Miracle-dz/UniFAR-FTUs huggingface dataset:
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA

Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method constructs the large-scale reasoning corpus by synthesizing pseudo heterogeneous reasoning paths from Wikipedia and generating corresponding questions. We evaluate our system on OTT-QA, a large-scale table-and-text open-domain question answering benchmark, and our system achieves the state-of-the-art performance. Further analyses illustrate that the explicit hybrid chain offers substantial performance improvement and interpretablity of the intermediate reasoning process, and the chain-centric pre-training boosts the performance on the chain extraction.
Individualized treatment rules under stochastic treatment cost constraints

Abstract Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.
Single-pion contribution to the Gerasimov-Drell-Hearn sum rule and related integrals

Phenomenological amplitudes obtained in partial-wave analyses (PWA) of single-pion photoproduction are used to evaluate the contribution of this process to the Gerasimov–Drell–Hearn (GDH), Baldin and Gell-Mann–Goldberger–Thirring (GGT) sum rules, by integrating up to 2 GeV in photon energy. Our study confirms that the single-pion contribution to all these sum rules converges even before the highest considered photon energy, but the levels of saturation are very different in the three cases. Single-pion production almost saturates the GDH sum rule for the proton, while a large fraction is missing in the neutron case. The Baldin integrals for the proton and the neutron are both saturated to about four fifths of the predicted total strength. For the GGT sum rule, the wide variability in predictions precludes any definitive statement.
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps. A key problem in the inference is to estimate the variance in each timestep of the reverse process. In this work, we present a surprising result that both the optimal reverse variance and the corresponding optimal KL divergence of a DPM have analytic forms w.r. t. its score function. Building upon it, we propose Analytic-DPM, a training-free inference framework that estimates the analytic forms of the variance and KL divergence using the Monte Carlo method and a pretrained score-based model. Further, to correct the potential bias caused by the score-based model, we derive both lower and upper bounds of the optimal variance and clip the estimate for a better result. Empirically, our analytic-DPM improves the log-likelihood of various DPMs, produces high-quality samples, and meanwhile enjoys a 20x to 80x speed up.
On the Linear Convergence of Extragradient Methods for Nonconvex-Nonconcave Minimax Problems

Recently, minimax optimization has received renewed focus due to modern applications in machine learning, robust optimization, and reinforcement learning. The scale of these applications naturally leads to the use of first-order methods. However, the nonconvexities and nonconcavities present in these problems, prevents the application of typical gradient descent/ascent, which is known to diverge even in bilinear problems. Recently, it was shown that the proximal point method (PPM) converges linearly for a family of nonconvex–nonconcave problems. In this paper, we study the convergence of a damped version of the extragradient method (EGM), which avoids potentially costly proximal computations, relying only on gradient evaluation. We show that the EGM converges linearly for smooth minimax optimization problems satisfying the same nonconvex–nonconcave condition needed by the PPM. Funding: H. Lu was supported by The University of Chicago Booth School of Business Benjamin Grimmer was supported by Johns Hopkins Applied Mathematics and Statistics Department.
Understanding Political Polarization on Social Platforms by Jointly Modeling Users, Connections and Multi-modal Post Contents in Heterogeneous Graphs

In this study, we investigate the political polarization in Twitter discussions about inflation during the COVID-19 pandemic using a dataset composed of more than 20,000 vetted tweets posted by over 8,000 unique Twitter users from July to November in 2021. Our analysis shows the timing of the volume changes in online discussions roughly corresponds to the dates when the U.S. Bureau of Labor Statistics released the Consumer Price Index (CPI). The usage of the hashtags varies across left- and right-leaning users. Left-leaning users tend to discuss more diverse subtopics while right-leaning users focus on blaming U.S. President Joe Biden for the inflation. Unlike previous studies, we characterize political polarization by jointly modeling user information, their connections, and the multi-modal post contents in a heterogeneous graph. By mapping the node embeddings into a two-dimensional space, we find there is clear segregation between left- and right-leaning users. Although using only one of the features or the concatenation does not improve the performance of modeling, we find notable differences in user descriptions, topics, images, and levels of retweet activities. Our framework shows wide applicability and can be further extended by integrating other modalities of data and more relations. The findings have implications for understanding online political polarization and designing mitigation policies for potentially negative outcome of extreme polarization.
Domain Adaptation via Bidirectional Cross-Attention Transformer

