Miracle-dz/UniFAR-FTUs huggingface dataset:
WR-One2Set: Towards Well-Calibrated Keyphrase Generation

Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a set and has achieved competitive performance. Nevertheless, we observe serious calibration errors outputted by ONE2SET, especially in the over-estimation of ∅ token (means “no corresponding keyphrase”). In this paper, we deeply analyze this limitation and identify two main reasons behind: 1) the parallel generation has to introduce excessive ∅ as padding tokens into training instances; and 2) the training mechanism assigning target to each slot is unstable and further aggravates the ∅ token over-estimation. To make the model well-calibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive instance-level cost Weighting strategy and a target Re-assignment mechanism. The former dynamically penalizes the over-estimated slots for different instances thus smoothing the uneven training distribution. The latter refines the original inappropriate assignment and reduces the supervisory signals of over-estimated slots. Experimental results on commonly-used datasets demonstrate the effectiveness and generality of our proposed paradigm.
On Topology Changes in Quantum Field Theory and Quantum Gravity

Two singularity theorems can be proven if one attempts to let a Lorentzian cobordism interpolate between two topologically distinct manifolds. On the other hand, Cartier and DeWitt-Morette have given a rigorous definition for quantum field theories (qfts) by means of path integrals. This article uses their results to study whether qfts can be made compatible with topology changes. We show that path integrals over metrics need a finite norm for the latter and for degenerate metrics, this problem can sometimes be resolved with tetrads. We prove that already in the neighborhood of some cuspidal singularities, difficulties can arise to define certain qfts. On the other hand, we show that simple qfts can be defined around conical singularities that result from a topology change in a simple setup. We argue that the ground state of many theories of quantum gravity will imply a small cosmological constant and, during the expansion of the universe, will cause frequent topology changes. Unfortunately, it is difficult to describe the transition amplitudes consistently due to the aforementioned problems. We argue that one needs to describe qfts by stochastic differential equations, and in the case of gravity, by Regge calculus in order to resolve this problem.
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning

The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection. However, prior works focus on concatenating data from multiple platforms, inherently adopting Empirical Risk Minimization (ERM) method. In this work, we address this challenge from the perspective of domain generalization objective. We design SCL-Fish, a supervised contrastive learning integrated meta-learning algorithm to detect abusive language on unseen platforms. Our experimental analysis shows that SCL-Fish achieves better performance over ERM and the existing state-of-the-art models. We also show that SCL-Fish is data-efficient and achieves comparable performance with the large-scale pre-trained models upon finetuning for the abusive language detection task.
The energy of dilute Bose gases

For a dilute system of non-relativistic bosons interacting through a positive $L^1$ potential $v$ with scattering length $a$ we prove that the ground state energy density satisfies the bound $e(\rho) \geq 4\pi a \rho^2 (1+ \frac{128}{15\sqrt{\pi}} \sqrt{\rho a^3} +o(\sqrt{\rho a^3}\,))$, thereby proving the Lee-Huang-Yang formula for the energy density.
On the capacity of neural networks

in English The aim of this thesis is to compare the capacity of different models of neural networks. We start by analysing the problem solving capacity of a single perceptron using a simple combinatorial argument. After some observations on the storage capacity of a basic network, known as an associative memory, we introduce a powerful statistical mechanical approach to calculate its capacity in the training rule-dependent Hopfield model. With the aim of finding a more general definition that can be applied even to quantum neural nets, we then follow Gardner’s work, which let us get rid of the dependency on the training rule, and comment the results obtained by Lewenstein et al. by applying Gardner’s methods on a recently proposed quantum perceptron model. Abstract in Italiano Lo scopo di questa tesi è comparare la capacità di diversi modelli di reti neurali. Tramite un semplice calcolo combinatiorio, viene valutata la capacità del perceptron semplice. Dopo alcune osservazioni riguardo alla capacità di un elementare modello di rete, chiamato memoria associativa, viene introdotto l’approccio meccanico-statistico, che permette il calcolo della capacità della rete in un modello, detto di Hopfield, dipendente dalla regola di apprendimento dei neuroni. Dunque, con l’obiettivo di trovare un approccio che permetta di svincolarsi da questa dipendenza, vengono seguiti i lavori di E. Gardner sul calcolo della capacità. Infine vengono commentati i risultati ottenuti da Lewenstein et al. applicando l’approccio di Gardner ad un modello di neurone quantistico recentemente proposto.in Italiano Lo scopo di questa tesi è comparare la capacità di diversi modelli di reti neurali. Tramite un semplice calcolo combinatiorio, viene valutata la capacità del perceptron semplice. Dopo alcune osservazioni riguardo alla capacità di un elementare modello di rete, chiamato memoria associativa, viene introdotto l’approccio meccanico-statistico, che permette il calcolo della capacità della rete in un modello, detto di Hopfield, dipendente dalla regola di apprendimento dei neuroni. Dunque, con l’obiettivo di trovare un approccio che permetta di svincolarsi da questa dipendenza, vengono seguiti i lavori di E. Gardner sul calcolo della capacità. Infine vengono commentati i risultati ottenuti da Lewenstein et al. applicando l’approccio di Gardner ad un modello di neurone quantistico recentemente proposto.
Numerical analysis of Coleman–de Luccia tunneling

