Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Submodular Maximization via New Bounds for DR-Submodular Functions

Submodular maximization under various constraints is a fundamental problem studied continuously, in both computer science and operations research, since the late $1970$'s. A central technique in this field is to approximately optimize the multilinear extension of the submodular objective, and then round the solution. The use of this technique requires a solver able to approximately maximize multilinear extensions. Following a long line of work, Buchbinder and Feldman (2019) described such a solver guaranteeing $0.385$-approximation for down-closed constraints, while Oveis Gharan and Vondr\'ak (2011) showed that no solver can guarantee better than $0.478$-approximation. In this paper, we present a solver guaranteeing $0.401$-approximation, which significantly reduces the gap between the best known solver and the inapproximability result. The design and analysis of our solver are based on a novel bound that we prove for DR-submodular functions. This bound improves over a previous bound due to Feldman et al. (2011) that is used by essentially all state-of-the-art results for constrained maximization of general submodular/DR-submodular functions. Hence, we believe that our new bound is likely to find many additional applications in related problems, and to be a key component for further improvement.
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing

Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex behaviors in a sample-efficient way: planning actions by generating imaginary trajectories with predicted rewards. Despite its success, we found that surprisingly, reward prediction is often a bottleneck of MBRL, especially for sparse rewards that are challenging (or even ambiguous) to predict. Motivated by the intuition that humans can learn from rough reward estimates, we propose a simple yet effective reward smoothing approach, DreamSmooth, which learns to predict a temporally-smoothed reward, instead of the exact reward at the given timestep. We empirically show that DreamSmooth achieves state-of-the-art performance on long-horizon sparse-reward tasks both in sample efficiency and final performance without losing performance on common benchmarks, such as Deepmind Control Suite and Atari benchmarks.
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach

The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, we notice important differences between the competition challenge and the challenges we face in a large e-commerce company. The datasets in our scenario are larger (hundreds of thousands of time series), and e-commerce can afford to have a larger assortment than brick-and-mortar retailers, leading to more intermittent data. To scale to larger dataset sizes with feasible computational effort, firstly, we investigate a two-layer hierarchy and propose a top-down approach to forecasting at an aggregated level with less amount of series and intermittency, and then disaggregating to obtain the decision-level forecasts. Probabilistic forecasts are generated under distributional assumptions. Secondly, direct training at the lower level with subsamples can also be an alternative way of scaling. Performance of modelling with subsets is evaluated with the main dataset. Apart from a proprietary dataset, the proposed scalable methods are evaluated using the Favorita dataset and the M5 dataset. We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets. Notably, our top-down forecasting framework enters the top 50 of the original M5 competition, even with models trained at a higher level under a much simpler setting.
Hybrid classical-quantum text search based on hashing

The paper considers the problem of finding a given substring in a text. It is known that the complexity of a classical search query in an unordered database is linear in the length of the text and a given substring. At the same time, Grover's quantum search provides a quadratic speedup in the complexity of the query and gives the correct result with a high probability. We propose a hybrid classical-quantum algorithm (hybrid random-quantum algorithm to be more precise), that implements Grover's search to find a given substring in a text. As expected, the algorithm works a) with a high probability of obtaining the correct result and b) with a quadratic query acceleration compared to the classical one. What's new is that our algorithm uses the uniform hash family functions technique. As a result, our algorithm is much more memory efficient (in terms of the number of qubits used) compared to previously known quantum algorithms.
Asymmetric Ramsey properties of randomly perturbed graphs

In this note, we investigate for various pairs of graphs $(H,G)$ the question of how many random edges must be added to a dense graph to guarantee that any red-blue coloring of the edges contains a red copy of $H$ or a blue copy of $G$. We determine this perturbed Ramsey threshold for many new pairs of graphs and various ranges of densities, obtaining several generalizations of results obtained by Das and Treglown. In particular, we resolve the remaining cases toward determining the perturbed Ramsey threshold for pairs $(K_t,K_s)$ where $t\geq s\geq 5$.
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL

