Miracle-dz/UniFAR-FTUs huggingface dataset:
Stick It to The Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks

A large body of research in network and social sciences studies the effects of interventions in network systems. Nearly all of this work assumes that network participants will respond to interventions in similar ways. However, in real-world systems, a subset of participants may respond in ways purposefully different than their true outcome. We characterize the inﬂuence of non-cooperative nodes and the bias these nodes introduce in estimates of average treatment effect (ATE). In addition to theoretical bounds, we empirically demonstrate estimation bias through experiments on synthetically generated graphs and a real-world network. We demonstrate that causal estimates in networks can be sensitive to the actions of non-cooperative members, and we identify network structures that are particularly vulnerable to non-cooperative responses. Our work demonstrates a vulnerability in cluster-randomized network A/B testing to manipulation under non-cooperative behavior, particularly for networks with long-tailed degree distributions. We have shown that networks with strong peer effects are susceptible to ATE bias from non-cooperative behavior and identiﬁed forest-ﬁre models and SBMs as network structures vulnerable to non-cooperative spillover effects. Our experiments using a real-world network show results consistent with our ﬁndings in synthetic networks.
CHANGE MANAGEMENT AT CAMBODIAN PUBLIC HIGHER EDUCATION INSTITUTIONS: APPRAISING CHANGE ACCEPTANCE FROM CULTURE, TRUST, PROCESS AND ELEMENTS

: This study aims to appraise teaching staff’s acceptance with organizational trust, culture, process and elements, followed by an attempt to construct a model to predict change acceptance. This study employed online survey questionnaires for data collection. Participants in this research are teaching staff experiencing the adoption of new technology change, Blended Learning, at two public higher education institutions in Cambodia. The study found that majority of teaching staff expressed their support towards the change program, yet not all of them had adopted such changes in their work. The analyses of these six-paired variables-change acceptance, trust, task and relationship cultures, change process, strategy and technology-confirm positive correlations. There is also sufficient evidence proving that change acceptance can be predicted from these early dimensions, and relationship culture is found to be a significant predictor.
Estimating Causal Effects on Networked Observational Data via Representation Learning

In this paper, we study the causal effects estimation problem on networked observational data. We theoretically prove that standard graph machine learning (ML) models, e.g., graph neural networks (GNNs), fail in estimating the causal effects on networks. We show that graph ML models exhibit two distribution mismatches of their objective functions compared to causal effects estimation, leading to the failure of traditional ML models. Motivated by this, we first formulate the networked causal effects estimation as a data-driven multi-task learning problem, and then propose a novel framework NetEst to conduct causal inference in the network setting. NetEst uses GNNs to learn representations for confounders, which are from both a unit's own characteristics and the network effects. The embeddings are then used to sufficiently bridge the distribution gaps via adversarial learning and estimate the observed outcomes simultaneously. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of NetEst. We also provide analyses on why and when NetEst works.
Cross-Border Mergers and Acquisitions: Does the Exchange Rate Matter? Some Evidence for Canada

Theoretical and empirical studies investigating the relationship between the exchange rate and FDI have generated mixed results. Using bilateral Canadian-U.S. industry level count data on cross-border mergers and acquisitions (M&As) and conditioning on industry tariff rates, value added share of industries, industry M&A trend activity, and the number of establishments, we find evidence that a real dollar depreciation of the home currency leads to an increase in the probability of foreign M&As but only in high R&D industries. These empirical results are consistent with Blonigen's asset acquisition hypothesis. Results on European M&As of Canadian firms also lean towards this result.
Exploring the star formation histories of galaxies

In this thesis, I explore the star formation histories of both spiral and elliptical galaxies. In Part 1,1 present an in-depth study of the star formation histories of spiral galaxies with a wide range of properties. Optical and near-infrared colours are used in conjunction with up-to-date stellar population synthesis models to constrain the ages and metallicities of my sample galaxies. I find that age and metallicity gradients are common in spiral galaxies of all types. The age of a spiral galaxy correlates mainly with its surface brightness, and its metallicity correlates strongly with both its surface brightness and absolute magnitude. Using simple models, I demonstrate that the correlations observed in this thesis show that the star formation history of a region within a galaxy depends primarily on its surface density, and possibly on the dynamical time. Metal- enriched outflow from low mass galaxies seems to be required to reproduce a reasonably strong metallicity-magnitude correlation. These variations in star formation history are a continuous function of the physical parameters: in particular, I find no evidence for a bimodal spiral galaxy surface brightness distribution. In Part 2, I present a short study on the formation epoch of early-type galaxies. I developed a photometric redshift estimator optimised for redshifts z ~ 1. The redshift estimator provides redshifts accurate to ~ 10 per cent. This redshift estimator is then applied to a sample of morphologically-selected early-type galaxies in the northern Hubble Deep Field. Comparison of their colour-magnitude relation with a passively evolved Coma cluster colour-magnitude relation indicates that over half of the sample must form at redshifts greater than two.
``Easy" Representations and the {\sc qsf} property for groups

