Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking Surface Facts to Large-Scale Knowledge Graphs

Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject";"relation";"object") triples. These facts are, however, merely surface forms, the ambiguity of which impedes their downstream usage; e.g., the surface phrase"Michael Jordan"may refer to either the former basketball player or the university professor. Knowledge Graphs (KGs), on the other hand, contain facts in a canonical (i.e., unambiguous) form, but their coverage is limited by a static schema (i.e., a fixed set of entities and predicates). To bridge this gap, we need the best of both worlds: (i) high coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of KGs. In order to achieve this goal, we propose a new benchmark with novel evaluation protocols that can, for example, measure fact linking performance on a granular triple slot level, while also measuring if a system has the ability to recognize that a surface form has no match in the existing KG. Our extensive evaluation of several baselines show that detection of out-of-KG entities and predicates is more difficult than accurate linking to existing ones, thus calling for more research efforts on this difficult task. We publicly release all resources (data, benchmark and code) on https://github.com/nec-research/fact-linking.
Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously curated data, they often overlook out-of-distribution (OoD) situations. Yet, in practical settings -- especially safety-critical ones like autonomous driving -- common corruptions can arise. Addressing this oversight, we introduce a comprehensive robustness test suite, RoboDepth, encompassing 18 corruptions spanning three categories: i) weather and lighting conditions; ii) sensor failures and movement; and iii) data processing anomalies. We subsequently benchmark 42 depth estimation models across indoor and outdoor scenes to assess their resilience to these corruptions. Our findings underscore that, in the absence of a dedicated robustness evaluation framework, many leading depth estimation models may be susceptible to typical corruptions. We delve into design considerations for crafting more robust depth estimation models, touching upon pre-training, augmentation, modality, model capacity, and learning paradigms. We anticipate our benchmark will establish a foundational platform for advancing robust OoD depth estimation.
Computationally Efficient Electromagnetic Transient Power System Studies using Bayesian Optimization

The power system of the future will be governed by complex interactions and non-linear phenomena, that should be studied more and more through computationally expensive software simulations. To solve the abovementioned problems, power system engineers face problems with following characteristics: (i) a computationally expensive simulator, (ii) non-linear functions to optimize and (iii) lack of abundance of data. Existing optimization settings involving EMT-type simulations have been developed, but mainly use a deterministic model and optimizer, which may be computationally inefficient and do not guarantee finding a global optimum. In this paper, an automation framework based on Bayesian Optimization is introduced, and applied to two case studies. It is found that the framework has the potential to reduce computational effort, outperform deterministic optimizers and is applicable to a multitude of problems. Nevertheless, it was found that the output of the Bayesian Optimization depends on the number of evaluations used for initialization, and in addition, careful selection of surrogate models, which should be subject to future investigation.
Unlocking the Transferability of Tokens in Deep Models for Tabular Data

Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks. However, such a paradigm becomes particularly challenging with tabular data when there are discrepancies between the feature sets of pre-trained models and the target tasks. In this paper, we propose TabToken, a method aims at enhancing the quality of feature tokens (i.e., embeddings of tabular features). TabToken allows for the utilization of pre-trained models when the upstream and downstream tasks share overlapping features, facilitating model fine-tuning even with limited training examples. Specifically, we introduce a contrastive objective that regularizes the tokens, capturing the semantics within and across features. During the pre-training stage, the tokens are learned jointly with top-layer deep models such as transformer. In the downstream task, tokens of the shared features are kept fixed while TabToken efficiently fine-tunes the remaining parts of the model. TabToken not only enables knowledge transfer from a pre-trained model to tasks with heterogeneous features, but also enhances the discriminative ability of deep tabular models in standard classification and regression tasks.
Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings

Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artificial artifacts installed in the environment or excessively expensive equipment, that is not suitable at scale. A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest. In order to leverage state-of-the-art methods in deep learning for object pose estimation, large amounts of data need to be collected and annotated. In this work, we provide an approach to the annotation of large datasets of monocular images without the need for manual labor. Our approach localizes cameras in space, unifies their location with a motion capture system, and uses a set of linear mappings to project 3D models of objects of interest at their ground truth 6D pose locations. We test our pipeline on a custom dataset collected from a system of eight cameras in an industrial setting that mimics the intended area of operation. Our approach was able to provide consistent quality annotations for our dataset with 26, 482 object instances at a fraction of the time required by human annotators.
Universal Knowledge Graph Embeddings

A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect only the semantics of a single knowledge graph, and embeddings for different knowledge graphs are not aligned, e.g., they cannot be used to find similar entities across knowledge graphs via nearest neighbor search. However, knowledge graph embedding applications such as entity disambiguation require a more global representation, i.e., a representation that is valid across multiple sources. We propose to learn universal knowledge graph embeddings from large-scale interlinked knowledge sources. To this end, we fuse large knowledge graphs based on the owl:sameAs relation such that every entity is represented by a unique identity. We instantiate our idea by computing universal embeddings based on DBpedia and Wikidata yielding embeddings for about 180 million entities, 15 thousand relations, and 1.2 billion triples. Moreover, we develop a convenient API to provide embeddings as a service. Experiments on link prediction show that universal knowledge graph embeddings encode better semantics compared to embeddings computed on a single knowledge graph. For reproducibility purposes, we provide our source code and datasets open access at https://github.com/dice-group/Universal_Embeddings
ORANI-G: A General Equilibrium Model of the Australian Economy

ORANI is an applied general equilibrium (AGE) model of the Australian economy which is widely used by academics and by economists in the government and private sectors. We describe a generic version of the model, ORANI-G, designed both for expository purposes and as a convenient starting-point for those wishing to construct their own AGE model. ORANI-G forms the basis of an annual modelling course, and has been adapted to build models of South Africa, Pakistan, Sri Lanka, Fiji, South Korea, Denmark, Vietnam, Thailand, Indonesia, Philippines and both Chinas. Our description of the model's equations and database is closely integrated with an explanation of how the model is solved. Indeed, the model equations are presented in the syntax, resembling ordinary algebraic notation, used by the GEMPACK modelling system to specify the model. The document includes: an outline of the structure of the model and of the appropriate interpretations of the results of comparative-static and forecasting simulations; a description of the solution procedure; a brief description of the data, emphasising the general features of the data structure required for such a model; a complete description of the theoretical specification of the model framed around the TABLO Input file which implements the model in GEMPACK.
Tectonomagnetism and tectonoelectricity

The last four years has seen a considerable waning of efforts in the U.S. to use the inherent stress sensitivity of the magnetization of rocks in tectonically active regions as a tool for understanding and, perhaps predicting, earthquake activity and volcanic eruptions. The primary reasons for this decreased interest derive from accumulating evidence that the mean deviatoric stress release during earthquakes (and the ambient shear stress near active faults) is lower than than had been previously assumed and, secondly, the observational limits of magnetometers separated by about 10 km, after application of a variety of noise reduction techniques, appear to be no better than about 0.1 nT. Lower estimates of the average change in deviatoric stress associated with earthquake rupture correspondingly reduce the fields calculated from tectonomagnetic models. At the same time, some unexpected and exciting results have been obtained which indicate many aspects of magnetic field changes near active faults and volcanoes are still not completely understood. The more important of these results are: 1) the observation of correlated magnetic, strain, uplift, and gravity changes during the latter stages of a remarkable deformation episode near the San Andreas fault in southern California, 2) indications of anomalous change in magnetic fields along the San Andreas fault to the southeast of Palm Springs, 3) an unambiguous observation of a volcanomagnetic effect at Mt. St. Helens prior to an extrusive eruption, and 4) the first observations of coseismic seismomagnetic effects on two independent magnetometers during the July 8, 1986, ML 5.9 North Palm Springs earthquake. A resurgence of interest has occurred during the quadrennium in a related area concerning electrical phenomena and electromagnetic effects related to earthquakes and volcanoes. Mechanisms have been proposed to explain the occurrence of earthquakes lights and the possibility of electromagnetic emission prior to the 1960 Chilean earthquake has been proposed. Self potential anomalies have been observed before and during several eruptions on Kilauea volcano in Hawaii.
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a"proof of concept"that RL can be used to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.
A retinotopic code structures the interaction between perception and memory systems

