Miracle-dz/UniFAR-FTUs huggingface dataset:
Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning

The concept of Label Distribution Learning (LDL) is a technique to stabilize classification and regression problems with ambiguous and/or imbalanced labels. A prototypical use-case of LDL is human age estimation based on profile images. Regarding this regression problem, a so called Deep Label Distribution Learning (DLDL) method has been developed. The main idea is the joint regression of the label distribution and its expectation value. However, the original DLDL method uses loss components with different mathematical motivation and, thus, different scales, which is why the use of a hyperparameter becomes necessary. In this work, we introduce a loss function for DLDL whose components are completely defined by Kullback-Leibler (KL) divergences and, thus, are directly comparable to each other without the need of additional hyperparameters. It generalizes the concept of DLDL with regard to further use-cases, in particular for multi-dimensional or multi-scale distribution learning tasks.
Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions

There has been much recent interest in the regulation of AI. We argue for a view based on civil-rights legislation, built on the notions of equal treatment and equal impact. In a closed-loop view of the AI system and its users, the equal treatment concerns one pass through the loop. Equal impact, in our view, concerns the long-run average behaviour across repeated interactions. In order to establish the existence of the average and its properties, one needs to study the ergodic properties of the closed-loop and its unique stationary measure.
Non-convergence of the rotating stratified flows toward the quasi-geostrophic dynamics

The quasi-geostrohpic (QG) equation has been used to capture the asymptotic dynamics of the rotating stratified Boussinesq flows in the regime of strong stratification and rapid rotation. In this paper, we establish the invalidity of such approximation when the rotation-stratification ratio is either fixed to be unity or tends to unity sufficiently slowly in the asymptotic regime: the difference between the rotating stratified Boussinesq flow and the corresponding QG flow remains strictly away from zero, independently of the intensities of rotation and stratification. In contrast, we also show that the convergence occurs when the rotation-stratification ratio is fixed to be a number other than unity or converges to unity sufficiently fast. As a corollary, we compute a lower bound of the convergence rate, which blows up as the rotation-stratification ratio goes to unity.
Informative Language Representation Learning for Massively Multilingual Neural Machine Translation

In a multilingual neural machine translation model that fully shares parameters across all languages, an artificial language token is usually used to guide translation into the desired target language. However, recent studies show that prepending language tokens sometimes fails to navigate the multilingual neural machine translation models into right translation directions, especially on zero-shot translation. To mitigate this issue, we propose two methods, language embedding embodiment and language-aware multi-head attention, to learn informative language representations to channel translation into right directions. The former embodies language embeddings into different critical switching points along the information flow from the source to the target, aiming at amplifying translation direction guiding signals. The latter exploits a matrix, instead of a vector, to represent a language in the continuous space. The matrix is chunked into multiple heads so as to learn language representations in multiple subspaces. Experiment results on two datasets for massively multilingual neural machine translation demonstrate that language-aware multi-head attention benefits both supervised and zero-shot translation and significantly alleviates the off-target translation issue. Further linguistic typology prediction experiments show that matrix-based language representations learned by our methods are capable of capturing rich linguistic typology features.
Geometric Main Conjectures in Function Fields

We prove an Equivariant Main Conjecture in Iwasawa Theory along any rank one, sign-normalized Drinfeld modular, split at ∞ Iwasawa tower of a general function ﬁeld of characteristic p , for the Iwasawa modules recently considered by Greither and Popescu in [5], in their proof of the classical Equivariant Main Conjecture along the (arithmetic) cyclotomic Iwasawa tower. As a consequence, we prove an Equivariant Main Conjecture for a projective limit of certain Ritter–Weiss type modules, along the same Drinfeld modular Iwasawa towers. This generalizes the results of Angl`es et.al. [1], Bandini et al. [2], and Coscelli [3], for the split at ∞ piece of the Iwasawa towers considered in loc.cit. , and reﬁnes the results in [5].
On Steklov Eigenspaces for Free Boundary Minimal Surfaces in the Unit Ball

