Miracle-dz/UniFAR-FTUs huggingface dataset:
Imprints of Einstein-Maxwell-dilaton-axion gravity in the observed shadows of Sgr A* and M87*

Einstein-Maxwell dilaton-axion (EMDA) gravity provides a simple framework to investigate the signatures of string theory. The axion and the dilaton fields arising in EMDA gravity have important implications in inflationary cosmology and in addressing the late time acceleration of the universe. It is therefore instructive to explore the implications of such a model in explaining the astrophysical observations. In this work we explore the role of EMDA gravity in explaining the observed shadows of black holes (M87* and Sgr A*) released by the Event Horizon Telescope (EHT) collaboration. The Kerr-Sen metric represents the exact, stationary and axisymmetric black hole solution of EMDA gravity. Such a black hole is characterized by the angular momentum $a$ acquired from the axionic field and the dilatonic charge $r_2$ arising from string compactifications. We study the role of spin and the dilaton charge in modifying the shape and size of the black hole shadow. We note that black holes with larger dilaton charge cast a smaller shadow. We investigate the consequences of such a result in addressing the EHT observations of M87* and Sgr A*. Our analysis reveals that the shadow of M87* exhibits a preference towards the Kerr scenario. However, when 10% offset in the shadow diameter is considered, $0.1\lesssim r_2\lesssim 0.3$ is observationally favored within 1-$\sigma$. The shadow of Sgr A* on the other hand shows a preference towards the Kerr-Sen scenario since the central value of its shadow can be better explained by a non-zero dilaton charge $0.1 \lesssim r_2 \lesssim 0.4$. However, when the 1-$\sigma$ interval is considered the Kerr scenario is included. We discuss the implications of our results.
Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, so far, existing fairness metrics used in education are predictive performance-oriented, focusing on assessing biased outcomes across groups of students, without considering the behaviors of the models nor the severity of the biases in the outcomes. Therefore, we propose a novel metric, the Model Absolute Density Distance (MADD), to analyze models' discriminatory behaviors independently from their predictive performance. We also provide a complementary visualization-based analysis to enable fine-grained human assessment of how the models discriminate between groups of students. We evaluate our approach on the common task of predicting student success in online courses, using several common predictive classification models on an open educational dataset. We also compare our metric to the only predictive performance-oriented fairness metric developed in education, ABROCA. Results on this dataset show that: (1) fair predictive performance does not guarantee fair models' behaviors and thus fair outcomes, (2) there is no direct relationship between data bias and predictive performance bias nor discriminatory behaviors bias, and (3) trained on the same data, models exhibit different discriminatory behaviors, according to different sensitive features too. We thus recommend using the MADD on models that show satisfying predictive performance, to gain a finer-grained understanding on how they behave and to refine models selection and their usage.
Text encoders bottleneck compositionality in contrastive vision-language models

Performant vision-language (VL) models like CLIP represent captions using a single vector. How much information about language is lost in this bottleneck? We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e.g., single object, to object+property, to multiple interacting objects). Then, we train text-only recovery probes that aim to reconstruct captions from single-vector text representations produced by several VL models. This approach does not require images, allowing us to test on a broader range of scenes compared to prior work. We find that: 1) CLIP's text encoder falls short on more compositional inputs, including object relationships, attribute-object association, counting, and negations; 2) some text encoders work significantly better than others; and 3) text-only recovery performance predicts multi-modal matching performance on ControlledImCaps: a new evaluation benchmark we collect and release consisting of fine-grained compositional images and captions. Specifically, our results suggest text-only recoverability is a necessary (but not sufficient) condition for modeling compositional factors in contrastive VL models. We release our datasets and code.
How To Train Your (Compressed) Large Language Model

