Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex analysis of Askaryan radiation: A fully analytic model in the time domain

The detection of ultra-high energy (UHE, ≥ 10 PeV) neutrinos via detectors designed to utilize the Askaryan eﬀect has been a long-time goal of the astroparticle physics community. The Askaryan eﬀect describes radio-frequency (RF) radiation from high-energy cascades. When a UHE neutrino initiates a cascade, cascade properties are imprinted on the radiation. Thus, observed radiation properties must be used to reconstruct the UHE neutrino event. Analytic Askaryan models have three advantages when used for UHE neutrino reconstruction. First, cascade properties may be derived from the match between analytic function and observed data. Second, analytic models minimize computational intensity in simulation packages. Third, analytic models can be embedded in ﬁrmware to enhance the real-time sensitivity of detectors. We present a fully analytic Askaryan model in the time-domain for UHE neutrino-induced cascades in dense media that builds upon prior models in the genre. We then show that our model matches semi-analytic parameterizations used in Monte Carlo simulations for the design of IceCube-Gen2. We ﬁnd correlation coeﬃcients greater than 0.95 and fractional power diﬀerences < 5% between the the fully analytic and semi-analytic approaches.
Database reasoning over text

Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context.
belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification

Natural language processing (NLP) is becoming an important means for automatic recognition of human traits and states, such as intoxication, presence of psychiatric disorders, presence of airway disorders and states of stress. Such applications have the potential to be an important pillar for online help lines, and may gradually be introduced into eHealth modules. However, NLP is language specific and for languages such as Dutch, NLP models are scarce. As a result, recent Dutch NLP models have a low capture of long range semantic dependencies over sentences. To overcome this, here we present belabBERT, a new Dutch language model extending the RoBERTa architecture. belabBERT is trained on a large Dutch corpus (+32 GB) of web crawled texts. We applied belabBERT to the classification of psychiatric illnesses. First, we evaluated the strength of text-based classification using belabBERT, and compared the results to the existing RobBERT model. Then, we compared the performance of belabBERT to audio classification for psychiatric disorders. Finally, a brief exploration was performed, extending the framework to a hybrid text- and audio-based classification. Our results show that belabBERT outperformed the current best text classification network for Dutch, RobBERT. belabBERT also outperformed classification based on audio alone.
RevCore: Review-Augmented Conversational Recommendation

Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating external information (e.g., reviews) is a potential solution to alleviate this problem. Given that reviews often provide a rich and detailed user experience on different interests, they are potential ideal resources for providing high-quality recommendations within an informative conversation. In this paper, we design a novel end-to-end framework, namely, Review-augmented Conversational Recommender (RevCore), where reviews are seamlessly incorporated to enrich item information and assist in generating both coherent and informative responses. In detail, we extract sentiment-consistent reviews, perform review-enriched and entity-based recommendations for item suggestions, as well as use a review-attentive encoder-decoder for response generation. Experimental results demonstrate the superiority of our approach in yielding better performance on both recommendation and conversation responding.
Cross-document Coreference Resolution over Predicted Mentions

Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting. Our model achieves competitive results for event and entity coreference resolution on gold mentions. More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions. Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources.
Intermittent Private Information Retrieval With Application to Location Privacy

We study the problem of intermittent private information retrieval with multiple servers, in which a user consecutively requests one of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages from <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases such that part of requests need to be protected while others do not need privacy. Motivated by the location privacy application, the correlation between requests is modeled by a Markov chain. We propose an intermittent private information retrieval scheme that concatenates an obfuscation scheme and a private information retrieval scheme for the time period when privacy is not needed, to prevent leakage incurred by the correlation over time. In the end, we illustrate how the proposed scheme for the problem of intermittent private information retrieval with Markov structure correlation can be applied to design a location privacy protection mechanism in the location privacy problem.
Generating Informative Conclusions for Argumentative Texts