We develop new methods to compare the span C (Σ) of the coordinate functions on a free boundary minimal submanifold Σ embedded in the unit n -ball B n with its ﬁrst Steklov eigenspace E σ 1 (Σ). Using these methods, we show that C ( A ) = E σ 1 ( A ) for any embedded free boundary minimal annulus A in B 3 invariant under the antipodal map, and thus prove that A is congruent to the critical catenoid. We also conﬁrm that C = E σ 1 for any free boundary minimal surface embedded in B 3 with the symmetries of many known or expected examples, including: examples of any positive genus from stacking at least three disks; two inﬁnite families of genus 0 examples with dihedral symmetry, as well as a ﬁnite family with the various Platonic symmetries; and examples of any genus by desingularizing several disks that meet at equal angles along a diameter of the ball.
Construction material classification on imbalanced datasets using Vision Transformer (ViT) architecture

This research proposes a reliable model for identifying different construction materials with the highest accuracy, which is exploited as an advantageous tool for a wide range of construction applications such as automated progress monitoring. In this study, a novel deep learning architecture called Vision Transformer (ViT) is used for detecting and classifying construction materials. The robustness of the employed method is assessed by utilizing different image datasets. For this purpose, the model is trained and tested on two large imbalanced datasets, namely Construction Material Library (CML) and Building Material Dataset (BMD). A third dataset is also generated by combining CML and BMD to create a more imbalanced dataset and assess the capabilities of the utilized method. The achieved results reveal an accuracy of 100 percent in evaluation metrics such as accuracy, precision, recall rate, and f1-score for each material category of three different datasets. It is believed that the suggested model accomplishes a robust tool for detecting and classifying different material types. To date, a number of studies have attempted to automatically classify a variety of building materials, which still have some errors. This research will address the mentioned shortcoming and proposes a model to detect the material type with higher accuracy. The employed model is also capable of being generalized to different datasets.
A Practical Guide on Explainable Ai Techniques Applied on Biomedical Use Case Applications

Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and prediction skills, their functioning does not allow obtaining detailed explanations of their behaviour. As opaque machine learning models are increasingly being employed to make important predictions in critical environments, the danger is to create and use decisions that are not justiﬁable or legitimate. Therefore, there is a general agreement on the importance of endowing machine learning models with explainability. EXplainable Artiﬁcial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is meant to be the go-to handbook for any audience with a computer science background aiming at getting intuitive insights on machine learning models, accompanied with straight, fast, and intuitive explanations out of the box. This article aims to ﬁll the lack of compelling XAI guide by applying XAI techniques in their particular day-to-day models, datasets and use-cases. Figure 1 acts as a ﬂowchart/map for the reader and should help him to ﬁnd the ideal method to use according to his type of data. In each chapter, the reader will ﬁnd a description of the proposed method as well as an example of use on a Biomedical application and a Python notebook. It can be easily modiﬁed in order to be applied to speciﬁc applications.
G AMMA S AMPLING : F INE - GRAINED C ONTROLLING L ANGUAGE M ODELS WITHOUT T RAINING

The dominant approaches for controlling language models achieve prominence in controlling high-level attributes (e.g. topic and sentiment). However, these methods often require condition-specific data or are computationally expensive. We propose a new simple guided decoding method, Gamma Sampling, which does not require any training data to achieve fine-grained controllable text generation while maintaining a fast generation speed. Gamma Sampling introduces attributerelated information (provided by humans or language models themselves) into the sampling process to guide language models to generate texts with desired attributes. Since no training is involved, Gamma Sampling can be easily applied to any language model for controllable text generation. Through experiments, we show that Gamma Sampling-steered GPT2-small (117M) outperforms baselines such as PPLM (345M) and CTRL (1.6B) in diversity, attribute relevance, and overall quality of generated samples.
GROWTH on S190426c II: GROWTH-India Telescope search for an optical counterpart with a custom image reduction and candidate vetting pipeline

S190426c / GW190426_152155 was the first probable neutron star – black hole merger candidate detected by the LIGO-Virgo Collaboration. We undertook a tiled search for optical counterparts of this event using the 0.7m GROWTH-India Telescope. Over a period of two weeks, we obtained multiple observations over a 22.1 deg2 area, with a 17.5 per cent probability of containing the source location. Initial efforts included obtaining photometry of sources reported by various groups, and a visual search for sources in all galaxies contained in the region. Subsequently, we have developed an image subtraction and candidate vetting pipeline with $\sim 94{{\%}}$ efficiency for transient detection. Processing the data with this pipeline, we find several transients, but none that are compatible with kilonova models. We present the details of our observations, working of our pipeline, results from the search, and our interpretations of the non-detections that will work as a pathfinder during the O4 run of LVK.
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.
Spatial-Temporal Transformer for Video Snapshot Compressive Imaging

