Miracle-dz/UniFAR-FTUs huggingface dataset:
Controllable Universal Fair Representation Learning

Learning fair and transferable representations of users that can be used for a wide spectrum of downstream tasks (specifically, machine learning models) has great potential in fairness-aware Web services. Existing studies focus on debiasing w.r. t. a small scale of (one or a handful of) fixed pre-defined sensitive attributes. However, in real practice, downstream data users can be interested in various protected groups and these are usually not known as prior. This requires the learned representations to be fair w.r. t. all possible sensitive attributes. We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. An effective bound is first derived via the lens of mutual information to guarantee parity of the universal set of sensitive attributes while maintaining the accuracy of downstream tasks. We also theoretically establish that the number of sensitive attributes that need to be processed can be reduced from exponential to linear. Experiments on two public real-world datasets demonstrate CUFRL can achieve significantly better accuracy-fairness trade-off compared with baseline approaches.
Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem

The Diameter-bounded max-Coverage Group Steiner Tree (DCGST) problem has recently been proposed as an expressive way of formulating keyword-based search and exploration of knowledge graphs. It aims at finding a diameter-bounded tree which covers the most given groups of vertices and has the minimum weight. In contrast to its specialization—the classic Group Steiner Tree (GST) problem which has been extensively studied, the emerging DCGST problem still lacks an efficient algorithm. In this paper, we propose Cba, the first approximation algorithm for the DCGST problem, and we prove its worst-case approximation ratio. Furthermore, we incorporate a best-first search strategy with two pruning methods into PrunedCBA, an improved approximation algorithm. Our extensive experiments on real and synthetic graphs demonstrate the effectiveness and efficiency of PrunedCBA.
A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring the Experience of Boundedly Rational Users

Previous researches demonstrate that users’ actions in search interaction are associated with relative gains and losses to reference points, known as the reference dependence effect. However, this widely confirmed effect is not represented in most user models underpinning existing search evaluation metrics. In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. To test the overall effectiveness of the proposed framework, (1) we evaluate the performance, in terms of correlation with user satisfaction, of ReDeMs built upon different reference points against that of the widely-used metrics on three search datasets; (2) we examine the performance of ReDeMs under different task states, like task difficulty and task urgency; and (3) we analyze the statistical reliability of ReDeMs in terms of discriminative power. Experimental results indicate that: (1) ReDeMs integrated with a proper reference point achieve better correlations with user satisfaction than most of the existing metrics, like Discounted Cumulative Gain (DCG) and Rank-Biased Precision (RBP), even though their parameters have already been well-tuned; (2) ReDeMs reach relatively better performance compared to existing metrics when the task triggers a high-level cognitive load; (3) the discriminative power of ReDeMs is far stronger than Expected Reciprocal Rank (ERR), slightly stronger than Precision and similar to DCG, RBP and INST. To our knowledge, this study is the first to explicitly incorporate the reference dependence effect into the user browsing model and offline evaluation metrics. Our work illustrates a promising approach to leveraging the insights about user biases from cognitive psychology in better evaluating user search experience and enhancing user models.
Stochastic ordering of random processes with an imbedded point process

We introduce multivariate partial orderings related with the Palm and time-stationary probabilities of a point process. Using these orderings, we give conditions for the monotonicity of a random sequence, with respect to some integral stochastic ordering, to be inherited with a continuous time process in which this sequence is imbedded. This type of inheritance is also discussed for the property of association.
HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale

Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation. In this work, we investigate workers’ rating behavior and compare it with experts. First, we instrument a crowdsourcing study where we asked workers to evaluate design ideas from three innovation challenges. We show that workers share similar insights with experts but tend to rate more generously and weigh certain criteria more importantly. Next, we develop a hybrid human-AI approach that combines a machine learning model with crowdsourcing to evaluate ideas. Our approach models workers’ reliability and bias while leveraging ideas’ textual content to train a machine learning model. It is able to incorporate experts’ ratings whenever available, to supervise the model training and infer worker performance. Results show that our framework outperforms baseline methods and requires significantly less training data from experts, thus providing a viable solution for evaluating ideas at scale.
AI tools for debunking online spam reviews? Trust of younger and older adults in AI detection criteria

ABSTRACT In order to make AI tools for spam review detection to be more trustworthy, two types of explanation were designed, the Text-AI tool and the Behaviour-AI tool. The Text-AI tool establishes detection criteria based on the textual features of reviews, while the Behaviour-AI tool establishes detection criteria based on the behavioural features of reviewers. The trust of younger (20–26 years) and older (50–78 years) adults in AI tools and the changes in credibility judgments for reviews and overall attitude toward the product made by them based on AI tools’ detection results were measured. We mainly found that: (i) Almost all the older participants reported that they trust AI tools’ prediction, but 48.7% of them would abandon AI when AI’s prediction was different from their own judgment. (ii) Younger adults showed a higher trust in the Behaviour-AI tool than in the Text-AI tool, especially when the AI tool detected more spam reviews than themselves. (iii) Regardless of age and types of explanation, participants perceived AI tools as more competent and benevolent when AI tools outperformed participants by detecting unexpected spam reviews.
Trade and Climate Mitigation Interactions Create Agro‐Economic Opportunities With Social and Environmental Trade‐Offs in Latin America and the Caribbean