The purpose of an argumentative text is to support a certain conclusion. Yet, they are often omitted, expecting readers to infer them rather. While appropriate when reading an individual text, this rhetorical device limits accessibility when browsing many texts (e.g., on a search engine or on social media). In these scenarios, an explicit conclusion makes for a good candidate summary of an argumentative text. This is especially true if the conclusion is informative, emphasizing specific concepts from the text. With this paper we introduce the task of generating informative conclusions: First, Webis-ConcluGen-21 is compiled, a large-scale corpus of 136,996 samples of argumentative texts and their conclusions. Second, two paradigms for conclusion generation are investigated; one extractive, the other abstractive in nature. The latter exploits argumentative knowledge that augment the data via control codes and finetuning the BART model on several subsets of the corpus. Third, insights are provided into the suitability of our corpus for the task, the differences between the two generation paradigms, the trade-off between informativeness and conciseness, and the impact of encoding argumentative knowledge. The corpus, code, and the trained models are publicly available.
Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approach

In this paper, we propose SACHER (soft actor-critic (SAC) with hindsight experience replay (HER)), which constitutes a class of deep reinforcement learning (DRL) algorithms. SAC is known as an off-policy model-free DRL algorithm based on the maximum entropy framework, which outperforms earlier DRL algorithms in terms of exploration, robustness and learning performance. However, in SAC, maximizing the entropy-augmented objective may degrade the optimality of learning outcomes. HER is known as a sample-efficient replay method that enhances the performance of off-policy DRL algorithms by allowing the agent to learn from both failures and successes. We apply HER to SAC and propose SACHER to improve the learning performance of SAC. More precisely, SACHER achieves the desired optimal outcomes faster and more accurately than SAC, since HER improves the sample efficiency of SAC. We apply SACHER to the navigation and control problem of unmanned aerial vehicles (UAVs), where SACHER generates the optimal navigation path of the UAV under various obstacles in operation. Specifically, we show the effectiveness of SACHER in terms of the tracking error and cumulative reward in UAV operation by comparing them with those of state-of-the-art DRL algorithms, SAC and DDPG. Note that SACHER in UAV navigation and control problems can be applied to arbitrary models of UAVs.
The homotopy types of free racks and quandles

We initiate the homotopical study of racks and quandles, two algebraic structures that govern knot theory and related braided structures in algebra and geometry. We prove analogs of Milnor’s theorem on free groups for these theories and their pointed variants, identifying the homotopy types of the free racks and free quandles on spaces of generators. These results allow us to complete the stable classification of racks and quandles by identifying the ring spectra that model their stable homotopy theories. As an application, we show that the stable homotopy of a knot quandle is, in general, more complicated than what any Wirtinger presentation coming from a diagram predicts.
Solving Arithmetic Word Problems with Transformers and Preprocessing of Problem Text

This paper outlines the use of Transformer networks trained to translate math word problems to equivalent arithmetic expressions in infix, prefix, and postfix notations. We compare results produced by many neural configurations and find that most configurations outperform previously reported approaches on three of four datasets with significant increases in accuracy of over 20 percentage points. The best neural approaches boost accuracy by 30% when compared to the previous state-of-the-art on some datasets.
FedHybrid: A Hybrid Federated Optimization Method for Heterogeneous Clients

We consider a distributed consensus optimization problem over a server-client (federated) network, where all clients are connected to a central server. Current distributed algorithms fail to capture the heterogeneity in clients' local computation capacities. Motivated by the method of multipliers in centralized optimization, we derive a Newton-type primal-dual method with a distributed implementation utilizing the server-client topology. Then we propose FedHybrid as a hybrid primal-dual method that allows heterogeneous clients to perform different types of updates. Specifically, those clients with higher computational capabilities and/or cheaper costs to perform computation can implement Newton-type updates locally, while other clients can adopt much simpler gradient-type updates. Theoretically, we propose a novel merit function by combining the dual optimality gap and the primal tracking error. We prove that FedHybrid converges linearly to the exact optimal point for strongly convex functions, regardless of clients' choices of gradient-type or Newton-type updates. Finally, we show numerical studies to demonstrate the efficacy of our method in practice. To the best of our knowledge, this is the first hybrid method allowing heterogeneous local updates for distributed consensus optimization with provable convergence and rate guarantees.
KO-PDE: Kernel Optimized Discovery of Partial Differential Equations with Varying Coefficients