In this paper, we study the false vacuum decay of a single scalar field ϕ coupled to gravity described by the Coleman–de Luccia (CdL) instanton. We show that it is possible to numerically calculate the bounce factor, which is related to the CdL tunneling rate, without using the thin-wall approximation. In this paper, we consider 1/cosh(ϕ)- and cos(ϕ)-type potentials as examples, which have cosmological and phenomenological applications. Especially, in the cos(ϕ)-type potential, we show that the range of values in which axion decay constant can take is restricted by the form of the periodic potential if the CdL tunneling occurs.
Actionable Entities Recognition Benchmark for Interactive Fiction

This paper presents a new natural language processing task - Actionable Entities Recognition (AER) - recognition of entities that protagonists could interact with for further plot development. Though similar to classical Named Entity Recognition (NER), it has profound differences. In particular, it is crucial for interactive fiction, where the agent needs to detect entities that might be useful in the future. We also discuss if AER might be further helpful for the systems dealing with narrative processing since actionable entities profoundly impact the causal relationship in a story. We validate the proposed task on two previously available datasets and present a new benchmark dataset for the AER task that includes 5550 descriptions with one or more actionable entities.
Spreading Processes with Population Heterogeneity Over Multi-Layer Networks

Modeling spreading processes over complex networks has been receiving increasing attention. For example, bond percolation models considering population heterogeneity have been used to derive insights into disease spread and misinformation control. However, most works on spreading processes with population heterogeneity only concentrate on single-layer contact networks. To study how the course of a spreading process changes due to multiple layers of contact networks (e.g., neighborhood vs. schools or Twitter vs. Facebook) while considering population heterogeneity from a principled, mathematical lens, we propose the Multi-layer Mask model based on SIR dynamics. We derive analytical expressions for three fundamental epidemiological quantities: the probability of emergence, the epidemic threshold, and the expected epidemic size. Analytical results are shown to be in near-perfect agreement with the numerical results obtained through extensive simulations. These results reveal the impact of the structure of the multi-layer contact network, viral transmission dynamics, and population heterogeneity on the final state of the spreading process. Thus, they might help develop mitigation and control strategies for disease spread and information diffusion.
The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding

In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while temperature, top-k, and nucleus sampling often yield diverse but low-quality outputs. In this work, we present crowd sampling, a family of decoding methods based on Bayesian risk minimization, to address this diversity-quality trade-off. Inspired by the principle of"the wisdom of the crowd,"crowd sampling seeks to select a candidate from a pool of candidates that has the least expected risk (i.e., highest expected reward) under a generative model according to a given utility function. Crowd sampling can be seen as a generalization of numerous existing methods, including majority voting, and in practice, it can be used as a drop-in replacement for existing sampling methods. Extensive experiments show that crowd sampling delivers improvements of 3-7 ROUGE and BLEU points across a wide range of tasks, including summarization, data-to-text, translation, and textual style transfer, while achieving new state-of-the-art results on WebNLG and WMT'16.
X-RAY EMISSION FROM SUPERNOVAE IN DENSE CIRCUMSTELLAR MATTER ENVIRONMENTS: A SEARCH FOR COLLISIONLESS SHOCKS