Existing Text-to-SQL generators require the entire schema to be encoded with the user text. This is expensive or impractical for large databases with tens of thousands of columns. Standard dense retrieval techniques are inadequate for schema subsetting of a large structured database, where the correct semantics of retrieval demands that we rank sets of schema elements rather than individual elements. In response, we propose a two-stage process for effective coverage during retrieval. First, we instruct an LLM to hallucinate a minimal DB schema deemed adequate to answer the query. We use the hallucinated schema to retrieve a subset of the actual schema, by composing the results from multiple dense retrievals. Remarkably, hallucination $\unicode{x2013}$ generally considered a nuisance $\unicode{x2013}$ turns out to be actually useful as a bridging mechanism. Since no existing benchmarks exist for schema subsetting on large databases, we introduce three benchmarks. Two semi-synthetic datasets are derived from the union of schemas in two well-known datasets, SPIDER and BIRD, resulting in 4502 and 798 schema elements respectively. A real-life benchmark called SocialDB is sourced from an actual large data warehouse comprising 17844 schema elements. We show that our method1 leads to significantly higher recall than SOTA retrieval-based augmentation methods.
Crustal structure of Oaxaca, Mexico, from seismic refraction measurements

Seismic refraction and gravity data have been analyzed to obtain a model of the compressional-wave structure of the ocean-to-continent transition in the State of Oaxaca in southwestern Mexico. Crustal thickness on the continent at the latitude 18°N is 45 _ 4 km, based on reflected phases from the Moho discontinuity. The crust has been modeled with three layers, with velocities of 4.3 to 4.6, 5.0 to 5.7, and 6.85 to 7.0 km/sec, each with positive velocity gradient. The crust thins to 10 km at the coast near Pinotepa Nacional, where Precambrian metamorphic rocks are exposed 45 km from the mid-America trench. Offshore, the oceanic structure consists of an 8-km-thick crust with a normal crustal velocity structure (Spudich and Orcutt, 1980). The apparent dip of the subducting plate beneath western Mexico is 10 °, On the oceanic side, strong reflections suggest a minimum depth of 35 km for the lithosphere-asthenosphere boundary. The asthenosphere has a seismic velocity of 7.6 km/sec, and a thin lid in which the velocity is 8.6 km/sec.
Strongly Kreiss Bounded Operators in UMD Banach Spaces

In this paper we give growth estimates for $\|T^n\|$ for $n\to \infty$ in the case $T$ is a strongly Kreiss bounded operator on a UMD Banach space $X$. In several special cases we provide explicit growth rates. This includes known cases such as Hilbert and $L^p$-spaces, but also intermediate UMD spaces such as non-commutative $L^p$-spaces and variable Lebesgue spaces.
Theory of transient chimeras in finite Sakaguchi-Kuramoto networks

Chimera states are a phenomenon in which order and disorder can co-exist within a network that is fully homogeneous. Precisely how transient chimeras emerge in finite networks of Kuramoto oscillators with phase-lag remains unclear. Utilizing an operator-based framework to study nonlinear oscillator networks at finite scale, we reveal the spatiotemporal impact of the adjacency matrix eigenvectors on the Sakaguchi-Kuramoto dynamics. We identify a specific condition for the emergence of transient chimeras in these finite networks: the eigenvectors of the network adjacency matrix create a combination of a zero phase-offset mode and low spatial frequency waves traveling in opposite directions. This combination of eigenvectors leads directly to the coherent and incoherent clusters in the chimera. This approach provides two specific analytical predictions: (1) a precise formula predicting the combination of connectivity and phase-lag that creates transient chimeras, (2) a mathematical procedure for rewiring arbitrary networks to produce transient chimeras.
Semiparametric Latent ANOVA Model for Event-Related Potentials

Event-related potentials (ERPs) extracted from electroencephalography (EEG) data in response to stimuli are widely used in psychological and neuroscience experiments. A major goal is to link ERP characteristic components to subject-level covariates. Existing methods typically follow two-step approaches, first identifying ERP components using peak detection methods and then relating them to the covariates. This approach, however, can lead to loss of efficiency due to inaccurate estimates in the initial step, especially considering the low signal-to-noise ratio of EEG data. To address this challenge, we propose a semiparametric latent ANOVA model (SLAM) that unifies inference on ERP components and their association to covariates. SLAM models ERP waveforms via a structured Gaussian process prior that encodes ERP latency in its derivative and links the subject-level latencies to covariates using a latent ANOVA. This unified Bayesian framework provides estimation at both population- and subject- levels, improving the efficiency of the inference by leveraging information across subjects. We automate posterior inference and hyperparameter tuning using a Monte Carlo expectation-maximization algorithm. We demonstrate the advantages of SLAM over competing methods via simulations. Our method allows us to examine how factors or covariates affect the magnitude and/or latency of ERP components, which in turn reflect cognitive, psychological or neural processes. We exemplify this via an application to data from an ERP experiment on speech recognition, where we assess the effect of age on two components of interest. Our results verify the scientific findings that older people take a longer reaction time to respond to external stimuli because of the delay in perception and brain processes.
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%.
Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games

