Miracle-dz/UniFAR-FTUs huggingface dataset:
On homogeneous closed gradient Laplacian solitons

We prove a structure theorem for homogeneous closed gradient Laplacian solitons and use it to show some examples of closed Laplacian solitons cannot be made gradient. More specifically, we show that the Laplacian solitons on nilpotent Lie groups found by Nicolini are not gradient up to homothetic $G_2$-structures except for $N_1$, where $f$ must be a Gaussian. We also show that the closed $G_2$-structure $\varphi_{12}$ on $N_{12}$ constructed by Fern\'andez-Fino-Manero cannot be a gradient soliton. We further show that closed non-torsion-free gradient Laplacian solitons on almost abelian solvmanifolds are isometric to products $N \times \mathbb R^k$ with $f$ constant on $N$.
Behavior Proximal Policy Optimization

Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs. Thus, various additional augmentations are proposed to keep the learned policy close to the offline dataset (or the behavior policy). In this work, starting from the analysis of offline monotonic policy improvement, we get a surprising finding that some online on-policy algorithms are naturally able to solve offline RL. Specifically, the inherent conservatism of these on-policy algorithms is exactly what the offline RL method needs to overcome the overestimation. Based on this, we propose Behavior Proximal Policy Optimization (BPPO), which solves offline RL without any extra constraint or regularization introduced compared to PPO. Extensive experiments on the D4RL benchmark indicate this extremely succinct method outperforms state-of-the-art offline RL algorithms. Our implementation is available at https://github.com/Dragon-Zhuang/BPPO.
Trip-based mobile sensor deployment for drive-by sensing with bus fleets

Drive-by sensing (i.e. vehicle-based mobile sensing) is an emerging data collection paradigm that leverages vehicle mobilities to scan a city at low costs. It represents a positive social externality of urban transport activities. Bus transit systems are widely considered in drive-by sensing due to extensive spatial coverage, reliable operations, and low maintenance costs. It is critical for the underlying monitoring scenario (e.g. air quality, traffic state, and road roughness) to assign a limited number of sensors to a bus fleet to ensure their optimal spatial-temporal distribution. In this paper we present a trip-based sensor deployment problem, which explicitly considers timetabled trips that must be executed by the fleet while a portion of them perform sensing tasks. To address the computational challenge in large-scale instances, we design a multi-stage solution framework that decouples the spatial-temporal structures of the sensing task through line pre-selection and bi-level optimization. As a result, the computational complexity is reduced to be sub-linear w.r. t. the number of lines, rather than combinatorial w.r. t. the number of buses in existing vehicle-based approaches. A real-world case study covering 400 km$^2$ in central Chengdu demonstrates the effectiveness of the model in solving large-scale problems. It is found that coordinating bus scheduling and sensing tasks can substantially increase the spatial-temporal sensing coverage. We also provide a few model extensions and recommendation for practice regarding the application of this method.
The Virtual Cactus Group and Littelmann Paths

We define a virtual cactus group and show that the cactus group action on Littelmann paths is compatible with the virtualization map defined by Pan-Scrimshaw. To show this we use the combinatorics of Dynkin diagram foldings. Our definition generalizes the group with the same name recently defined for the symplectic Lie algebra in recent joint work with Azenhas and Tarighat-Feller.
Eluder-based Regret for Stochastic Contextual MDPs

We present the E-UC$^3$RL algorithm for regret minimization in Stochastic Contextual Markov Decision Processes (CMDPs). The algorithm operates under the minimal assumptions of realizable function class and access to \emph{offline} least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient offline regression oracles) and enjoys a regret guarantee of $ \widetilde{O}(H^3 \sqrt{T |S| |A|d_{\mathrm{E}}(\mathcal{P}) \log (|\mathcal{F}| |\mathcal{P}|/ \delta) )}) , $ with $T$ being the number of episodes, $S$ the state space, $A$ the action space, $H$ the horizon, $\mathcal{P}$ and $\mathcal{F}$ are finite function classes used to approximate the context-dependent dynamics and rewards, respectively, and $d_{\mathrm{E}}(\mathcal{P})$ is the Eluder dimension of $\mathcal{P}$ w.r. t the Hellinger distance. To the best of our knowledge, our algorithm is the first efficient and rate-optimal regret minimization algorithm for CMDPs that operates under the general offline function approximation setting. In addition, we extend the Eluder dimension to general bounded metrics which may be of separate interest.
ON THE POSSIBLE EXISTENCE OF QUARK STARS

