Miracle-dz/UniFAR-FTUs huggingface dataset:
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions, and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE. Experimental results show that ConReader makes the prediction more interpretable and achieves new state-of-the-art on two CCE tasks in both conventional and zero-shot settings.
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.
Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing

We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing environmental contacts typically assume a contact type (e.g. point or line), does not capture contact/no-contact transitions, and only works with basic geometric-shaped objects. Neural Contact Fields are the first method that can track arbitrary multi-modal extrinsic contacts without making any assumptions about the contact type. Our key insight is to estimate the probability of contact for any 3D point in the latent space of object's shapes, given vision-based tactile inputs that sense the local motion resulting from the external contact. In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations. In addition to Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of simulated extrinsic contact interactions to enable further research in this area. Project page: https://github.com/carolinahiguera/NCF
Statistics of High-Throughput Characterization of Microbial Interactions

An active area of research interest is the inference of ecological models of complex microbial communities. Inferring such ecological models entails understanding the interactions between microbes and how they affect each other's growth. This dissertation employs a statistical perspective to contribute further to the knowledge currently addressing this problem. Part I explains how high-throughput droplet-based microfluidics technology can be used to screen for microbial interactions. An explicit, statistical framework is motivated and developed that can guide the analysis of data from such experiments. Part II explains how it might be possible to predict, based on the experimental setup, how much data will be produced to infer given microbial interactions. Running the experiment once without incubating the droplets turns out to be necessary to make such predictions. Part III demonstrates the feasibility of inferring microbial interactions from the data produced by these experiments. Relevant ideas from the microbiological and ecological literature are recast into an explicit, statistical framework.
An introduction to programming Physics-Informed Neural Network-based computational solid mechanics

Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-based computational solid mechanics. Besides, two prevailingly used physics-informed loss functions for PINN-based computational solid mechanics are summarised. Moreover, numerical examples ranging from 1D to 3D solid problems are presented to show the performance of PINN-based computational solid mechanics. The programs are built via Python coding language and TensorFlow library with step-by-step explanations. It is worth highlighting that PINN-based computational mechanics is easy to implement and can be extended for more challenging applications. This work aims to help the researchers who are interested in the PINN-based solid mechanics solver to have a clear insight into this emerging area. The programs for all the numerical examples presented in this work are available on https://github.com/JinshuaiBai/PINN_Comp_Mech.
GMRT observations of the radio source 4C 35.06: precessing jets from a cD galaxy under assembly?

We report GMRT observation of the strong radio source 4C 35.06, an extended (z=0.047) radio-loud AGN at the center of galaxy cluster Abell 407. The radio map at 610 MHz reveal a striking, helically twisted jet system emanating from an optically faint AGN host. The radio morphology closely resembles the precessing jets of the galactic microquasar SS 433. The optical SDSS images of central region show a complex ensemble of nine galactic condensations within 1 arc minute, embedded in a faint, diffuse stellar halo. This system presents a unique case for studying the formation of a giant elliptical galaxy (cD) at the cluster center.
Quantum Fourier Transform Has Small Entanglement

The Quantum Fourier Transform (QFT) is a key component of many important quantum algorithms, most famously as being the essential ingredient in Shor's algorithm for factoring products of primes. Given its remarkable capability, one would think it can introduce large entanglement to qubit systems and would be difficult to simulate classically. While early results showed QFT indeed has maximal operator entanglement, we show that this is entirely due to the bit reversal in the QFT. The core part of the QFT has Schmidt coefficients decaying exponentially quickly, and thus it can only generate a constant amount of entanglement regardless of the number of qubits. In addition, we show the entangling power of the QFT is the same as the time evolution of a Hamiltonian with exponentially decaying interactions, and thus a variant of the area law for dynamics can be used to understand the low entanglement intuitively. Using the low entanglement property of the QFT, we show that classical simulations of the QFT on a matrix product state with low bond dimension only take time linear in the number of qubits, providing a potential speedup over the classical fast Fourier transform (FFT) on many classes of functions. We demonstrate this speedup in test calculations on some simple functions. For data vectors of length $10^6$ to $10^8$, the speedup can be a few orders of magnitude.
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection

Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.
Efficiency of Thin and Thick Markets