The optical light curve of some supernovae (SNe) may be powered by the outward diffusion of the energy deposited by the explosion shock (the so-called shock breakout) in optically thick (τ ≳ 30) circumstellar matter (CSM). Recently, it was shown that the radiation-mediated and radiation-dominated shock in an optically thick wind must transform into a collisionless shock and can produce hard X-rays. The X-rays are expected to peak at late times, relative to maximum visible light. Here we report on a search, using Swift/XRT and Chandra, for X-ray emission from 28 SNe that belong to classes whose progenitors are suspected to be embedded in dense CSM. Our sample includes 19 Type IIn SNe, one Type Ibn SN, and eight hydrogen-poor superluminous SNe (SLSN-I such as SN 2005ap). Two SNe (SN 2006jc and SN 2010jl) have X-ray properties that are roughly consistent with the expectation for X-rays from a collisionless shock in optically thick CSM. However, the X-ray emission from SN 2006jc can also be explained as originating in an optically thin region. Thus, we propose that the optical light curve of SN 2010jl is powered by shock breakout in CSM. We suggest that two other events (SN 2010al and SN 2011ht) were too X-ray bright during the SN maximum optical light to be explained by the shock-breakout model. We conclude that the light curves of some, but not all, SNe IIn/Ibn are powered by shock breakout in CSM. For the rest of the SNe in our sample, including all of the SLSN-I events, our X-ray limits are not deep enough and were typically obtained too early (i.e., near the SN maximum light) for definitive conclusions about their nature. Late-time X-ray observations are required in order to further test whether these SNe are indeed embedded in dense CSM. We review the conditions required for a shock breakout in a wind profile. We argue that the timescale, relative to maximum light, for the SN to peak in X-rays is a probe of the column density and the density profile above the shock region. In SNe whose X-ray emission slowly rises, and peaks at late times, the optical light curve is likely powered by the diffusion of shock energy in a dense CSM. We note that if the CSM density profile falls faster than a constant-rate wind-density profile, then X-rays may escape at earlier times than estimated for the wind-profile case. Furthermore, if the CSM has a region in which the density profile is very steep relative to a steady wind-density profile, or if the CSM is neutral, then the radio free–free absorption may be sufficiently low for radio emission to be detected.
Some cases of the Zilber-Pink conjecture for curves in $\mathcal{A}_g$

Following our work in \cite{papas2022height}, we extend the height bounds established by Y. Andr\'e in his seminal research monograph \cite{andre1989g} for $1$-parameter families of abelian varieties defined over number fields. In our exposition we no longer assume that the family acquires completely multiplicative reduction at some point, as in Andr\'e's original result. As a corollary of these height bounds, we obtain unconditional results of Zilber-Pink-type for curves in $\mathcal{A}_g$, building upon recent results of C. Daw and M. Orr.
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization

We investigate composed image retrieval with text feedback. Users gradually look for the target of interest by moving from coarse to fine-grained feedback. However, existing methods merely focus on the latter, i.e., fine-grained search, by harnessing positive and negative pairs during training. This pair-based paradigm only considers the one-to-one distance between a pair of specific points, which is not aligned with the one-to-many coarse-grained retrieval process and compromises the recall rate. In an attempt to fill this gap, we introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval by considering the multi-grained uncertainty. The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively. Specifically, our method contains two modules: uncertainty modeling and uncertainty regularization. (1) The uncertainty modeling simulates the multi-grained queries by introducing identically distributed fluctuations in the feature space. (2) Based on the uncertainty modeling, we further introduce uncertainty regularization to adapt the matching objective according to the fluctuation range. Compared with existing methods, the proposed strategy explicitly prevents the model from pushing away potential candidates in the early stage, and thus improves the recall rate. On the three public datasets, i.e., FashionIQ, Fashion200k, and Shoes, the proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline, respectively.
A proposal on the galaxy intrinsic alignment self‐calibration in weak lensing surveys

