Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets

Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies

Video anomaly detection has proved to be a challenging task owing to its unsupervised training procedure and high spatio-temporal complexity existing in real-world scenarios. In the absence of anomalous training samples, state-of-the-art methods try to extract features that fully grasp normal behaviors in both space and time domains using different approaches such as autoencoders, or generative adversarial networks. However, these approaches completely ignore or, by using the ability of deep networks in the hierarchical modeling, poorly model the spatio-temporal interactions that exist between objects. To address this issue, we propose a novel yet efficient method named Ano-Graph for learning and modeling the interaction of normal objects. Towards this end, a Spatio-Temporal Graph (STG) is made by considering each node as an object's feature extracted from a real-time off-the-shelf object detector, and edges are made based on their interactions. After that, a self-supervised learning method is employed on the STG in such a way that encapsulates interactions in a semantic space. Our method is data-efficient, significantly more robust against common real-world variations such as illumination, and passes SOTA by a large margin on the challenging datasets ADOC and Street Scene while stays competitive on Avenue, ShanghaiTech, and UCSD.
Building Specifications in the Event-B Institution

This paper describes a formal semantics for the Event-B specification language using the theory of institutions. We define an institution for Event-B, EVT, and prove that it meets the validity requirements for satisfaction preservation and model amalgamation. We also present a series of functions that show how the constructs of the Event-B specification language can be mapped into our institution. Our semantics sheds new light on the structure of the Event-B language, allowing us to clearly delineate three constituent sub-languages: the superstructure, infrastructure and mathematical languages. One of the principal goals of our semantics is to provide access to the generic modularisation constructs available in institutions, including specification-building operators for parameterisation and refinement. We demonstrate how these features subsume and enhance the corresponding features already present in Event-B through a detailed study of their use in a worked example. We have implemented our approach via a parser and translator for Event-B specifications, EBtoEVT, which also provides a gateway to the Hets toolkit for heterogeneous specification.
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients

Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, deep learning models have drawn increasing attention in healthcare. However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes. In this paper we introduce BERTSurv, a deep learning survival framework which applies Bidirectional Encoder Representations from Transformers (BERT) as a language representation model on unstructured clinical notes, for mortality prediction and survival analysis. We also incorporate clinical measurements in BERTSurv. With binary cross-entropy (BCE) loss, BERTSurv can predict mortality as a binary outcome (mortality prediction). With partial log-likelihood (PLL) loss, BERTSurv predicts the probability of mortality as a time-to-event outcome (survival analysis). We apply BERTSurv on Medical Information Mart for Intensive Care III (MIMIC III) trauma patient data. For mortality prediction, BERTSurv obtained an area under the curve of receiver operating characteristic curve (AUC-ROC) of 0.86, which is an improvement of 3.6% over baseline of multilayer perceptron (MLP) without notes. For survival analysis, BERTSurv achieved a concordance index (C-index) of 0.7. In addition, visualizations of BERT's attention heads help to extract patterns in clinical notes and improve model interpretability by showing how the model assigns weights to different inputs.
S3M: Siamese Stack (Trace) Similarity Measure

Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existing issue, 2) assign these bugs to appropriate developers, and 3) gain a general overview of the application’s bug landscape. The efficiency of report aggregation and subsequent operations heavily depends on the quality of the report similarity metric. However, a distinctive feature of this kind of report is that no textual input from the user (i.e., bug description) is available: it contains only stack trace information.In this paper, we present S3M ("extreme") — the first approach to computing stack trace similarity based on deep learning. It is based on a siamese architecture that uses a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset. Additionally, we review the impact of stack trace trimming on the quality of the results.
Connecting Images through Time and Sources: Introducing Low-data, Heterogeneous Instance Retrieval

With impressive results in applications relying on feature learning, deep learning has also blurred the line between algorithm and data. Pick a training dataset, pick a backbone network for feature extraction, and voil\`a ; this usually works for a variety of use cases. But the underlying hypothesis that there exists a training dataset matching the use case is not always met. Moreover, the demand for interconnections regardless of the variations of the content calls for increasing generalization and robustness in features. An interesting application characterized by these problematics is the connection of historical and cultural databases of images. Through the seemingly simple task of instance retrieval, we propose to show that it is not trivial to pick features responding well to a panel of variations and semantic content. Introducing a new enhanced version of the Alegoria benchmark, we compare descriptors using the detailed annotations. We further give insights about the core problems in instance retrieval, testing four state-of-the-art additional techniques to increase performance.
Enhancement of quantum correlations and a geometric phase for a driven bipartite quantum system in a structured environment