In this paper, we propose a matching model to study the efficiency of thin and thick markets. Our model shows that the probabilities of matches in a thin market are significantly lower than those in a thick market. When applying our results to a job search model, it implies that, if the ratio of job candidates to job openings remains (roughly) a constant, the probability that a person can find a job is higher in a thick market than in a thin market. We apply our matching model to the U.S. academic market for new PhD economists. Consistent with the prediction of our model, a field of specialization with more job openings and more candidates has a higher probability of matching.
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance of full fine-tuning. In this way, SSF also surprisingly outperforms other parameter-efficient fine-tuning approaches even with a smaller number of tunable parameters. Furthermore, different from some existing parameter-efficient fine-tuning methods (e.g., Adapter or VPT) that introduce the extra parameters and computational cost in the training and inference stages, SSF only adds learnable parameters during the training stage, and these additional parameters can be merged into the original pre-trained model weights via re-parameterization in the inference phase. With the proposed SSF, our model obtains 2.46% (90.72% vs. 88.54%) and 11.48% (73.10% vs. 65.57%) performance improvement on FGVC and VTAB-1k in terms of Top-1 accuracy compared to the full fine-tuning but only fine-tuning about 0.3M parameters. We also conduct amounts of experiments in various model families (CNNs, Transformers, and MLPs) and datasets. Results on 26 image classification datasets in total and 3 robustness&out-of-distribution datasets show the effectiveness of SSF. Code is available at https://github.com/dongzelian/SSF.
A High Fidelity Simulation Framework for Potential Safety Benefits Estimation of Cooperative Pedestrian Perception

This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies. This simulator can support cooperative perception algorithms in the loop by simulating the environmental conditions, traffic conditions, and pedestrian characteristics at the same time. Besides, the benefit estimation model applied in our framework can systematically quantify both the risk conflict (non-crash condition) and the severity of the pedestrian's injuries (crash condition). An experiment was conducted in this paper that built a digital twin of a crowded urban intersection in China. The result shows that our framework is efficient for safety benefit estimation of V2I pedestrian safety strategies.
Universal scaling dependence of QCD energy loss from data-driven studies

In this paper we study the energy loss of jets in the Quark Gluon Plasma via the nuclear modification factor R-AA for unidentified particles at high p(T) (greater than or similar to 10 GeV/c) in and out of the reaction plane of the collision. We argue that at such a high p(T) there are no genuine flow effects and, assuming that the energy loss is only sensitive to initial characteristics such as the density and geometry, find that R-AA depends linearly on the (rms) length extracted from Glauber simulations. Furthermore, we observe that for different centrality classes the density dependence of the energy loss enters as the square root of the charged-particle multiplicity normalized to the initial overlap area. The energy loss extracted for BNL Relativistic Heavy Ion Collider and CERN Large Hadron Collider data from the R-AA is found to exhibit a universal behavior.
Break The Spell Of Total Correlation In β −TCVAE

—This paper proposes a way to break the spell of total correlation in β −TCVAE based on the motivation of the total correlation decomposition. An iterative decomposition path of total correlation is proposed, and an explanation for representation learning ability of VAE from the perspective of model capacity allocation. Newly developed objective function combines latent variable dimensions into joint distribution while relieving independent distribution constraint of the marginal distribution in combination, leading to latent variables with a more manipulable prior distribution. The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly. Experimental results on various datasets show an interesting relevance between model capacity and the latent variable grouping size, called the "V"-shaped best ELBO trajectory. Additional experiments demonstrate that the proposed method obtains better disentanglement performance with reasonable parameter capacity allocation. Finally, we design experiments to show the limitations of estimating total correlation with mutual information, identifying its source of estimation deviation.
Entropy squeezing and atomic inversion in the k-photon Jaynes—Cummings model in the presence of the Stark shift and a Kerr medium: A full nonlinear approach

The interaction between a two-level atom and a single-mode field in the k-photon Jaynes—Cummings model (JCM) in the presence of the Stark shift and a Kerr medium is studied. All terms in the Hamiltonian, such as the single-mode field, its interaction with the atom, the contribution of the Stark shift and the Kerr medium effects are considered to be f-deformed. In particular, the effect of the initial state of the radiation field on the dynamical evolution of some physical properties such as atomic inversion and entropy squeezing are investigated by considering different initial field states (coherent, squeezed and thermal states).
Generalised Mixin-based Inheritance to Support Multiple Inheritance

The semantics of multiple inheritance is still very much a subject of debate. Before a clearer view on the issue is obtained, multiple inheritance will not enjoy wide-spread use. As stated by Alan Snyder in [Shana it is not clear how to design one that supports new examples". We argue that it is possible to construct a simple, comprehensible, and general multiple inheritance mechanism. This is achieved by fragmenting the functionality of multiple inheritance. For this purpose a generalised form of mixin-based inheritance is introduced. We show that these generalised mixins are exactly the right building blocks, and provide exactly the right balance between exposure and encapsulation of inheritance to express multiple inheritance hierarchies.
User-aware Explications of Ontology Consequences: Levelling Technicality

Explaining consequences obtained from ontological reasoning is an active research topic. Unlike the computation of the cause of the consequence, the explication of the cause has received little attention so far. However, as many ontologies are designed by experts, the terms and notions used in an explanation need not be known to the user of the ontology-based system, before they can attempt to understand the underlying logical process. In this paper, we address the task of making an explanation of a consequence more comprehensible to a variety of users by re-phrasing it in a vocabulary known to them. Assuming the existence of a dictionary, we attempt to rewrite technical jargon into expressions using a simpler vocabulary. We show that solving this problem requires taking several technical issues into account.
Attempt to Generate Narrow Linewidth, CW Terahertz Radiation by Using Optical Frequency Comb