This paper presents new families of algorithms for the repeated play of two-agent (near) zero-sum games and two-agent zero-sum stochastic games. For example, the family includes fictitious play and its variants as members. Commonly, the algorithms in this family are all uncoupled, rational, and convergent even in heterogeneous cases, e.g., where the dynamics may differ in terms of learning rates, full, none or temporal access to opponent actions, and model-based vs model-free learning. The convergence of heterogeneous dynamics is of practical interest especially in competitive environments since agents may have no means or interests in following the same dynamic with the same parameters. We prove that any mixture of such asymmetries does not impact the algorithms' convergence to equilibrium (or near equilibrium if there is experimentation) in zero-sum games with repeated play and in zero-sum (irreducible) stochastic games with sufficiently small discount factors.
Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission

Currently, electronics and iontronics in abiotic-biotic systems can only use electrons and single-species ions as unitary signal carriers. Thus, a mechanism of gating transmission for multiple biosignals in such devices is needed to match and modulate complex aqueous-phase biological systems. Here we report the use of cascade-heterogated biphasic gel iontronics to achieve diverse electronic-to-multi-ionic signal transmission. The cascade-heterogated property determined the transfer free energy barriers experienced by ions and ionic hydration-dehydration states under an electric potential field, fundamentally enhancing the distinction of cross-interface transmission between different ions by several orders of magnitude. Such heterogated or chemical-heterogated iontronics with programmable features can be coupled with multi-ion cross-interface mobilities for hierarchical and selective cross-stage signal transmission. We expect that such iontronics would be ideal candidates for a variety of biotechnology applications. Description Editor’s summary Recognition and control of diverse bioionic signals, which would allow for the regulation of physiological processes in aqueous-phase biological media, remains one of the main challenges in the field of iontronics. State-of-the-art electronics and iontronics are still limited to electrons or single ions as signal carriers. Chen et al. report a heterogated biphasic gel iontronic device capable of biocompatible electronic-ionic signal processing and transmission, as demonstrated by regulating the cardiac electrical activity of bullfrog hearts. This approach is a promising step toward biocompatible signal processing and transmission in abiotic-biotic systems and has the potential to inspire interdisciplinary research at the intersection of electronics, ionics, chemistry, biology, and medicine. —Yury Suleymanov Gel-based iontronics offers diverse, electronic to multi-ionic signal processing compatible with biological environments.
Social health and subsequent cognitive functioning in people aged 50 years and above: examining the mediating roles of depressive symptoms and inflammatory biomarkers

Background Social health markers, including marital status, contact frequency, network size, and social support, have shown associations with cognition. However, the underlying mechanisms remain poorly understood. We investigated whether depressive symptoms and inflammation mediated associations between social health and subsequent cognition. Methods In the English Longitudinal Study of Ageing (ELSA; n=7,136; aged 50+), we used four-way decomposition to examine to what extent depressive symptoms, C-reactive protein (CRP) and fibrinogen (assessed at an intermediate time point) mediated associations between social health and subsequent standardised cognition (verbal fluency, delayed and immediate recall) including cognitive change, with slopes derived from multilevel models (ELSA: 12-year slope). We examined whether findings replicated in the Swedish National Study of Aging and Care in Kungsholmen (SNAC-K; n=2,846; aged 60+; 6-year slope). Findings We found indirect effects via depressive symptoms of network size, positive support and less negative support on subsequent verbal fluency, and positive support on subsequent immediate recall (pure indirect effect (PIE)=0.002 [0.000-0.003]). The positive support-verbal fluency mediation finding replicated in SNAC-K. Depressive symptoms partially mediated associations between less negative support and slower immediate (PIE=0.001 [0.000-0.002]) and delayed recall decline (PIE=0.001 [0.000-0.002]), and between positive support and slower immediate recall decline (PIE=0.001, [0.000-0.001]), which replicated in SNAC-K. We did not observe mediation by inflammatory biomarkers. Interpretation Findings provide new insights into mechanisms linking social health with cognition, suggesting that associations between cognition and interactional aspects of social health in particular, such as social support, are partly underpinned by depressive symptoms.
Impairment of hippocampal astrocyte-mediated striatal dopamine release and locomotion in Alzheimer’s disease