We study the role of driving in an initial maximally entangled state evolving under the presence of a structured environment in a weak and strong regime. We focus on the enhancement and degradation of maximal Concurrence when the system is driven on and out of resonance for a general evolution, as well as the effect of adding a transverse coupling among the particles of the model. We further investigate the role of driving in the acquisition of a geometric phase for the maximally entangled state. As the model studied herein can be used to model experimental situations such as hybrid quantum classical systems feasible with current technologies, this knowledge can aid the search for physical setups that best retain quantum properties under dissipative dynamics.
Data-driven feedback stabilization of switched linear systems with probabilistic stability guarantees

This paper tackles the feedback stabilization of switched linear systems under arbitrary switching. We propose a data-driven approach which allows to compute a stabilizing static feedback using only a finite set of observations of trajectories without any knowledge of the dynamics. We assume that the switching signal is not observed, and as a consequence, we aim at solving a uniform stabilization problem in which the feedback is stabilizing for all possible switching sequences. In order to generalize the solution obtained from trajectories to the actual system, probabilistic guarantees are derived via geometric analysis in the spirit of scenario optimization. The performance of this approach is demonstrated on a few numerical examples.
Unified approach to secondary effects on the CMB B-mode polarization

We develop a systematic and unified approach to estimate all possible secondary (i.e. non-primordial) nonlinear effects to the cosmic microwave background (CMB) polarization, named curve-of-sight integration approach. In this approach, the Boltzmann equation for polarized photons is rewritten in a line-of-sight integral along an exact geodesic in the perturbed universe, rather than a geodesic in the background universe used in the linear-order CMB calculation. This approach resolves the difficulty to solve the Boltzmann hierarchy with the nonlinear gravitational effects in the photon free-streaming regime and thus unifies the standard remapping approach for CMB lensing into the direct approach solving the Boltzmann equation for the nonlinear collisional effects. In this paper, we derive formulae that: (i) include all the nonlinear effects; (ii) can treat extended sources such as the contributions after the reionization. It offers a solid framework to discuss possible systematics in the standard estimation of CMB lensing by the remapping approach. As an explicit demonstration, we estimate the secondary B-mode power spectrum induced by all foreground gravitational effects: lensing, redshift, time-delay, emission-angle, and polarization-rotation effects. We define these effects properly so that they do not have any overlap, also without overlooking any effect. Then, we show that these effects only give corrections of the order of 0.001-0.01% to the standard lensing-induced B-mode power spectrum in the concordance Λ cold dark matter model. Our result confirms the reliability of using the remapping approach in upcoming CMB experiments aiming to detect the primordial gravitational waves with the tensor-to-scalar ratio of r ∼ 10-3.
Cluster dynamics studied with the phase-space minimum spanning tree approach

The origin of weakly bound objects like clusters and hypernuclei, observed in heavy-ion collisions, is of theoretical and experimental interest. It is in the focus of the experiments at RHIC and LHC since it is not evident how such weakly bound objects can survive in an environment whose hadronic decay products point to a temperature of the order of 150 MeV. It is as well one of the key research topics in the future facilities of FAIR and NICA which are under construction in Darmstadt (Germany) and Dubna (Russia), respectively. We present here first results on the cluster dynamics within the model-independent cluster recognition library ”phase-space Minimum Spanning Tree” (psMST) applied to different transport approaches: PHQMD, PHSD, SMASH and UrQMD. The psMST is based on the ”Minimum Spanning Tree” (MST) algorithm for the cluster recognition which exploits correlations in coordinate space, and it is extended to correlations of baryons in the clusters in momentum space. We show the sensitivity of the cluster formation on the microscopic realization of the n-body dynamics and on the potential interactions in heavy-ion collisions.
Molecular contrails – triggered contraction by passages of massive objects through molecular clouds