The galaxy intrinsic alignment causes the galaxy ellipticity-ellipticity power spectrum between two photometric redshifts to decrease faster with respect to the redshift separation Delta z(P), for fixed mean redshift. This offers a valuable diagnosis on the intrinsic alignment. We show that the distinctive dependences of the GG, II and GI correlations on Delta z(P) over the range vertical bar Delta z(P)vertical bar less than or similar to 0.2 can be understood robustly without strong assumptions on the intrinsic alignment. This allows us to measure the intrinsic alignment within each conventional photo-z bin of typical size greater than or similar to 0.2, through lensing tomography of photo-z bin size similar to 0.01. Both the statistical and systematical errors in the lensing cosmology can be reduced by this self-calibration technique.
Large-Scale Bidirectional Training for Zero-Shot Image Captioning

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.
The Best Path Algorithm automatic variables selection via High Dimensional Graphical Models

This paper proposes a new algorithm for an automatic variable selection procedure in High Dimensional Graphical Models. The algorithm selects the relevant variables for the node of interest on the basis of mutual information. Several contributions in literature have investigated the use of mutual information in selecting the appropriate number of relevant features in a large data-set, but most of them have focused on binary outcomes or required high computational effort. The algorithm here proposed overcomes these drawbacks as it is an extension of Chow and Liu's algorithm. Once, the probabilistic structure of a High Dimensional Graphical Model is determined via the said algorithm, the best path-step, including variables with the most explanatory/predictive power for a variable of interest, is determined via the computation of the entropy coefficient of determination. The latter, being based on the notion of (symmetric) Kullback-Leibler divergence, turns out to be closely connected to the mutual information of the involved variables. The application of the algorithm to a wide range of real-word and publicly data-sets has highlighted its potential and greater effectiveness compared to alternative extant methods.
The Path to Durable Linearizability

There is an increasing body of literature proposing new and efficient persistent versions of concurrent data structures ensuring that a consistent state can be recovered after a power failure or a crash. Their correctness is typically stated in terms of durable linearizability (DL), which requires that individual library operations appear to be executed atomically in a sequence consistent with the real-time order and, moreover, that recovering from a crash return a state corresponding to a prefix of that sequence. Sadly, however, there are hardly any formal DL proofs, and those that do exist cover the correctness of rather simple persistent algorithms on specific (simplified) persistency models. In response, we propose a general, powerful, modular, and incremental proof technique that can be used to guide the development and establish DL. Our technique is (1) general, in that it is not tied to a specific persistency and/or consistency model, (2) powerful, in that it can handle the most advanced persistent algorithms in the literature, (3) modular, in that it allows the reuse of an existing linearizability argument, and (4) incremental, in that the additional requirements for establishing DL depend on the complexity of the algorithm to be verified. We illustrate this technique on various versions of a persistent set, leading to the link-free set of Zuriel et al.
Exploring the LHC Landscape with Dileptons

The dilepton decay channels provide clean signatures and are an ideal hunting ground for high mass resonant, like Z', or non-resonant, like contact interactions or extra dimensions, searches at the LHC. The production of high invariant mass opposite sign lepton pairs in proton-proton collisions in the Standard Model is dominated by the Drell-Yan process. In addition to this photon or Z exchange mediated mechanism, photons radiated by the incoming protons can collide and produce lepton pairs. In this paper detailed calculations of the Drell-Yan process at next-to-next-to-leading order in QCD and next-to-leading order in the electroweak corrections, augmented with the photon-induced effects, are presented in the typical acceptance of a multi-purpose LHC detector at center of mass energy 13 TeV. Estimates of the expected backgrounds for new physics searches are provided for dilepton invariant masses up to the LHC kinematic limit.
Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques

Predicting fund performance is beneficial to both investors and fund managers, and yet is a challenging task. In this paper, we have tested whether deep learning models can predict fund performance more accurately than traditional statistical techniques. Fund performance is typically evaluated by the Sharpe ratio, which represents the risk-adjusted performance to ensure meaningful comparability across funds. We calculated the annualised Sharpe ratios based on the monthly returns time series data for more than 600 open-end mutual funds investing in listed large-cap equities in the United States. We find that long short-term memory (LSTM) and gated recurrent units (GRUs) deep learning methods, both trained with modern Bayesian optimization, provide higher accuracy in forecasting funds' Sharpe ratios than traditional statistical ones. An ensemble method, which combines forecasts from LSTM and GRUs, achieves the best performance of all models. There is evidence to say that deep learning and ensembling offer promising solutions in addressing the challenge of fund performance forecasting.