With the increase in the size of large language models (LLMs), we need compression methods that can reduce the model size while preserving the generality and zero-shot promptability of the model. This goal is more ambitious than the typical compression setup, which reduces the model's size at the expense of specializing it to a specific end-task. To study this, we develop a task-agnostic compression pipeline with a large-scale evaluation comprising language modeling perplexity and 12 zero-shot end-tasks. Our results show that a simple layer-wise pruning followed by continued language model pretraining matches or outperforms three existing state-of-the-art baselines while being 1.5x more computationally efficient. However, unlike typical task-specialized compression, our best-compressed model significantly underperforms a similar-sized model trained from scratch. We posit the half-sized pretrained model as an upper bound for task-agnostic compression and call for future work to bridge this gap under a reasonable token budget. Our findings highlight the inadequacy of existing compression methods for LLMs and establish a requirement for new methods that preserve a model's generality and zero-shot promptability under compression. We release our code and evaluation setup to facilitate reproducibility and help iterate on method design.
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy

When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren't among the given answer choices. Spreading probability mass across multiple surface forms with identical meaning (such as"bath"and"bathtub") is thought to cause an underestimation of a model's true performance, referred to as the"surface form competition"(SFC) hypothesis. This has motivated the introduction of various probability normalization methods. However, many core questions remain unanswered. How do we measure SFC? Are there direct ways of reducing it, and does doing so improve task performance? We propose a mathematical formalism for SFC which allows us to quantify and bound its impact for the first time. We identify a simple method for reducing it -- namely, increasing probability mass on the given answer choices by a) including them in the prompt and b) using in-context learning with even just one example. We show this method eliminates the impact of SFC in the majority of instances. Our experiments on three diverse datasets and six LMs reveal several additional surprising findings. For example, both normalization and prompting methods for reducing SFC can be ineffective or even detrimental to task performance for some LMs. We conclude with practical insights for effectively prompting LMs for multiple-choice tasks.
Pronominal Clitics in European and Brazilian Portuguese

This paper addresses two issues, the categorial nature of (pronominal) clitics and the conditions ruling their surface distribution as proclitics and enclitics, which we take as related. We claim that pronominal clitics are defective functional heads consisting of a bundle of φ-features, and hence, that they are merged in a fixed position in the functional structure of the clause. The (micro)variation in clitic order patterns will be accounted for through the interplay of verb movement and the Proclisis Parameter: when languages set the value ‘yes’ for this parameter, proclisis is the dominant pattern, whereas in languages setting the value ‘no’ for the same parameter, enclisis dominantly occurs; other differences between Romance languages and, in particular, EP and BP, are accounted for by the properties of the nodes T and Asp, namely, their ability to attract V and or to check uninterpretable features through Agree without Attract.
COMET-M: Reasoning about Multiple Events in Complex Sentences

Understanding the speaker's intended meaning often involves drawing commonsense inferences to reason about what is not stated explicitly. In multi-event sentences, it requires understanding the relationships between events based on contextual knowledge. We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence. COMET-M builds upon COMET (Bosselut et al., 2019), which excels at generating event-centric inferences for simple sentences, but struggles with the complexity of multi-event sentences prevalent in natural text. To overcome this limitation, we curate a multi-event inference dataset of 35K human-written inferences. We trained COMET-M on the human-written inferences and also created baselines using automatically labeled examples. Experimental results demonstrate the significant performance improvement of COMET-M over COMET in generating multi-event inferences. Moreover, COMET-M successfully produces distinct inferences for each target event, taking the complete context into consideration. COMET-M holds promise for downstream tasks involving natural text such as coreference resolution, dialogue, and story understanding.
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation

In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model's weaknesses and foster a tailored learning experience by generating targeted exercises aligned with educational science principles, such as knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math tutor and run two steps iteratively: 1) assessing the student model's current learning status on a GPT-generated exercise book, and 2) improving the student model by training it with tailored exercise samples generated by GPT-3. Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and PaLM) in accuracy across three distinct benchmarks while employing significantly fewer parameters. Furthermore, we provide a comprehensive analysis of the various components within our methodology to substantiate their efficacy.
Rethinking the Evaluation Protocol of Domain Generalization

Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learned from multiple training domains to generalize to unseen test domains. To accurately evaluate the OOD generalization ability, it is required that test data information is unavailable. However, the current domain generalization protocol may still have potential test data information leakage. This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection. We propose modifications to the current protocol that we should employ self-supervised pretraining or train from scratch instead of employing the current supervised pretraining, and we should use multiple test domains. These would result in a more precise evaluation of OOD generalization ability. We also rerun the algorithms with the modified protocol and introduce new leaderboards to encourage future research in domain generalization with a fairer comparison.
Detecting disparities in police deployments using dashcam data

