vivo chemotherapy demonstrated that the LSC population evades drug treatment. Patient sample analyses confirmed the presence of Ki67neg/lowCD44high LSC both at diagnosis and relapse that displayed a specific transcriptomic signature. Interestingly, the intense expression of CD44 in T-ALL Ki67neg/lowLSC was associated with E-selectin binding. Importantly, when 39 human T-ALL samples were analyzed, the E-selectin binding ability was found significantly higher in Relapse/Refractory compared to drug-sensitive patients. These findings characterize a T-ALL LSC population with chemoresistant properties and shade light on new strategies for prognostic stratification while opening avenues for novel therapeutic options.
Contrasting mineral assemblages in polymetamorphic rocks from South Transdanubia (Hungary)

In the northern part of the Tisza Superunit (Hungary), the Vajta-3 borehole crosscuts basement rocks. The Al-rich metapelites contain contrasting mineral assemblages : an older kyanite + staurolite + almandine assemblage, which may be referred to a Barrovian-type metamorphie event, and a younger andalu­ site + biotite + (almandine) assemblage, indicating a low-pressure event. In spite of the complete lack of geochronological data, a two-stage metamorphie history for the Tisza Superunit does fit the picture emerging from the literature for the whole Hungarian basement. Key-words : Hungary, basement, pre-Alpine, metamorphism, mineral chemistry.
Comparison of Soil Phosphorus Concentration in Farm Restored and Reference Wetlands in Lake County, IL

The soil in the Midwest is fertile for agriculture use and therefore a lot of the wetlands have been turned into farmland. Wetlands can act as a sink for excess nutrients such as phosphorus. In part due to their value for nutrient storage, restoration of wetlands has become more frequent, including restoration of wetlands on former farmland. I am interested in phosphorus and the potential of wetlands to either store or release phosphorus. I compared differences in soil reactive phosphorus of restored and reference wetlands. This study compares Prairie Wolf Slough (PWS), a restored wetland, to two reference wetlands, both located less than 10 miles from Prairie Wolf Slough with similar hydrology, soils, and vegetation. I measured soil reactive phosphorus (SRP) in soil cores (to a depth of 0.15 meters). ANOVA analysis found no significant difference in SRP in the restored wetland compared to the reference wetlands. Although the hypothesis was not supported, the findings can be used as preliminary data for further investigation of phosphorus in wetlands.
Vacillation cycles in WRF simulations of hurricane Katrina

The intensification of hurricane Katrina (2005) in non-hydrostatic simulations with the WRF model is compared to a hydrostatic simulation conducted in a previous study with the Australian Bureau of Meteorology’s Tropical Cyclone Limited Area Prediction System (TCLAPS). During the intensification both simulations exhibit inner-core vacillation cycles between highly asymmetric and more symmetric phases in the low-level potential vorticity (PV) and vertical velocity, although their evolution and structure are more complex in WRF. Although in WRF the lowlevel PV vacillates between symmetric and more asymmetric stages, similar to the hydrostatic TCLAPS simulation, no fully monopolar structure develops in asymmetric phases; the PV maintains its ring-like structure during the intensification. The asymmetric phases in WRF are characterised by isolated PV anomalies breaking from the eyewall and propagating towards the centre. While stirring high PV into the centre episodically, these isolated PV anomalies cause a stepwise drop in central pressure.
Моделирование и прогнозирование развития туристских дестинаций

This article deals with the creation and development of the tourist destinations. The authors describe different types of development-related destination appeal, propose a logically interconnected sequence of stages in the formation of the desired profile of a tourist destination, and show that, in order to ensure effective functioning, it is imperative that economic and mathematical modeling to assess the potential of the current and achievable competitiveness of tourist destinations be used. The proposed model allows to calculate the quantitative effect of attracting investment to the development of tourist destinations. The defining characteristics are the coefficient of efficiency of economic activity of a tourist destination, and the efficiency ratio of a tourist destination. The authors introduce a new measure — the coefficient of tourist loyalty to a destination (coefficient of return tourist business) and propose a formula for calculating the integral index of the efficiency (competitiveness) of a tourist destination. In the article, the authors consider the basic principles of forecasting, which are to be taken into account in the course of the development-forecast model construction; provide a description of forecast models, and identify the properties required of an efficient forecasting model.