Video snapshot compressive imaging (SCI) captures multiple sequential video frames by a single measurement using the idea of computational imaging. The underlying principle is to modulate high-speed frames through different masks and these modulated frames are summed to a single measurement captured by a low-speed 2D sensor (dubbed optical encoder); following this, algorithms are employed to reconstruct the desired high-speed frames (dubbed software decoder) if needed. In this article, we consider the reconstruction algorithm in video SCI, i.e., recovering a series of video frames from a compressed measurement. Specifically, we propose a Spatial-Temporal transFormer (STFormer) to exploit the correlation in both spatial and temporal domains. STFormer network is composed of a token generation block, a video reconstruction block, and these two blocks are connected by a series of STFormer blocks. Each STFormer block consists of a spatial self-attention branch, a temporal self-attention branch and the outputs of these two branches are integrated by a fusion network. Extensive results on both simulated and real data demonstrate the state-of-the-art performance of STFormer. The code and models are publicly available at https://github.com/ucaswangls/STFormer.
REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect

Blind image quality assessment (BIQA) of user generated content (UGC) suffers from the range effect which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated; focusing on a particular range, the correlation is lower. The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Firstly, we design a rank-and-gradient loss for coarse-grained metric. The loss keeps the order and grad consistency between pMOS and MOS, thereby reducing the predicted deviation in a wide range. Secondly, we propose multi-level tolerance loss to make fine-grained prediction. The loss is constrained by a decreasing threshold to limite the predicted deviation in narrower and narrower ranges. Finally, we design a feedback network to conduct the coarse-to-fine assessment. On the one hand, the network adopts feedback blocks to process multi-scale distortion features iteratively and on the other hand, it fuses non-local context feature to the output of each iteration to acquire more quality-aware feature representation. Experimental results demonstrate that the proposed method can alleviate the range effect compared to the state-of-the-art methods effectively.
"Dummy Grandpa, do you know anything?": Identifying and Characterizing Ad hominem Fallacy Usage in the Wild

Today, participating in discussions on online forums is extremely commonplace and these discussions have started rendering a strong influence on the overall opinion of online users. Naturally, twisting the flow of the argument can have a strong impact on the minds of naive users, which in the long run might have socio-political ramifications, for example, winning an election or spreading targeted misinformation. Thus, these platforms are potentially highly vulnerable to malicious players who might act individually or as a cohort to breed fallacious arguments with a motive to sway public opinion. Ad hominem arguments are one of the most effective forms of such fallacies. Although a simple fallacy, it is effective enough to sway public debates in offline world and can be used as a precursor to shutting down the voice of opposition by slander. In this work, we take a first step in shedding light on the usage of ad hominem fallacies in the wild. First, we build a powerful ad hominem detector based on transformer architecture with high accuracy (F1 more than 83%, showing a significant improvement over prior work), even for datasets for which annotated instances constitute a very small fraction. We then used our detector on 265k arguments collected from the online debate forum – CreateDebate. Our crowdsourced surveys validate our in-the-wild predictions on CreateDebate data (94% match with manual annotation). Our analysis revealed that a surprising 31.23% of CreateDebate content contains ad hominem fallacy, and a cohort of highly active users post significantly more ad hominem to suppress opposing views. Then, our temporal analysis revealed that ad hominem argument usage increased significantly since the 2016 US Presidential election, not only for topics like Politics, but also for Science and Law. We conclude by discussing important implications of our work to detect and defend against ad hominem fallacies.
The proper forcing axiom for $\aleph_1$-sized posets, P\v{r}\'{i}kr\'{y}-type proper forcing, and the size of the continuum

We show that the Proper Forcing Axiom for forcing notions of size $\aleph_1$ is consistent with the continuum being arbitrarily large. In fact, assuming $GCH$ holds and $\kappa\geq\omega_2$ is a regular cardinal, we prove that there is a proper and $\aleph_2$-c.c. \ forcing $\mathbb{P}$ giving rise to a model of this forcing axiom together with $2^{\aleph_0}=\kappa$ and which, in addition, satisfies all statements of the form $\mathcal{H}(\aleph_2)\models \forall x\exists y\varphi(x, y)$, where $\varphi(x, y)$ is a restricted formula with the property that for every $a\in \mathcal{H}(\aleph_2)$ and every inner model $M$ of $CH$ with $a\in M$ there is, in $M$, a suitably nice proper poset adding some $b$ such that $\varphi(a, b)$. In particular, $\mathbb{P}$ forces Moore's Measuring principle, Baumgartner's Axiom for $\aleph_1$-dense sets of reals, Todor\v{c}evi\'{c}'s Open Colouring Axiom for sets of size $\aleph_1$, and Todor\v{e}vi\'{c}'s P-ideal Dichotomy for $\aleph_1$-generated ideals on $\omega_1$, among other statements. In particular, all these statements are simultaneously compatible with a large continuum.
Physical constraints on visual anemometry using vegetation displacement statistics