Partial differential equations (PDEs) ﬁtting scientiﬁc data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects. Most natural dynamics are expressed by PDEs with varying coefﬁcients (PDEs-VC), which highlights the importance of PDE discovery. Previous algorithms can discover some simple instances of PDEs-VC but fail in the discovery of PDEs with coefﬁcients of higher complexity, as a result of coefﬁcient estimation inaccuracy. In this paper, we propose KO-PDE, a kernel optimized regression method that incorporates the kernel density estimation of adjacent coefﬁcients to reduce the coefﬁcient estimation error. KO-PDE can discover PDEs-VC on which previous baselines fail and is more robust against inevitable noise in data. In experiments, the PDEs-VC of seven challenging spatiotemporal scientiﬁc datasets in ﬂuid dynamics are all discovered by KO-PDE, while the three baselines render false results in most cases. With state-of-the-art performance, KO-PDE sheds light on the automatic description of natural phenomenons using discovered PDEs in the real world.
Grasp Stability Prediction with Time Series Data Based on STFT and LSTM

With the increasing demand for robots, robotic grasping will play a increasingly important role in future applications. This paper takes grasp stability prediction as the key technology for grasping and attempts to solve the problem using time series data inputs, including the force and pressure data. By applying algorithms to predict unstable grasping using time series data in more fields, we can significantly promote the application of artificial intelligence in traditional industries. This research investigates models that combine Short-Time Fourier Transform (STFT) and Long Short-Term Memory (LSTM) and tests their generalizability using a dexterous hand and a suction cup gripper. The experiments suggest good results for grasp stability prediction using the force data and generalized results using pressure data. Among the four models tested, the (Data + STFT) & LSTM delivers the best performance. We plan to perform more work on grasp stability prediction, generalize the findings to different types of sensors, and apply the grasp stability prediction in more grasping use cases in real life.
End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification

We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of the network are optimized in an end-to-end fashion by minimizing the average normalized system distance of the adaptive filter. This avoids the need of explicit signal power spectral density estimation as required for model-based adaptation control and further auxiliary mechanisms to deal with model inaccuracies. The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by high-level, non-white and non-stationary additive noise signals, abrupt environment changes and additional model inaccuracies.
Trade Shocks and the Role of Stakeholder Preference in Corporate Social Responsibility

This paper investigates the role of stakeholder preference on corporate social responsibility (CSR). We find that Indian firms increase CSR expenses when trade restrictions (Antidumping) are initiated against competing Chinese exports from countries with a high stakeholder preference for CSR. However, when these shocks emanate from countries with a lower stakeholder preference, CSR expenses remain unchanged. Capital investments and R&amp;D of Indian firms increase due to the same shocks, irrespective of their country of origin. Finally, CSR increases firm value only when the demand shocks originate from countries with a higher CSR preference. Collectively, our results provide empirical evidence of investment motives for CSR.
Single-crystal X-ray diffraction study of Cs2Er[Si6O14]F and Cs2Er[Si4O10]F