We study the effects of passages of compact objects such as stars, star clusters and black holes through molecular clouds, and propose that the gravitational interaction between the compact object and the ambient gas can lead to the formation of thin and collimated features made of dense gas, which we call “molecular contrails”. Supercritical contrails can collapse further leading to triggered star formation. The width of a molecular contrail is determined by the mass and velocity of the compact object and the velocity dispersion of the ambient molecular medium. Under typical conditions in the Milky Way, passages of stellar-mass objects lead to the formation of width d & 0.01 parsec contrails, and passages of star clusters lead to the formation of d & 1 parsec contrails. We present a few molecular contrail candidates from both categories identified from ALMA 1.3mm continuum observations of star-forming regions and the 13CO(1-0) map from the Galactic Ring Survey respectively. The contrails represent an overlooked channel where stars and gas in the Galactic disk interact to form structures. They also present a potential way of detecting dark compact objects in the Milky Way.
HD molecules at high redshift: cosmic ray ionization rate in the diffuse interstellar medium

We present a systematic study of deuterated molecular hydrogen (HD) at high redshift, detected in absorption in the spectra of quasars. We present four new identifications of HD lines associated with known $\rm H_2$-bearing Damped Lyman-α systems. In addition, we measure upper limits on the $\rm HD$ column density in 12 recently identified $\rm H_2$-bearing DLAs. We find that the new $\rm HD$ detections have similar $N({\rm HD})/N(\rm H_2)$ ratios as previously found, further strengthening a marked difference with measurements through the Galaxy. This is likely due to differences in physical conditions and metallicity between the local and the high-redshift interstellar media. Using the measured N(HD)/N(H2) ratios together with priors on the UV flux (χ) and number densities (n), obtained from analysis of $\rm H_2$ and associated C i lines, we are able to constrain the cosmic ray ionization rate (CRIR, ζ) for the new $\rm HD$ detections and for eight known HD-bearing systems where priors on n and χ are available. We find significant dispersion in ζ, from a few × 10−18 s−1 to a few × 10−15 s−1. We also find that ζ strongly correlates with χ – showing almost quadratic dependence, slightly correlates with Z, and does not correlate with n, which probably reflects a physical connection between cosmic rays and star-forming regions.
From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors

Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus on a better understanding of the impacts of different factors so as to inform preventive actions. To validate our models and findings, we divide the land of California into 4,242 grids of 0.1 degrees 0.1 degrees in latitude and longitude, and compute the risk of each grid based on spatial and temporal conditions. To verify the generalizability of our models, we further expand the scope of wildfire risk assessment from California to Washington without any fine tuning. By performing counterfactual analysis, we uncover the effects of several possible methods on reducing the number of high risk wildfires. Taken together, our study has the potential to estimate, monitor, and reduce the risks of wildfires across diverse areas provided that such environment data is available.
Tense–aspect–mood marking, language-family size and the evolution of predication

This paper proposes a Complexity Covariance Hypothesis, whereby linguistic complexity covaries with cultural and socio-political complexity, and argues for an Evolutionary Inference Principle, in accordance with which, in domains where linguistic complexity correlates positively with cultural/socio-political complexity, simpler linguistic structures are evolutionarily prior to their more complex counterparts. Applying this methodology in a case study, the covariance of linguistic and cultural/socio-political complexity is examined by means of a cross-linguistic survey of tense–aspect–mood (TAM) marking in a worldwide sample of 868 languages. A novel empirical finding emerges: all else being equal, languages from small language families tend to have optional TAM marking, while languages from large language families are more likely to exhibit obligatory TAM marking. Since optional TAM marking is simpler than obligatory TAM marking, it can, therefore, be inferred that optional TAM marking is evolutionarily prior to obligatory TAM marking: a living fossil. In conclusion, it is argued that the presence of obligatory TAM marking, correlated with the more highly grammaticalized expression of thematic-role assignment, is a reflection of a deeper property of grammatical organization, namely, the grammaticalization of predication. Thus, it is suggested that the development of agriculture and resulting demographic expansions, resulting in the emergence of large language families, are a driving force in the evolution of predication in human language. This article is part of the theme issue ‘Reconstructing prehistoric languages’.
Pantomimic fossils in modern human communication