The authors show that functionally paired visual and memory brain areas share a common neural code, which structures their communication. This code is visual in nature and uses a push–pull dynamic to translate information between vision and memory. Conventional views of brain organization suggest that regions at the top of the cortical hierarchy processes internally oriented information using an abstract amodal neural code. Despite this, recent reports have described the presence of retinotopic coding at the cortical apex, including the default mode network. What is the functional role of retinotopic coding atop the cortical hierarchy? Here we report that retinotopic coding structures interactions between internally oriented (mnemonic) and externally oriented (perceptual) brain areas. Using functional magnetic resonance imaging, we observed robust inverted (negative) retinotopic coding in category-selective memory areas at the cortical apex, which is functionally linked to the classic (positive) retinotopic coding in category-selective perceptual areas in high-level visual cortex. These functionally linked retinotopic populations in mnemonic and perceptual areas exhibit spatially specific opponent responses during both bottom-up perception and top-down recall, suggesting that these areas are interlocked in a mutually inhibitory dynamic. These results show that retinotopic coding structures interactions between perceptual and mnemonic neural systems, providing a scaffold for their dynamic interaction.
Poor Social Functioning: A Potentially Modifiable Risk Factor for Pneumonia in the Elderly

Background Most risk factors for developing community-acquired pneumonia (CAP) are age-related and chronic medical conditions; modifying these factors can be challenging, especially in the elderly. Poor social functioning, however, has a negative impact on medical conditions but can be improved through interventions. Therefore, the social functioning domain of health-related quality of life (HRQOL) may be a modifiable risk factor for the development of CAP. This study investigated the association between poor social functioning and the incidence of CAP in elderly individuals. Methodology We conducted a retrospective cohort study using a dataset from 2018 to 2021, derived from an annual questionnaire-based survey of a cohort of community-dwelling people aged 75 years or older (the Sukagawa Study). The dataset included social functioning subscale scores of HRQOL obtained from the Eight-Item Short Form (SF-8) questionnaire. Health insurance claims data were matched with these HRQOL data. For each participant, the exposure (HRQOL) was measured, and outcomes (incidence of CAP) were observed yearly from 2018 through 2021. Results The four observation years had a total of 17,016 observation periods among 6,513 participants. The annual incidence rate of CAP was 0.90-1.77%. Lower social functioning was associated with a higher risk of CAP. Specifically, for each standard deviation difference in social functioning score, the adjusted rate ratio for CAP incidence was 1.26 (95% confidence interval (CI) = 1.08-1.48). In a subgroup analysis, the association between social functioning and CAP differed by sex (p = 0.037). Specifically, the adjusted rate ratio for CAP incidence was 1.41 (95% CI = 1.17-1.70) in men and 1.00 (95% CI = 0.76-1.35) in women. Conclusions Poor social functioning is an important risk factor for CAP in the elderly, especially in men. Interventions that improve social functioning may help to prevent CAP.
Hip thrust and back squat training elicit similar gluteus muscle hypertrophy and transfer similarly to the deadlift