Visual anemometry (VA) leverages observations of fluid-structure interactions to infer incident flow characteristics. Recent work has demonstrated the concept of VA using both data-driven and physical modelling approaches applied to natural vegetation. These methods have not yet achieved generalization across plant species and require site-specific calibration. We conducted a laboratory study in an open circuit wind tunnel using overhead imagery of three vegetation species to assess the utility of vegetation displacement fields for wind speed inference. Both the wind and vegetation speeds exhibited a two-parameter Weibull distribution. The relationship between the scale factor (one of the two parameters) of the wind and vegetation was found to be well described by a sigmoid function, indicating three regions of distinct structural response to the wind loading at low, intermediate, and high wind speeds. Within an intermediate range of wind speeds, the wind and vegetation scale factors are nearly linearly proportional, thereby facilitating VA. Importantly, the wind and vegetation scale factors were found to be uncorrelated in low and high wind regimes, revealing a fundamental constraint on VA using structure response data. We discuss the physical basis for these regimes and present additional parametric relationships that can be exploited in the intermediate wind regime to potentially generalize inference of the wind speed and direction.
Instrumental variable quantile regression under random right censoring

This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile regression coefficients are a solution. Identification is obtained when this system of equations has a unique solution. Our estimation procedure solves an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove the validity of a bootstrap procedure for inference. The finite sample performance of the approach is evaluated through numerical simulations. An application to the national Job Training Partnership Act study illustrates the method.
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure the consistency between representations derived from different graph augmentations of the user-item bipartite graph. This self-supervised approach allows for the extraction of general features from raw data, thereby mitigating the issue of data sparsity. Despite the effectiveness of this paradigm, the factors contributing to its performance gains have yet to be fully understood. This paper provides novel insights into the impact of CL on recommendation. Our findings indicate that CL enables the model to learn more evenly distributed user and item representations, which alleviates the prevalent popularity bias and promoting long-tail items. Our analysis also suggests that the graph augmentations, previously considered essential, are relatively unreliable and of limited significance in CL-based recommendation. Based on these findings, we put forward an eXtremely Simple Graph Contrastive Learning method (XSimGCL) for recommendation, which discards the ineffective graph augmentations and instead employs a simple yet effective noise-based embedding augmentation to generate views for CL. A comprehensive experimental study on four large and highly sparse benchmark datasets demonstrates that, though the proposed method is extremely simple, it can smoothly adjust the uniformity of learned representations and outperforms its graph augmentation-based counterparts by a large margin in both recommendation accuracy and training efficiency.
Jet–ISM Interaction in NGC 1167/B2 0258+35, an LINER with an AGN Past

We report the results of joint Chandra/ACIS—NuSTAR deep observations of NGC 1167, the host galaxy of the young radio jet B2 0258+35. In the ACIS data, we detect X-ray emission, extended both along and orthogonal to the jet. At the end of the southeast radio jet, we find lower-energy X-ray emission that coincides with a region of CO turbulence and fast outflow motions. This suggests that the hot interstellar medium (ISM) may be compressed by the jet and molecular outflow, resulting in more efficient cooling. Hydrodynamic simulations of jet–ISM interaction tailored to NGC 1167 are in agreement with this conclusion and with the overall morphology and spectra of the X-ray emission. The faint hard nuclear source detected with Chandra and the stringent NuSTAR upper limits on the harder X-ray emission show that the active galactic nucleus (AGN) in NGC 1167 is in a very low-accretion state. However, the characteristics of the extended X-ray emission are more consonant to those of luminous Compton-thick (CT) AGNs, suggesting that we may be observing the remnants of a past high accretion rate episode, with sustained strong activity lasting ∼2 × 103 yr. We conclude that NGC1167 is presently a Low-Ionization Nuclear Emission-line Region (LINER) , but was an AGN in the past, given the properties of the extended X-ray emission and their similarity with those of CT AGN extended emission.