Bodily mimesis, the capacity to use the body representationally, was one of the key innovations that allowed early humans to go beyond the ‘baseline’ of generalized ape communication and cognition. We argue that the original human-specific communication afforded by bodily mimesis was based on signs that involve three entities: an expression that represents an object (i.e. communicated content) for an interpreter. We further propose that the core component of this communication, pantomime, was able to transmit referential information that was not limited to select semantic domains or the ‘here-and-now’, by means of motivated—most importantly iconic—signs. Pressures for expressivity and economy then led to conventionalization of signs and a growth of linguistic characteristics: semiotic systematicity and combinatorial expression. Despite these developments, both naturalistic and experimental data suggest that the system of pantomime did not disappear and is actively used by modern humans. Its contemporary manifestations, or pantomimic fossils, emerge when language cannot be used, for instance when people do not share a common language, or in situations where the use of (spoken) language is difficult, impossible or forbidden. Under such circumstances, people bootstrap communication by means of pantomime and, when these circumstances persist, newly emergent pantomimic communication becomes increasingly language-like. This article is part of the theme issue ‘Reconstructing prehistoric languages’.
Buxus sempervirens and Laurus nobilis leaves from an archaeological site at 7300 cal BP (La Draga, Spain) provide a better understanding of past vegetation and human activities

Leaves are valuable, but very rare, remains in archaeological contexts, as they can provide precise information on the landscape vegetation composition, the past climatic conditions and the use of plant resources in a settlement. La Draga (Spain) is an early Neolithic site partially waterlogged, which has allowed the preservation of organic material. During the excavation of the oldest level (7300–7000 cal BP), we recovered 29 leaf samples in an optimal state of preservation. Here we explore the potential of these leaves as indicators both of the use of vegetation at the settlement and of past climatic conditions. Firstly, we determined the species identity by comparing the leaf morphological and microanatomical traits with those of contemporary individuals. Secondly, we analyzed the leaf stomatal patterning (stomatal density, index, and size) as proxies of paleoclimate during the Neolithic, when lower atmospheric CO2 and wetter conditions are documented. The leaves of La Draga were identified as Laurus nobilis and Buxus sempervirens, two evergreen species well documented at the site from other archaeobotanical records, such as charcoal and wooden implements. We found no differences in stomatal traits between the archaeological and the contemporary leaves of L. nobilis and B. sempervirens, which shows the limitation of stomatal frequency as proxies for CO2. The presence of these species at the site provides good complementary data for past woodland vegetation composition and the production of wood implements. The wide distribution of L. nobilis leaves across the site suggests that this taxon was present in the riparian forest at the site surroundings. In contrast, leaves of B. sempervirens were grouped together, which might indicate that they belonged to the same branch, probably transported as raw material from nearby forests to the settlement. This study illustrates the value of archaeological leaves to provide a better understanding on the past vegetation and human activities.
Geoarchaeology of the Derkul River floodplain, west Kazakhstan: Soil formation, sediment accumulation and human settlement

The natural environment and prehistoric human activity in the Holocene floodplains of the Low Volga River and in the southern Urals are important research objects in geomorphology, soil science and archaeology. The alternating sequences of soil-alluvium sequences represent a sedimentary archive with chrono-stratigraphic records of human land use, sediment accumulation and soil formation. The central floodplain of the Derkul River (western Kazakhstan) was studied using the multiproxy approach to investigate the soil-alluvium sequence dating from 8000 years ago until the present and containing a buried Stagnic Fluvic Phaeozem. Alluvial deposition began with stream sedimentation in the early Holocene, followed by a prolonged period of soil formation under low water conditions (7.5–5.7 ka cal year BP). Humans started habitation the floodplain in 6.6–5.7 ka cal year BP. Increased atmospheric precipitation in 5.7–3.4 ka cal year BP accelerated alluvial sedimentation. Soil formation followed the synsedimentation model. Conditions for the stationary land use by humans in the floodplain were less optimal. In 3.4–2.1 ka cal year BP, alluvial sedimentation was less pronounced, and solonetz carbonated soils were formed, reflecting increased climate aridity and continentality. Humans returned to the floodplain area, but in 2.1–1.9 ka cal year BP, the flooding frequency increased, and in 1.9 ka cal year BP, the surface of the floodplain passes to function in a high floodplain. Thus, synsedimentation formation resumed, with colluvium discharge from the adjacent hills being the main source of material input.