Domain Adaptation (DA) aims to leverage the knowledge learned from a source domain with ample labeled data to a target domain with unlabeled data only. Most existing studies on DA contribute to learning domain-invariant feature representations for both domains by minimizing the domain gap based on convolution-based neural networks. Recently, vision transformers significantly improved performance in multiple vision tasks. Built on vision transformers, in this paper we propose a Bidirectional Cross-Attention Transformer (BCAT) for DA with the aim to improve the performance. In the proposed BCAT, the attention mechanism can extract implicit source and target mixup feature representations to narrow the domain discrepancy. Specifically, in BCAT, we design a weight-sharing quadruple-branch transformer with a bidirectional cross-attention mechanism to learn domain-invariant feature representations. Extensive experiments demonstrate that the proposed BCAT model achieves superior performance on four benchmark datasets over existing state-of-the-art DA methods that are based on convolutions or transformers.
Asymmetric Star Formation Triggered by Gas Inflow in a Barred Lenticular Galaxy PGC 34107

Comparing the inactive and gas-poor normal lenticular galaxies (S0s) in the local universe, we study a barred star-forming S0 galaxy, PGC 34107, which has been observed by the Centro Astronómico Hispano Alemán 3.5 m telescope and the Northern Extended Millimeter Array. The spatially resolved ionized gas and molecular gas traced by 12CO(1–0), hereafter CO(1–0), show similar distribution and kinematics to the stellar component with an off-center star-forming region, ∼380 pc away from the center. The resolved kinematics of molecular CO(1–0) emission reveals that there is a blueshifted (redshifted) velocity component on the receding (approaching) side of the galaxy along the stellar bar. This might provide plausible evidence of noncircular motion, such as the bar-induced molecular gas inflow. The velocity of the molecular gas inflow decreases when approaching toward the peak of the off-center star formation in the north, which might be associated with the inner Lindblad resonance. In addition to CO(1–0), we also detect the isotopic line of 13CO(1–0). For most Hα, CO(1–0) and 13CO(1–0) emissions are concentrated on this northern star-forming region. We find that PGC 34107 follows the local stellar mass–metallicity relation, star-forming main sequence, and the Kennicutt–Schmidt law. The resolved and integrated molecular gas main sequence suggests that there is a higher gas fraction in the central region of the galaxy, which supports a scenario that the bar-induced gas reservoir provides the raw material, and subsequently triggers the central star formation.
Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

The subreddit r/The_Donald was repeatedly denounced as a toxic and misbehaving online community, reasons for which it faced a sequence of moderation interventions by Reddit administrators. It was quarantined in June 2019, restricted in February 2020, and finally banned in June 2020, but despite precursory work on the matter, the effects of this sequence of interventions are still unclear. In this work, we follow a multidimensional causal inference approach, with data containing more than 15M posts made in a time frame of 2 years, to examine the effects of such interventions inside and outside of the subreddit. We find that the interventions greatly reduced the activity of problematic users. However, the interventions also caused an increase in toxicity and led users to share more polarized and less factual news. In addition, the restriction had stronger effects than the quarantine, and core users of r/The_Donald suffered stronger effects than the rest of users. Overall, our results provide evidence that the interventions had mixed effects and paint a nuanced picture of the consequences of community-level moderation strategies. We conclude by reflecting on the challenges of policing online platforms and on the implications for the design and deployment of moderation interventions.
StemP: A fast and deterministic Stem-graph approach for RNA and protein folding prediction