Clinical and translational research has identified deficits in the dopaminergic neurotransmission in the striatum in Alzheimer’s disease (AD) and this could be related to the pathophysiology of psychiatric symptoms appearing even at early stages of the pathology. We hypothesized that AD pathology in the hippocampus may influence dopaminergic neurotransmission even in the absence of AD-related lesion in the mesostriatal circuit. We thus chemogenetically manipulated the activity of hippocampal neurons and astrocytes in wild-type and hemizygous TgF344-AD (Tg) rats, an animal model of AD pathology. We assessed the brain-wide functional output of this manipulation using in vivo Single Photon Emission Computed Tomography to measure cerebral blood flow and D2/3 receptor binding. We also assessed the effects of the chemogenetic manipulations on astrocytic and microglial capacity to surround and phagocytize Aβ both locally and in the striatum. Our results show that acute and chronic neuronal and astrocytic stimulation induces widespread effects on the brain regional activation pattern, notably with an inhibition of striatal activation. In the TgF344-AD rats, both these effects were blunted. Chemogenetic stimulation in the hippocampus increased microglial density and its capacity to limit AD pathology, whereas these effects were absent in the striatum perhaps as a consequence of the altered connectivity between the hippocampus and the striatum. Our work suggests that hippocampal AD pathology may alter mesostriatal signalling and induce widespread alterations of brain activity. Neuronal and astrocytic activation may induce a protective, Aβ-limiting phenotype of microglia, which surrounds Aβ plaques and limits Αβ concentration more efficiently.
Evidence for circulation of Rift Valley fever virus in wildlife and domestic animals in a forest environment in Gabon, Central Africa

Rift Valley fever (RVF) is a mosquito-borne viral zoonosis caused by the RVF virus (RVFV) that can infect domestic and wild animals. Although the RVFV transmission cycle has been well documented across Africa in savanna ecosystems, little is known about its transmission in tropical rainforest settings, particularly in Central Africa. We therefore conducted a survey in northeastern Gabon to assess RVFV circulation among wild and domestic animals. Among 163 wildlife samples tested using RVFV-specific RT-qPCR, four ruminants belonging to subfamily Cephalophinae were detected positive. The phylogenetic analysis revealed that the four RVFV sequences clustered together with a virus isolated in Namibia within the well-structured Egyptian clade. A cross-sectional survey conducted on sheep, goats and dogs living in villages within the same area determined the IgG RVFV-specific antibody prevalence using cELISA. Out of the 306 small ruminants tested (214 goats, 92 sheep), an overall antibody prevalence of 15.4% (95% CI [11.5–19.9]) was observed with a higher rate in goats than in sheep (20.1% versus 3.3%). RVFV-specific antibodies were detected in a single dog out 26 tested. Neither age, sex of domestic animals nor season was found to be significant risk factors of RVFV occurrence. Our findings highlight sylvatic circulation of RVFV for the first time in Gabon. These results stress the need to develop adequate surveillance plan measures to better control the public health threat of RVFV. Author summary Rift Valley fever (RVF) is a mosquito-borne viral zoonosis caused by the RVF virus (RVFV) that can affect wild and domestic animals. Although the RVFV transmission cycle has been well documented across Africa in savanna ecosystems, little is known about its transmission in tropical rainforests, especially in Central Africa. We thus conducted a survey in northeastern Gabon to assess RVFV circulation among wild and domestic animals. In this study, we demonstrated for the first time in Gabon the presence of the RVFV in two wildlife species (Peter’s duiker Cephalophus callipygus and the blue duiker Philantomba monticola). In addition, we detected RVFV-specific antibodies in small domestic ruminants (sheep and goats) with an overall antibody prevalence of 15.4%, with a much higher seroprevalence rate in goats than sheep (20.1% versus 3.3%). Furthermore, RVFV-specific antibodies were also observed in a single (hunting) dog out of the 26 tested. These results stress the need to develop adequate surveillance plan measures to better control the public health threat of RVFV.