Large-scale policing data is vital for detecting inequity in police behavior and policing algorithms. However, one important type of policing data remains largely unavailable within the United States: aggregated police deployment data capturing which neighborhoods have the heaviest police presences. Here we show that disparities in police deployment levels can be quantified by detecting police vehicles in dashcam images of public street scenes. Using a dataset of 24,803,854 dashcam images from rideshare drivers in New York City, we find that police vehicles can be detected with high accuracy (average precision 0.82, AUC 0.99) and identify 233,596 images which contain police vehicles. There is substantial inequality across neighborhoods in police vehicle deployment levels. The neighborhood with the highest deployment levels has almost 20 times higher levels than the neighborhood with the lowest. Two strikingly different types of areas experience high police vehicle deployments — 1) dense, higher-income, commercial areas and 2) lower-income neighborhoods with higher proportions of Black and Hispanic residents. We discuss the implications of these disparities for policing equity and for algorithms trained on policing data.
Deep Learning based Forecasting: a case study from the online fashion industry

Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.
An extremely metal-poor star complex in the reionization era: Approaching Population III stars with JWST

We present JWST/NIRSpec integral field spectroscopy (IFS) of a lensed Population III candidate stellar complex (dubbed Lensed And Pristine 1, LAP1), with a lensing-corrected stellar mass ~<10^4 Msun, absolute luminosity M_UV>-11.2 (m_UV>35.6), confirmed at redshift 6.639 +/- 0.004. The system is strongly amplified (\mu>~ 100) by straddling a critical line of the Hubble Frontier Field galaxy cluster MACS J0416. Despite the stellar continuum is currently not detected in the Hubble and JWST/NIRCam and NIRISS imaging, arclet-like shapes of Lyman and Balmer lines, Lya, Hg, Hb and Ha are detected with NIRSpec IFS with signal-to-noise ratios SNR=5-13 and large equivalent widths (>300-2000A), along with a remarkably weak [OIII]4959-5007 at SNR ~ 4. LAP1 shows a large ionizing photon production efficiency, log(\xi_{ion}[erg~Hz^{-1}])>26. From the metallicity indexes R23 = ([OIII]4959-5007 + [OII]3727) / Hb ~<0.74 and R3 = ([OIII]5007 / Hb) = 0.55 +/- 0.14, we derive an oxygen abundance 12+log(O/H) ~<6.3. Intriguingly, the Ha emission is also measured in mirrored sub-components where no [OIII] is detected, providing even more stringent upper limits on the metallicity if in-situ star formation is ongoing in this region (12+log(O/H)<6, or Z<0.002 Zsun). The formal stellar mass limit of the sub-components would correspond to ~10^{3} Msun or M_UV fainter than -10. Alternatively, such a metal-free pure line emitting region could be the first case of a fluorescing HI gas region, induced by transverse escaping ionizing radiation from a nearby star-complex. The presence of large equivalent-width hydrogen lines and the deficiency of metal lines in such a small region, make LAP1 the most metal poor star-forming region currently known in the reionization era and a promising site that may host isolated, pristine stars.
Accelerated Nonconvex ADMM with Self-Adaptive Penalty for Rank-Constrained Model Identification

The alternating direction method of multipliers (ADMM) has been widely adopted in low-rank approximation and low-order model identification tasks; however, the performance of nonconvex ADMM is highly reliant on the choice of penalty parameter. To accelerate ADMM for solving rank-constrained identification problems, this paper proposes a new self-adaptive strategy for automatic penalty update. Guided by first-order analysis of the increment of the augmented Lagrangian, the self-adaptive penalty updating enables effective and balanced minimization of both primal and dual residuals and thus ensures a stable convergence. Moreover, improved efficiency can be obtained within the Anderson acceleration scheme. Numerical examples show that the proposed strategy significantly accelerates the convergence of nonconvex ADMM while alleviating the critical reliance on tedious tuning of penalty parameters.
Field-free all-optical switching and electrical read-out of Tb/Co-based magnetic tunnel junctions