We propose a new deterministic methodology to predict RNA sequence and protein folding. Is stem enough for structure prediction? The main idea is to consider all possible stem formation in the given sequence. With the stem loop energy and the strength of stem, we explore how to deterministically utilize stem information for RNA sequence and protein folding structure prediction. We use graph notation, where all possible stems are represented as vertices, and co-existence as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for folding structure prediction. We introduce a Stem-Loop score to add structure information and to speed up the computation. The proposed method can handle secondary structure prediction as well as protein folding with pseudo knots. Numerical experiments are done using a laptop and results take only a few minutes or seconds. One of the strengths of this approach is in the simplicity and flexibility of the algorithm, and it gives deterministic answer. We explore protein sequences from Protein Data Bank, rRNA 5S sequences, and tRNA sequences from the Gutell Lab. Various experiments and comparisons are included to validate the propose method.
An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets

Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.
Specifying and Reasoning about CPS through the Lens of the NIST CPS Framework

This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.
HARQ Optimization for Real-Time Remote Estimation in Wireless Networked Control

This paper analyzes wireless network control for remote estimation of linear time-invariant dynamical systems under various Hybrid Automatic Repeat Request (HARQ) packet retransmission schemes. In conventional HARQ, packet reliability increases gradually with additional packets; however, each retransmission maximally increases the Age of Information and causes severe degradation in estimation mean squared error (MSE) performance. We optimize standard HARQ schemes by allowing partial retransmissions to increase the packet reliability gradually and limit the AoI growth. In incremental redundancy HARQ, we optimize the retransmission time to enable the early arrival of the next status updates. In Chase combining HARQ, since packet length remains fixed, we allow retransmission and new updates in a single time slot using non-orthogonal signaling. Non-orthogonal retransmissions increase packet reliability without delaying the fresh updates. We formulate bi-objective optimization with the proposed variance of the MSE-based cost function and standard long-term average MSE cost function to guarantee short-term performance stability. Using the Markov decision process formulation, we find the optimal static and dynamic policies under the proposed HARQ schemes to improve MSE performance further. The simulation results show that the proposed HARQ-based policies are more robust and achieve significantly better and more stable MSE performance than standard HARQ-based policies.
Graph Transfer Learning via Adversarial Domain Adaptation With Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.
Rigidity of discrete conformal structures on surfaces

In \cite{G3}, Glickenstein introduced the discrete conformal structures on polyhedral surfaces in an axiomatic approach from Riemannian geometry perspective. Glickenstein's discrete conformal structures include Thurston's circle packings, Bowers-Stephenson's inversive distance circle packings and Luo's vertex scalings as special cases. Glickenstein \cite{G5} further conjectured the rigidity of the discrete conformal structures on polyhedral surfaces. Glickenstein's conjecture includes Luo's conjecture on the rigidity of vertex scalings \cite{L1} and Bowers-Stephenson's conjecture on the rigidity of inversive distance circle packings \cite{BSt} as special cases. In this paper, we prove Glickenstein's conjecture using variational principles. This unifies and generalizes the well-known results of Luo \cite{L4} and Bobenko-Pinkall-Springborn \cite{BPS}. Our method provides a unified approach to similar problems. We further discuss the relationships of Glickenstein's discrete conformal structures on polyhedral surfaces and $3$-dimensional hyperbolic geometry. As a result, we obtain some new results on the convexities of the co-volume functions of some generalized $3$-dimensional hyperbolic tetrahedra.
Time and Punishment: How Individuals Respond to Being Sanctioned in Voluntary Associations

Little consensus exists on how group members respond when sanctioned by their peers. One school of thought suggests individuals become more cooperative, or prosocial, toward the group. Another line of research shows that individuals have negative emotional reactions to being punished and become less cooperative. We reconcile these seemingly conflicting viewpoints in the context of voluntary associations, where cooperation is crucial. Building on social psychological theories of dependence, we propose that responses to sanctions shift over time as individuals become increasingly dependent on other members to achieve valued outcomes. We draw on data from Colombian microsavings groups to develop this proposition, using qualitative data to flesh out the proposed mechanism and longitudinal, quantitative records to test our hypothesis. We find that individuals initially respond to sanctions by reducing prosocial contributions, but their responses become increasingly prosocial over time. This study generates a fresh vision of sanctions as temporally and relationally dependent.
Gendered Dignity at Work