An important feature of the colored gluon theory of quark interactions (quantum chromodynamics) is that for low momentum transfers, i.e., large distances, the interaction between quarks becomes large. It is speculated that this “infrared slavery” is responsible for the permanent confinement of quarks.13 Conversely, a t high momentum transfers, i.e. a t short distances, the interaction between quarks becomes O n e interesting consequence of this asymptotic freedom for quarks is that a t high densities where the long-range forces between quarks are weakened, matter should behave like a gas of free quarks. * Of course, one knows experimentally that only baryons exist up to matter densities a s high a s those existing in a tomic nuclei. Thus, the transition to the state where free quarks exist must take place a t densities higher than those occurring in nuclei. The question of where the baryon-quark phase transition takes place is of considerable interest to astrophysics because of the possibility that this transition might take place inside neutron star^. ^,^ I t might be the case, for example, that the central regions o f t h e most massive neutron stars consist primarily of quarks. Another possibility is that free quarks d o not occur in the interiors of neutron stars but are the principal constituents of a new regime of stable cold stars.12 These questions have been previously investigated’. ’ using the M I T bag model theory of quark matter with the result that neither of the two possibilities just mentioned appeared likely to be realized in nature. I n this letter we would like to report on the results of a study of these questions based o n quantum chromodynamics (Q.C.D.). In order t o investigate the question of whether a star can exist with an interior consisting primarily of free quarks we need to have a theory for quark matter. I f we neglect quark masses (which is a good approximation except when one is near the strangeness or charm thresholds) then the energy density c of quark matter will be given by an expression of the form
Fusing Visual Appearance and Geometry for Multi-Modality 6DoF Object Tracking

In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learning-based methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available.
Towards Adversarial Evaluations for Inexact Machine Unlearning

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.
Dirichlet Mechanism for Differentially Private KL Divergence Minimization

Given an empirical distribution $f(x)$ of sensitive data $x$, we consider the task of minimizing $F(y) = D_{\text{KL}} (f(x)\Vert y)$ over a probability simplex, while protecting the privacy of $x$. We observe that, if we take the exponential mechanism and use the KL divergence as the loss function, then the resulting algorithm is the Dirichlet mechanism that outputs a single draw from a Dirichlet distribution. Motivated by this, we propose a R\'enyi differentially private (RDP) algorithm that employs the Dirichlet mechanism to solve the KL divergence minimization task. In addition, given $f(x)$ as above and $\hat{y}$ an output of the Dirichlet mechanism, we prove a probability tail bound on $D_{\text{KL}} (f(x)\Vert \hat{y})$, which is then used to derive a lower bound for the sample complexity of our RDP algorithm. Experiments on real-world datasets demonstrate advantages of our algorithm over Gaussian and Laplace mechanisms in supervised classification and maximum likelihood estimation.
Attachment-line, cross-flow and Tollmien–Schlichting instabilities on swept ONERA-D and Joukowski airfoils