We define the class of “easy” groups as the class of those groups Γ admitting an (inverse) representation (which is a map→ Γ satisfying several topological properties) for which the set of double points is closed. Our main result is that easy groups are QSF (i.e. quasi-simple filtrated).
Localization in random geometric graphs with too many edges

We consider a random geometric graph $G(\chi_n, r_n)$, given by connecting two vertices of a Poisson point process $\chi_n$ of intensity $n$ on the unit torus whenever their distance is smaller than the parameter $r_n$. The model is conditioned on the rare event that the number of edges observed, $|E|$, is greater than $(1 + \delta)\mathbb{E}(|E|)$, for some fixed $\delta >0$. This article proves that upon conditioning, with high probability there exists a ball of diameter $r_n$ which contains a clique of at least $\sqrt{2 \delta \mathbb{E}(|E|)}(1 - \varepsilon)$ vertices, for any given $\varepsilon >0$. Intuitively, this region contains all the "excess" edges the graph is forced to contain by the conditioning event, up to lower order corrections. As a consequence of this result, we prove a large deviations principle for the upper tail of the edge count of the random geometric graph. The rate function of this large deviation principle turns out to be non-convex.
Impact of COVID-19 pandemic on the incidence of suicidal behaviors: a retrospective analysis of integrated electronic health records in a 7.5-million population

The COVID-19 pandemic has caused a remarkable psychological overwhelming and increase of stressors that may trigger suicidal behaviors. However, its impact on the rate of suicidal behaviors has been poorly reported. We conducted a population-based retrospective analysis of all suicidal behaviors attended in healthcare centers of Catalonia (North-East Spain; 7.5-million inhabitants), between January 2017 and June 2022. We retrieved data from the episode, including an assessment of suicide risk and the individual's socioeconomic and clinical characteristics. Data were summarized yearly and for the periods before and after the onset of the COVID-19 pandemic in Spain in March 2020. The analysis included 26,458 episodes of suicidal behavior (21,920 individuals); of them, 16,414 (62.0%) were a suicide attempt. The monthly moving average ranged between 300 and 400 episodes until July 2020, and progressively increased to over 600 episodes monthly. In the post-pandemic period, suicidal ideation increased at the expense of suicidal attempts. Cases showed a lower suicide risk; the percentage of females and younger individuals increased, whereas the prevalence of classical risk factors, such as living alone or lacking family network and a history of psychiatric diagnosis, decreased. In summary, suicidal behaviors have increased during the COVID-19 pandemic, with more episodes of suicidal ideations without attempt and younger and lower risk profiles.
Deep empirical risk minimization in finance: Looking into the future

Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over‐training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.
Generalized Parametric Contrastive Learning

In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to semantic segmentation task and obvious improvements are observed on 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
Microscopic reversibility and emergent elasticity in ultrastable granular systems

In a recent paper (Zhao et al., Phys Rev X, 2022, 12: 031,021), we reported experimental observations of “ultrastable” states in a shear-jammed granular system subjected to small-amplitude cyclic shear. In such states, all the particle positions and contact forces are reproduced after each shear cycle so that a strobed image of the stresses and particle positions appears static. In the present work, we report further analyses of data from those experiments to characterize both global and local responses of ultrastable states within a shear cycle, not just the strobed dynamics. We find that ultrastable states follow a power-law relation between shear modulus and pressure with an exponent β ≈ 0.5, reminiscent of critical scaling laws near jamming. We also examine the evolution of contact forces measured using photoelasticimetry. We find that there are two types of contacts: non-persistent contacts that reversibly open and close; and persistent contacts that never open and display no measurable sliding. We show that the non-persistent contacts make a non-negligible contribution to the emergent shear modulus. We also analyze the spatial correlations of the stress tensor and compare them to the predictions of a recent theory of the emergent elasticity of granular solids, the Vector Charge Theory of Granular mechanics and dynamics (VCTG) (Nampoothiri et al., Phys Rev Lett, 2020, 125: 118,002). We show that our experimental results can be fit well by VCTG, assuming uniaxial symmetry of the contact networks. The fits reveal that the response of the ultrastable states to additional applied stress is substantially more isotropic than that of the original shear-jammed states. Our results provide important insight into the mechanical properties of frictional granular solids created by shear.
The outburst of an embedded low-mass YSO in L1641