Scholars have long noted paradoxical results surrounding women’s higher-than-expected job appraisals, particularly in the face of persistent pay gaps, segregation, discrimination, and glass ceilings. Part of the problem is that traditional appraisal indicators (e.g., job satisfaction) typically reflect an amalgam of intrinsic and extrinsic evaluations and omit from consideration power-laden, gendered workplace interactions. In this article, we focus on and suggest an alternative conception—dignity at work and its central elements, respect and recognition—to more convincingly capture women’s job-specific experiences and associated inequalities. Our analyses, drawing on nearly 6,000 full-time workers from the General Social Survey (2002–18), clearly demonstrate that women experience less dignity at work than do men—dignity that is notably undercut by firsthand encounters with workplace gender discrimination and sexual harassment. We conclude by underscoring the importance of women’s workplace dignity and the need for ample recognition of the unjust gendered encounters many women continue to experience.
A Review of Results from Clinical Trials Based on Co-Variants of the R-CHOP Regimen in Untreated Patients with Diffuse Large B-cell Lymphoma

Summary Since 2000, new drugs for treating lymphoproliferative diseases have been introduced in haematology, and their initial indications have been gradually expanded. Some of the so-called „target molecules“ have shown good efficacy when treating some malignant non-Hodgkin lymphomas. The possibility of achieving therapeutic disease control in some indolent non-Hodgkin lymphomas using drugs with a different mechanism of action from that of cytostatics has logically raised the question of a change in therapeutic management in other lymphoproliferative diseases. Since 2010, clinical trials have been initiated with untreated patients with aggressive non-Hodgkin‘s lymphoma - diffuse large B-cell lymphoma as the primary target. The key aim of most clinical trials has been to compare the efficacy of the so-called „gold standard“ - rituximab, cyclophosphamide, doxorubicin, oncovin, prednisolone (R- CHOP) versus its derivatives. The review discusses the results of completed clinical trials published on the Internet. These trials covered covariants of the R-CHOP regimen used as first-line treatment of patients with diffuse large B-cell lymphoma. The review also includes drugs registered in Bulgaria for the last ten years but with a different clinical indication at present. The results of five clinical trials in which obinotuzumab, venetoclax, lenalidomide, bortezomib, and ibrutinib were used are presented.
Synthesis and electroluminescence properties of tris-[5-choloro-8-hydroxyquinoline] aluminum Al(5-Clq)3

A new electroluminescent material tris-[5-choloro-8-hydroxyquinoline] aluminum has been synthesized and characterized. Solution of this material Al(5-Clq)3 in toluene showed absorption maxima at 385 nm which was attributed to the moderate energy (π–π*) transitions of the aromatic rings. The photoluminescence spectrum of Al(5-Clq)3 in toluene solution showed a peak at 522 nm. This material shows thermal stability up to 400 °C. The structure of the device is ITO/0.4 wt%F4-TCNQ doped α-NPD (35 nm) / Al(5-Clq)3 (30 nm) / BCP (6 nm) / Alq3 (30 nm) / LiF (1 nm) / Al (150 nm). This device exhibited a luminescence peak at 585 nm (CIE coordinates, x = 0.39, y = 0.50). The maximum luminescence of the device was 920 Cd/m2 at 25 V. The maximum current efficiency of OLED was 0.27 Cd/A at 20 V and maximum power efficiency was 0.04 lm/W at 18 V.
The Impact of Aging on the Lung Alveolar Environment, Predetermining Susceptibility to Respiratory Infections

Respiratory infections are one of the top causes of death in the elderly population, displaying susceptibility factors with increasing age that are potentially amenable to interventions. We posit that with increasing age there are predictable tissue-specific changes that prevent the immune system from working effectively in the lung. This mini-review highlights recent evidence for altered local tissue environment factors as we age focusing on increased tissue oxidative stress with associated immune cell changes, likely driven by the byproducts of age-associated inflammatory disease. Potential intervention points are presented.