Abstract : We report on an attempt to generate highly stable continuous terahertz (THz) wave by using optical frequency comb (OFC). About 10-nm wide OFC has been generated through a deep phase modulation of a 852 nm laser line in lithium niobate crystal cavity. The multiple optical modes (side bands) of the OFC, which are equally separated from each other by the modulation frequency (=6 GHz) are taken as the frequency reference. When another semiconductor laser is frequency locked, the stability of the difference frequency between the master laser and the second laser is improved on the same order of the RF modulator. An ultra-narrow, line and tunable THz radiation source can be achieved by photomixing of this stable difference-frequency optical beat in a photoconductive antenna.
Combining Knowledge Graphs and Language Models to Answer Questions over Tables

Tables remain a primary modality for organizing and presenting information to people. We interact every day with Excel sheets, CSV files, tables in PDF documents, and web tables. Providing a natural language interface to query table information is paramount for several use cases. This demo shows a solution to query semantically described tables using natural-language questions. Our solution employs knowledge graphs as a medium to integrate tables coming from heterogeneous sources. Then, a transformer-based language model analyzes a user’s question and finds the answer in the semantically represented tables. During the demo session, we will show a use case developed in collaboration with DATEV eG, where tax consultants can efficiently query information from financial tables. Attendees will experience how a natural-language interface speeds up the information retrieval process from tables. They will also be allowed to ask their questions to a prepared dataset, showing the scalability of our solution. The video demo is available at https://owncloud.fraunhofer. de/index.php/s/uXFmUfzCta70rqN. data-dependent.
rewordQALD9: A Bilingual Benchmark with Alternative Rewordings of QALD Questions

In this paper we describe an extended version of the QALD dataset, a well-known benchmark resource used for the task of Question Answering over knowledge graphs (KGQA). Along the lines of similar projects, the purpose of this work is to make available a) high-quality data even for languages other than English, and b) multiple reformulations of the same question, to test systems’ robustness. The QALD version we used is the one released for the 9th edition of the challenge of Question Answering over Linked Data, and the languages involved are English and Italian. Besides a revised and improved quality of Italian translations of questions, the resource presented here features a number of alternative rewordings of both English and Italian questions. The usability of the resource has been tested on the QAnswer multilingual Question Answering system and through the GERBIL platform. The resource has been publicly released for research purposes.
Searching for galactic white-dwarf binaries in mock LISA data using an -statistic template bank

We describe an -statistic search for continuous gravitational waves from galactic white-dwarf binaries in simulated LISA data. Our search method employs a hierarchical template-grid-based exploration of the parameter space. In the first stage, candidate sources are identified in searches using different simulated laser signal combinations (known as TDI variables). Since each source generates a primary maximum near its true ‘Doppler parameters’ (intrinsic frequency and sky position) as well as numerous secondary maxima of the -statistic in Doppler parameter space, a search for multiple sources needs to distinguish between true signals and secondary maxima associated with other ‘louder’ signals. Our method does this by applying a coincidence test to reject candidates which are not found at nearby parameter space positions in searches using each of the three TDI variables. For signals surviving the coincidence test, we perform a fully coherent search over a refined parameter grid to provide an accurate parameter estimation for the final candidates. Suitably tuned, the pipeline is able to extract 1989 true signals with only 5 false alarms. The use of the rigid adiabatic approximation allows recovery of signal parameters with errors comparable to statistical expectations, although there is still some systematic excess with respect to statistical errors expected from Gaussian noise. An experimental iterative pipeline with seven rounds of signal subtraction and reanalysis of the residuals allows us to increase the number of signals recovered to a total of 3419 with 29 false alarms.
Utilizing Interaction Metrics in a Virtual Learning Environment for Early Prediction of Students' Academic Performance

A plethora of data on students’ activities and interactions is acquired through the use of virtual learning environments. The analysis of gathered data and the prediction of withdrawal and academic success has been an ongoing point of discussion in the field of Learning Analytics. The main aim of this paper is an early prediction of students‘ academic performance in terms of the final grade, while also targeting the early identification of students at risk of failing the course. We introduce new handcrafted features encompassing interaction patterns of students’ virtual learning environment usage, and highlight their predictive powers in monthly predictions. The experiment showed that a combination of the proposed features improved the Root Mean Square Error by up to 7.2 grade points compared to a model without the proposed features. Improvement of up to 0.52 was also observed in the coefficient of determination. Proposed model’s accuracy of predicting students at risk of failure reached 74% at the end of April.