The Latin America and the Caribbean (LAC) region plays key roles in both meeting global agricultural demands and maintaining carbon sinks due to its abundant land and water resources. In this study we use the Global Change Analysis Model to evaluate the opportunities and challenges posed by two global‐scale drivers: agricultural market integration (i.e., reduction of trade barriers) and land‐based climate mitigation policy. We evaluate their potential individual and combined impacts on agricultural production and trade revenues across LAC's economies through mid‐century, as well as the resulting impacts on agricultural consumers and integrated land‐water‐climate systems across LAC's diverse sub‐regions. Increased global market integration results in increased agricultural production and trade revenues for many LAC economies, driven by their evolving comparative advantages. Climate mitigation measures on CO2 and non‐CO2 greenhouse gases increase revenues due to increased agricultural prices from land competition and emissions abatement. The combined outcomes from both drivers are complex and sometimes non‐linear, highlighting the importance of understanding the interactions between multiple drivers. Our results show that increased agricultural production and trade opportunities, from either of the two drivers, pose significant trade‐offs that require careful multi‐sectoral planning, such as emissions reduction challenges, potential loss of livestock production when pursuing land‐based climate mitigation strategies, increased consumer expenditures, and changes in land‐use or water withdrawals, resulting in deforestation or water scarcity pressures. There is considerable heterogeneity in economic and environmental outcomes across LAC sub‐regions and agricultural commodities, illustrating the value of considering outcomes at finer scales.
Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition

Human face recognition is highly accurate, and exhibits a number of distinctive and well documented behavioral “signatures” such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is “special”. But why does human face perception exhibit these properties in the first place? Here we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As for face perception, the car-trained network showed a drop in performance for inverted versus upright cars. Similarly, CNNs trained only on inverted faces produce an inverted inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so “special” after all. Significance Statement For decades, cognitive scientists have collected and characterized behavioral signatures of face recognition. Here we move beyond the mere curation of behavioral phenomena to asking why the human face system works the way it does. We find that many classic signatures of human face perception emerge spontaneously in CNNs trained on face discrimination, but not in CNNs trained on object classification (or on both object classification and face detection), suggesting that these long-documented properties of the human face perception system reflect optimizations for face recognition per se, not by-products of a generic visual categorization system. This work further illustrates how CNN models can be synergistically linked to classic behavioral findings in vision research, thereby providing psychological insights into human perception.
Response to photic stimulation as a measure of cortical excitability in epilepsy patients

Studying states and state transitions in the brain is challenging due to nonlinear, complex dynamics. In this research, we analyse the brain’s response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics, though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition. Therefore, biomarkers of the brain’s perturbation response or ‘cortical excitability’ could be used to indicate seizure transitions. However, perturbing the brain often involves invasive intracranial surgeries or expensive equipment such as transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups, or animal model studies. Photic stimulation is a widely used diagnostic technique in epilepsy that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalised epilepsy (GGE), with (n = 10) and without (n = 10) PPR, and patients with psychogenic non-epileptic seizures (PNES; n = 10), compared to resting controls (n = 10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiologic mechanisms. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.
Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials

Preventing parasite transmission from humans to mosquitoes is recognised to be critical for achieving elimination and eradication of malaria. Consequently developing new antimalarial drugs with transmission-blocking properties is a priority. Large screening campaigns have identified many new transmission-blocking molecules, however little is known about how they target the mosquito-transmissible Plasmodium falciparum stage V gametocytes, or how they affect their underlying cell biology. To respond to this knowledge gap, we have developed a machine learning image analysis pipeline to characterise and compare the cellular phenotypes generated by transmission-blocking molecules during male gametogenesis. Using this approach, we studied 40 molecules, categorising their activity based upon timing of action and visual effects on the organisation of tubulin and DNA within the cell. Our data both proposes new modes of action and corroborates existing modes of action of identified transmission-blocking molecules. Furthermore, the characterised molecules provide a new armoury of tool compounds to probe gametocyte cell biology and the generated imaging dataset provides a new reference for researchers to correlate molecular target or gene deletion to specific cellular phenotype. Our analysis pipeline is not optimised for a specific organism and could be applied to any fluorescence microscopy dataset containing cells delineated by bounding boxes, and so is extendible to any disease model.
Stacked regressions and structured variance partitioning for interpretable brain maps