We examined how set-volume equated resistance training using either the back squat (SQ) or hip thrust (HT) affected hypertrophy and various strength outcomes. Untrained college-aged participants were randomized into HT (n = 18) or SQ (n = 16) groups. Surface electromyograms (sEMG) from the right gluteus maximus and medius muscles were obtained during the first training session. Participants completed 9 weeks of supervised training (15–17 sessions), before and after which gluteus and leg muscle cross-sectional area (mCSA) was assessed via magnetic resonance imaging. Strength was also assessed prior to and after the training intervention via three-repetition maximum (3RM) testing and an isometric wall push test. Gluteus mCSA increases were similar across both groups. Specifically, estimates [(−) favors HT (+) favors SQ] modestly favored the HT versus SQ for lower [effect ±SE, −1.6 ± 2.1 cm2; CI95% (−6.1, 2.0)], mid [−0.5 ± 1.7 cm2; CI95% (−4.0, 2.6)], and upper [−0.5 ± 2.6 cm2; CI95% (−5.8, 4.1)] gluteal mCSAs but with appreciable variance. Gluteus medius + minimus [−1.8 ± 1.5 cm2; CI95% (−4.6, 1.4)] and hamstrings [0.1 ± 0.6 cm2; CI95% (−0.9, 1.4)] mCSA demonstrated little to no growth with small differences between groups. mCSA changes were greater in SQ for the quadriceps [3.6 ± 1.5 cm2; CI95% (0.7, 6.4)] and adductors [2.5 ± 0.7 cm2; CI95% (1.2, 3.9)]. Squat 3RM increases favored SQ [14 ± 2 kg; CI95% (9, 18),] and hip thrust 3RM favored HT [−26 ± 5 kg; CI95% (−34, −16)]. 3RM deadlift [0 ± 2 kg; CI95% (−4, 3)] and wall push strength [−7 ± 12N; CI95% (−32, 17)] similarly improved. All measured gluteal sites showed greater mean sEMG amplitudes during the first bout hip thrust versus squat set, but this did not consistently predict gluteal hypertrophy outcomes. Squat and hip thrust training elicited similar gluteal hypertrophy, greater thigh hypertrophy in SQ, strength increases that favored exercise allocation, and similar deadlift and wall push strength increases.
Sealing properties of faults and their influence on water-alternating-gas injection efficiency in the Snorre field, northern North Sea

Within the Snorre field, faults need careful consideration in reservoir modeling and simulation work. In this field, a static seal capacity for oil as much as about 5 bar (3.44 104 Pa) is observed across important faults. In a dynamic setting, the seal capacities for oil become about one order of magnitude higher than for the static situation. Water-alternating-gas (WAG) injection has been chosen as the main recovery mechanism. Because capillary seal capacities as much as 15 bar (1.03 105 Pa) are found for gas to flow across faults in the reservoir, the distribution of gas and efficiency of WAG to a large degree depend on the fault patterns. For the purpose of evaluating the flow paths of the injected fluids, tracer data and time-lapse seismic data were used.Tracer and time-lapse data confirm that the WAG process in Snorre depends on the geometry and properties of medium- to small-scale faults. As a consequence of intrareservoir faults, injected gas is trapped in fault-bounded segments, whereas the water is allowed to spread out laterally. The observations suggest that flow of oil and gas across faults in the Snorre field must be treated individually in flow simulators.
An experimental design for obtaining DNA of a target species and its diet from a single non‐invasive genetic protocol

Abstract Next‐generation sequencing technology has enabled accurate insights into the diet of wildlife species. The protocols for faecal sample collection and DNA extraction for diet analysis have differed from those focusing on target species, even in most studies combining questions on both aspects. We designed an experiment to evaluate two protocols using 11 parameters and select a single one that will generate both target species (Asiatic wild ass, Equus hemionus, in Israel) and diet DNA, as an effective strategy to minimise time, effort, and cost without hampering efficiency. In Protocol A, we swabbed the outer surface of faecal boluses and extracted DNA using a Stool Kit, while for Protocol B, we homogenised faecal matter from inside the bolus followed by extraction using a Powersoil Kit. Protocol A performed significantly better for four parameters, which included, for the target species, microsatellite amplification success and the quantity of the GAPDH gene; and for its diet, the number of exact sequence variants (ESVs) obtained at genus level and plant genus richness. However, there was no significant difference in the amplification success of sex‐linked and plant markers, total reads at genus level, number of genera obtained and plant genus composition. Although we chose Protocol A, both protocols yielded results for the target species and its diet, demonstrating that one single protocol can be used for both purposes, although a pilot study is recommended to optimise the protocol for specific systems. This strategy may also be useful for studies combining target species and their gut microbiome and parasitic load.