Switching of magnetic tunnel junction using femto-second laser enables a possible path for THz frequency memory operation, which means writing speeds 2 orders of magnitude faster than alternative electrical approaches based on spin transfer or spin orbit torque. In this work we demonstrate successful field-free 50fs single laser pulse driven magnetization reversal of [Tb/Co] based storage layer in a perpendicular magnetic tunnel junction. The nanofabricated magnetic tunnel junction devices have an optimized bottom reference electrode and show Tunnel Magnetoresistance Ratio values (TMR) up to 74\% after patterning down to sub-100nm lateral dimensions. Experiments on continuous films reveal peculiar reversal patterns of concentric rings with opposite magnetic directions, above certain threshold fluence. These rings have been correlated to patterned device switching probability as a function of the applied laser fluence. Moreover, the magnetization reversal is independent on the duration of the laser pulse. According to our macrospin model, the underlying magnetization reversal mechanism can be attributed to an in-plane reorientation of the magnetization due to a fast reduction of the out-of-plane uniaxial anisotropy. These aspects are of great interest both for the physical understanding of the switching phenomenon and their consequences for all-optical-switching memory devices, since they allow for a large fluence operation window with high resilience to pulse length variability.
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks?

Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, numerous studies have explored their application to text classification, as an alternative to traditional feature representation models. However, most studies considered just a specific domain and validated on data with particular characteristics. This work presents an extensive empirical investigation of graph-based text representation methods proposed for text classification, identifying practical implications and open challenges in the field. We compare several GNN architectures as well as BERT across five datasets, encompassing short and also long documents. The results show that: i) graph performance is highly related to the textual input features and domain, ii) despite its outstanding performance, BERT has difficulties converging when dealing with short texts, iii) graph methods are particularly beneficial for longer documents.
Sharpness-Aware Data Poisoning Attack

Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' training dataset such that the trained models have inference failures. While previous studies have executed different types of attacks, one major challenge that greatly limits their effectiveness is the uncertainty of the re-training process after the injection of poisoning samples, including the re-training initialization or algorithms. To address this challenge, we propose a novel attack method called ''Sharpness-Aware Data Poisoning Attack (SAPA)''. In particular, it leverages the concept of DNNs' loss landscape sharpness to optimize the poisoning effect on the worst re-trained model. It helps enhance the preservation of the poisoning effect, regardless of the specific retraining procedure employed. Extensive experiments demonstrate that SAPA offers a general and principled strategy that significantly enhances various types of poisoning attacks.
Are Large Language Models Robust Coreference Resolvers?

Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language. At the same time, pre-trained large language models (LMs) have been reported to exhibit strong zero- and few-shot learning abilities across a wide range of NLP tasks. However, prior work mostly studied this ability using artificial sentence-level datasets such as the Winograd Schema Challenge. In this paper, we assess the feasibility of prompt-based coreference resolution by evaluating instruction-tuned language models on difficult, linguistically-complex coreference benchmarks (e.g., CoNLL-2012). We show that prompting for coreference can outperform current unsupervised coreference systems, although this approach appears to be reliant on high-quality mention detectors. Further investigations reveal that instruction-tuned LMs generalize surprisingly well across domains, languages, and time periods; yet continued fine-tuning of neural models should still be preferred if small amounts of annotated examples are available.
Adiabatic driving and geometric phases in classical systems

We study the concepts of adiabatic driving and geometric phases of classical integrable systems under the Koopman-von Neumann formalism. In close relation to what happens to a quantum state, a classical Koopman-von Neumann eigenstate will acquire a geometric phase factor $exp\left\{ i\Phi\right\} $ after a closed variation of the parameters $\lambda$ in its associated Hamiltonian. The explicit form of $\Phi$ is then derived for integrable systems, and its relation with the Hannay angles is shown. Additionally, we use quantum formulas to write a classical adiabatic gauge potential that generates adiabatic unitary flow between classical eigenstates, and we explicitly show the relationship between the potential and the classical geometric phase.