Abstract Single-crystal growth experiments in the system CsF-Er2O3-SiO2 resulted in the simultaneous crystallization of two chemically related compounds within the same run: Cs2Er[Si6O14]F (phase I) and Cs2Er[Si4O10]F (phase II). They represent the first examples for cesium erbium silicates containing fluorine. Basic crystallographic data are – phase I: space group Cmca, a=17.2556(6) Å, b=24.6565(7) Å, c=14.4735(5) Å, V=6157.9(3) Å3, Z=16; phase II: space group Pnma, a=22.3748(7) Å, b=8.8390(2) Å, c=11.9710(4) Å, V=2367.5(1) Å3, Z=8. The structures were determined by direct methods and refined to residuals of R(|F|)=0.0229 for 2920 (phase I) and 0.0231 for 2314 (phase II) independent observed reflections with I>2σ(I). The structure of phase I represents a previously unknown structure type with a three dimensional tetrahedral framework consisting of Q3 and Q4 groups in the ratio 2:1. Basic building units of the network are unbranched sechser single-chains running parallel to [001]. The network can be conveniently built up from the condensation of tetrahedral layers parallel to (010) or (100), respectively. The crystal structure of phase II can be classified as a tubular or columnar chain silicate indicating that the backbones of the structure are multiple chains of silicate tetrahedra. This structure is isotypic to a Cs2Y[Si4O10]F, a compound that has been characterized previously. Alternatively, both compounds can be described as mixed octahedral-tetrahedral frameworks, which can be classified according to their polyhedral microensembles. A topological analysis of both nets is presented.
Voluntary vs. Mandatory Management Earnings Prediction and Company Survival

This study explores a natural experiment that involves the change in the regulation of earnings forecast disclosures from mandatory to voluntary in firms’ IPO prospectuses. Findings indicate a behavioural alteration with pessimistic earnings forecasts during the mandatory era turning optimistic in the voluntary period. Additionally, we document that IPO first-day returns are lower in the voluntary period, and that IPO firms with high forecast errors have lower underpricing. Finally, our results suggest that firms with high forecast errors have a shorter time to survive in subsequent periods following the offering. Self-selection estimation and other various tests confirm our results.
Internal Capital Reallocation as Operational Hedging∗

This paper documents dynamic operational hedging by non-financial corporations. We examine investments and internal capital reallocations of Korean business groups (chaebol) during the Asian financial crisis. Controlling for investment opportunities, chaebol reduced (increased) investment in member firms with high (low) cash-flow volatility during the post-crisis period, which was accompanied by active capital reallocations through equity investments from highto low-cash-flow-volatility member firms. The negative cash-flow volatility–investment association was stronger among chaebol with greater dependence on external financing. Consequently, chaebol reduced aggregate cash-flow volatility by 3.2%. Our results are consistent with internal capital reallocation being an effective means of risk management. JEL category: G30, G32
Distribution of Peripheral Blood Cells in Esophageal Cancer Patients During Concurrent Chemoradiotherapy Predicts Long-Term Locoregional Progression Hazard After Treatment (GASTO1072)

Purpose Predicting the response to chemoradiotherapy is critical for the optimal management of esophageal cancer; however, it remains an unmet clinical need. This study aimed to evaluate the predictive potential of peri-treatment peripheral blood cells (PBC) in disease progression hazard in esophageal cancer following chemoradiotherapy. Patients and Methods A total of 87 patients with primary esophageal squamous cell carcinoma were subjected to definitive concurrent chemoradiotherapy in a Phase II trial. PBC parameters (hemoglobin, neutrophils, platelets, lymphocytes, and monocytes) were collected at seven time points throughout the course of radiotherapy. The potential of peri-treatment PBC parameters to predict the 3-year cumulative hazard of tumor progression was evaluated. Results Patients with disease progression displayed distinct distribution patterns of peri-treatment PBC compared to that in patients without disease progression. Greater prediction capabilities for risk of locoregional disease progression were found in PBC collected after the start of radiotherapy compared to those in their pretreatment counterparts, and in individual parameters rather than cell-to-cell ratios. The most predictive PBC parameters were integrated by summation and designated as a PBC score (PBCS), which further augmented their predictive power. Patients classified according to their PBCS (high vs medium v. low) had significantly different 3-year cumulative hazards of locoregional progression (58% vs 29% vs 7%, P = 0.0017). Multivariate analysis confirmed that high PBCS (HR, 12.2; 95% CI, 2.0–76.3; P = 0.007) and medium (HR, 5.8; 95% CI 1.2–27.7; P = 0.028) were independent indicators of locoregional progression. Conclusion Systematic analysis of PBC distribution in esophageal cancer patients undergoing definitive chemoradiotherapy could help predict long-term locoregional progression hazard after treatment.