Context. Strong outbursts in very young and embedded protostars are rare and not yet fully understood. They are believed to originate from an increase in the mass accretion rate (M acc ) onto the source. Aims. We report the discovery of a strong outburst in a low-mass embedded young stellar object (YSO), namely 2MASS-J05424848-0816347 or [CTF93]216-2, as well as its photometric and spectroscopic follow-up. Methods. Using near- to mid-IR photometry and NIR low-resolution spectroscopy, we monitor the outburst, deriving its magnitude, duration, as well as the enhanced accretion luminosity and mass accretion rate. Results. [CTF93]216-2 increased in brightness by ~4.6, 4.0, 3.8, and 1.9 mag in the J, H, K s bands and at 24 μm, respectively, corresponding to an L bol increase of ~20 L ⊙ . Its early spectrum, probably taken soon after the outburst, displays a steep almost featureless continuum, with strong CO band heads and H 2 O broad-band absorption features, and Brγ line in emission. A later spectrum reveals more absorption features, allowing us to estimate T eff ~ 3200 K, M* ~ 0.25 M ⊙ , and M acc ~ 1.2 × 10 -6 M ⊙ yr -1 . This makes it one of the lowest mass YSOs with a strong outburst so far discovered.
Learning and forecasting of age-specific period mortality via B-spline processes with locally-adaptive dynamic coefficients

Although the analysis of human mortality has a well-established history, the attempt to accurately forecast future death-rate patterns for different age groups and time horizons still attracts active research. Such a predictive focus has motivated an increasing shift towards more flexible representations of age-specific period mortality trajectories at the cost of reduced interpretability. Although this perspective has led to successful predictive strategies, the inclusion of interpretable structures in modeling of human mortality can be, in fact, beneficial for improving forecasts. We pursue this direction via a novel B-spline process with locally-adaptive dynamic coefficients. Such a process outperforms state-of-the-art forecasting strategies by explicitly incorporating the core structures of period mortality within an interpretable formulation which enables inference on age-specific mortality trends and the corresponding rates of change across time. This is obtained by modeling the age-specific death counts via a Poisson log-normal model parameterized through a linear combination of B-spline bases with dynamic coefficients that characterize time changes in mortality rates via suitable stochastic differential equations. While flexible, the resulting formulation can be accurately approximated by a Gaussian state-space model that facilitates closed-form Kalman filtering, smoothing and forecasting, for both the trends of the spline coefficients and the corresponding first derivatives, which measure rates of change in mortality for different ages. As illustrated in applications to mortality data from different countries, our model outperforms state-of-the-art methods both in point forecasts and in calibration of predictive intervals. Moreover, it unveils substantial differences in mortality patterns across countries and ages, both in the past decades and during the COVID-19 pandemic.
The anomalous shift of the weak boson mass and the quintessence electroweak axion

One of the simplest ways to account for the observed W-boson mass shift is to introduce the SU(2) L triplet Higgs boson with zero hypercharge, whose vacuum expectation value is about 3 GeV. If the triplet is heavy enough at (1) TeV, it essentially contributes only to T parameter without any conflict with the observation. The presence of a complex triplet Higgs boson raises the SU(2) L gauge coupling constant to α 2(M PL) ≃ 1/44 at the Planck scale. Thanks to this larger gauge coupling constant, we show that the electroweak axion vacuum energy explains the observed cosmological constant provided that the axion field is located near the hill top of the potential at present.
Approximation in Hilbert spaces of the Gaussian and other weighted power series kernels

This article considers linear approximation based on function eval-uations in reproducing kernel Hilbert spaces of the Gaussian kernel and a more general class of weighted power series kernels on the interval [ − 1 , 1]. We derive almost matching upper and lower bounds on the worst-case error, measured both in the uniform and L 2 ([ − 1 , 1])-norm, in these spaces. The results show that if the power series kernel expansion coeﬃcients α − 1 n decay at least fac-torially, their rate of decay controls that of the worst-case error. Speciﬁcally, (i) the n th minimal error decays as α − 1 / 2 n up to a sub-exponential factor and (ii) for any n sampling points in [ − 1 , 1] there exists a linear algorithm whose error decays as α − 1 / 2 n up to an exponential factor. For the Gaussian kernel the dominating factor in the bounds is ( n ! ) − 1 / 2 .
Investigating self-supervised learning for lyrics recognition