Relating brain activity associated with a complex stimulus to different properties of that stimulus is a powerful approach for constructing functional brain maps. However, when stimuli are naturalistic, their properties are often correlated (e.g., visual and semantic features of natural images, or different layers of a convolutional neural network that are used as features of images). Correlated properties can act as confounders for each other and complicate the interpretability of brain maps, and can impact the robustness of statistical estimators. Here, we present an approach for brain mapping based on two proposed methods: stacking different encoding models and structured variance partitioning. Our stacking algorithm combines encoding models that each use as input a feature space that describes a different stimulus attribute. The algorithm learns to predict the activity of a voxel as a linear combination of the outputs of different encoding models. We show that the resulting combined model can predict held-out brain activity better or at least as well as the individual encoding models. Further, the weights of the linear combination are readily interpretable; they show the importance of each feature space for predicting a voxel. We then build on our stacking models to introduce structured variance partitioning, a new type of variance partitioning that takes into account the known relationships between features. Our approach constrains the size of the hypothesis space and allows us to ask targeted questions about the similarity between feature spaces and brain regions even in the presence of correlations between the feature spaces. We validate our approach in simulation, showcase its brain mapping potential on fMRI data, and release a Python package. Our methods can be useful for researchers interested in aligning brain activity with different layers of a neural network, or with other types of correlated feature spaces.
Gibberellin transport affects (lateral) root growth through HY5 during Far-Red light enrichment

Plants compete for light by growing taller than their nearest competitors. This is part of the shade avoidance syndrome and is a response to an increase of Far-Red light (FR) reflected from neighboring leaves. The root responds to this shoot-sensed FR cue by reducing lateral root emergence. It is well-established that the plant hormone Gibberellic Acid (GA) is involved in supplemental FR-induced shoot elongation. Although GA is also transported from shoot to root, its role in regulating lateral root growth is unclear. We show via GA manipulations, both chemical and genetic, that GA modulates the lateral root reduction induced by shoot-sensed FR enrichment. Using the FRET-based GA biosensor GPS1, we observed detailed GA changes in the root upon shoot exposure to FR enrichment and when GA was supplied to the shoot. Supplying GA to the shoot also mitigated the FR-enrichment root phenotype, indicating a functional link between GA and changes in root development in response to shoot-sensed FR. The regulatory role of GA in root growth appears to be partially dependent upon the role of ELONGATED HYPOCOTYL 5 (HY5), a light-responsive transcription factor that regulates root growth. Shoot-to-root transported GA4 led to an increase in HY5 protein levels in the lateral root primordia. HY5 then repressed auxin signaling to repress lateral root growth. Our data unveil a novel way in which hormone and light signaling coordinate development across spatial scales by adjusting (lateral) root growth from above-ground FR light signals.
CDK7 is a Novel Therapeutic Vulnerability in Fibrolamellar Carcinoma

Fibrolamellar carcinoma (FLC) is a rare and lethal cancer that afflicts young individuals. The tumor arises in the background of a healthy liver, and patients typically present with advanced cancer at the time of diagnosis. Unfortunately, for these patients with advanced or recurrent cancer, no proven systemic therapies exist resulting in only 30-45% of patients surviving to 5 years. Investigations into the molecular underpinning of FLC have revealed a unique gene fusion between heat shock protein 40 (DNAJB1) and the catalytic subunit alpha of protein kinase A (PRKACA), leading to the formation of an oncoprotein (DNAJ-PKAc) that retains kinase activity and is a proven tumor-causing event in FLC. To uncover potential therapeutic targets, we engineered an FLC cell line by introducing the DNAJB1-PRKACA oncogene rearrangement into human hepatocellular cells using CRISPR/Cas9. We identified aberrant cell cycle progression, and follow-up molecular analysis revealed evidence of enhanced cyclin dependent kinase 7 (CDK7) activation in the DNAJB1-PRKACA expressing FLC cells. These findings were confirmed in human samples of FLC. In turn, targeting CDK7 with selective inhibitors demonstrated efficacy in several patient-derived models of FLC, with minimal toxicity to normal liver. Collectively, this work uncovers a novel candidate therapeutic vulnerability in FLC.
Mena and VASP are required for vascular smooth muscle relaxation

Enabled/vasodilator stimulated phosphoproteins (Ena/VASP) proteins are important regulators of the cytoskeleton, linking kinase signaling pathways to actin assembly. In mammals, the Ena/VASP family of proteins consists of mammalian enabled (Mena), VASP, and Ena-VASP-like protein (EVL). The proteins are well known targets of cAMP- and cGMP-dependent protein kinases, PKA and PKG, respectively. Given the importance of cyclic nucleotide signaling in mediating vasodilation, we investigated the role of Ena/VASP protein in vascular smooth muscle relaxation. Whereas VASP and Mena were strongly expressed in vascular smooth muscle cells, EVL was undetectable in the arterial wall and EVL-deficiency had no impact on agonist-induced smooth muscle relaxation. VASP deletion impaired the acetylcholine (ACh)- and nitric oxide (NO)-induced relaxation murine mesenteric arteries ex vivo. Similarly, the ACh-induced and NO-dependent relaxation of aorta from 7-month-old but not 3- month-old VASP-/- mice was also reduced. Aortas from animals lacking VASP and expressing only minimal amounts of Mena displayed significantly impaired relaxations in response to NO, cAMP and cGMP stimulation. These results suggest that Mena and VASP play an important role in agonist induced smooth muscle relaxation and functionally compensate for each other.