Lyrics recognition is an important task in music processing. Despite the great number of traditional algorithms such as the hybrid HMM-TDNN model achieving good performance, studies on applying end-to-end models and self-supervised learning (SSL) are limited. In this paper, we ﬁrst establish an end-to-end baseline for lyrics recognition and then explore the performance of SSL models. We evaluate four upstream SSL models based on their training method (masked reconstruction, masked prediction, autoregressive reconstruction, contrastive model). After applying the SSL model, the best performance improved by 5.23% for the dev set and 2.4% for the test set compared with the previous state-of-art baseline system even without language model trained by large corpus. Moreover, we study the generalization ability of the SSL features considering that those models were not trained on music datasets.
Multi-Agent Sequential Decision-Making via Communication

Communication helps agents to obtain information about others so that better coordinated behavior can be learned. Some existing work communicates predicted future trajectory with others, hoping to get clues about what others would do for better coordination. However, circular dependencies sometimes can occur when agents are treated synchronously so it is hard to coordinate decision-making. In this paper, we propose a novel communication scheme, Sequential Communication (SeqComm). SeqComm treats agents asynchronously (the upper-level agents make decisions before the lower-level ones) and has two communication phases. In negotiation phase, agents determine the priority of decision-making by communicating hidden states of observations and comparing the value of intention, which is obtained by modeling the environment dynamics. In launching phase, the upper-level agents take the lead in making decisions and communicate their actions with the lower-level agents. Theoretically, we prove the policies learned by SeqComm are guaranteed to improve monotonically and converge. Empirically, we show that SeqComm outperforms existing methods in various multi-agent cooperative tasks.
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks. Nevertheless, one known challenge of these methods is their potential to introduce unfairness towards sensitive attributes. Although recent approaches have focused on enhancing fairness in AL, they tend to reduce the model's accuracy. To address this issue, we propose a novel strategy, named Fair Active Learning using fair Clustering, Uncertainty, and Representativeness (FAL-CUR), to improve fairness in AL. FAL-CUR tackles the fairness problem in AL by combining fair clustering with an acquisition function that determines which samples to query based on their uncertainty and representativeness scores. We evaluate the performance of FAL-CUR on four real-world datasets, and the results demonstrate that FAL-CUR achieves a 15% - 20% improvement in fairness compared to the best state-of-the-art method in terms of equalized odds while maintaining stable accuracy scores. Furthermore, an ablation study highlights the crucial roles of fair clustering in preserving fairness and the acquisition function in stabilizing the accuracy performance.
Quantifying and correcting geolocation error in sampling LiDAR forest canopy observations using high spatial accuracy ALS: A case study involving GEDI

Geolocation error in sampling LiDAR measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced ﬁeld measurements or other remotely sensed data. Integrating sampling LiDAR with other georeferenced data is especially problematic when geolocation error is not well quantiﬁed. Here, we propose and illustrate a model that uses high spatial accuracy airborne laser scanning (ALS) data from
Gravitational recoil from binary black hole mergers in scalar field clouds

In vacuum, the gravitational recoil of the final black hole from the merger of two black holes depends exclusively on the mass ratio and spins of the coalescing black holes, and on the eccentricity of the binary. If matter is present, accretion by the merging black holes may modify significantly their masses and spins, altering both the dynamics of the binary and the gravitational recoil of the remnant black hole. This paper considers such scenario. We investigate the effects on the kick of the final black hole from immersing the binary in a scalar field cloud. We consider two types of configurations: one with non-spinning and unequal-mass black holes, and a second with equal mass and spinning holes. For both types, we investigate how the gravitational recoil of the final black hole changes as we vary the energy density of the scalar field. We find that the accretion of scalar field by the merging black holes could have a profound effect. For the non-spinning, unequal-mass binary black holes, the kicks are in general larger than in the vacuum case, with speeds of approximately 1,200 km/s for binaries with mass ratio 2:1, one order of magnitude larger than in vacuum. For equal mass, binaries with black holes with spins aligned with the orbital angular momentum, kicks larger than in vacuum are also found. For systems with spins in the super-kick configuration, the scalar field triggers a similar dependence of the kicks with the entrance angle at merger as in the vacuum case but in this case depending on the strength of the scalar field.