Abstract Linear stability analyses are performed to investigate the boundary layer instabilities developing in an incompressible flow around the whole leading-edge of swept ONERA-D and Joukowski airfoils of infinite span. The stability analyses conducted in our study are global in the chordwise direction and local in the spanwise direction. A neutral curve is drawn at a given leading-edge Reynolds number $Re_R$ and several overlapping regions, called ‘lobes’, are identified on a physical basis. A detailed study of the marginal modes reveals the presence of attachment-line and cross-flow instabilities, as well as modes whose features do not fall within the standards of a specific type. Connected cross-flow/Tollmien–Schlichting modes, that show a dominant spatial structure reminiscent of Tollmien–Schlichting waves but whose destabilization is linked to a cross-flow mechanism, have been identified. The comparison of several neutral curves at different $Re_R$ values reveals the greater stabilizing effect of the increase of $Re_R$ on the cross-flow instability compared with the attachment-line instability. The influence of the airfoil shape is also studied by comparing the neutral curves of the ONERA-D with the neutral curves of the Joukowski airfoil. These curves reveal similar characteristics with the presence of distinct lobes and their comparison at constant sweep angle shows that, under the conditions studied, the ONERA-D airfoil is more stable than the Joukowski airfoil, even for cross-flow instabilities. The absolutely or convectively unstable nature of the flow in the spanwise direction is also tackled and our results suggest that the flow is only convectively unstable.
Unique pseudolifting property in digital topology

The generalized universal lifting property plays an important role in classical topology. In digital topology we also have its digital version (5, 6, 14). More precisely, the paper (6) established the concept of a digital covering (see also (11, 17)). It has substantially contributed to the calculation of digital fundamental groups of some digital spaces, the classification of digital spaces and so forth. The paper (6) also established the unique lifting property of a digital covering which plays an important role in studying both digital covering and digital homotopy theory. Motivated by the unique lifting property, the paper develops a pseudocovering which is weaker than a digital covering and investigates its various properties. Furthermore, the paper proves that a pseudocovering with some hypothesis has the unique pseudolifting property which is weaker than the unique lifting property in digital covering theory.
Complete Genome Sequences of Three Limnohabitans sp. (Lhab-A3) Strains, INBF002, TEGF004, and MORI2, Isolated from Two Lakes and a River in Japan

Freshwater bacterioplankton of the genus Limnohabitans represent a dominant group that has worldwide distribution. Here, we report the complete genome sequences of three Limnohabitans sp. (Lhab-A3 tribe) strains, i.e., INBF002, TEGF004, and MORI2, which were isolated from surface water samples from two shallow eutrophic lakes and a river in Japan. ABSTRACT Freshwater bacterioplankton of the genus Limnohabitans represent a dominant group that has worldwide distribution. Here, we report the complete genome sequences of three Limnohabitans sp. (Lhab-A3 tribe) strains, i.e., INBF002, TEGF004, and MORI2, which were isolated from surface water samples from two shallow eutrophic lakes and a river in Japan.
A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping

Users of online shopping platforms typically purchase multiple items at a time in the form of a shopping basket. Personalized within-basket recommendation is the task of recommending items to complete an incomplete basket during a shopping session. In contrast to the related task of session-based recommendation, where the goal is to complete an ongoing anonymous session, we have access to the shopping history of the user in within-basket recommendation. Previous studies have shown the superiority of neighborhood-based models for session-based recommendation and the importance of personal history in the grocery shopping domain. But their applicability in within-basket recommendation remains unexplored. We propose PerNIR, a neighborhood-based model that explicitly models the personal history of users for within-basket recommendation in grocery shopping. The main novelty of PerNIR is in modeling the short-term interests of users, which are represented by the current basket, as well as their long-term interest, which is reflected in their purchasing history. In addition to the personal history, user neighbors are used to capture the collaborative purchase behavior. We evaluate PerNIR on two public and proprietary datasets. The experimental results show that it outperforms 10 state-of-the-art competitors with a significant margin, i.e., with gains of more than 12% in terms of hit rate over the second best performing approach. Additionally, we showcase an optimized implementation of our method, which computes recommendations fast enough for real-world production scenarios.
Beyond-Accuracy Goals, Again

Improving the performance of information retrieval systems tends to be narrowly scoped. Often, better prediction performance is considered the only metric of improvement. As a result, work on improving information retrieval methods usually focuses on im- proving the methods' accuracy. Such a focus is myopic. Instead, as researchers and practitioners we should adopt a richer perspective measuring the performance of information retrieval systems. I am not the first to make this point (see, e.g., [4]), but I want to highlight dimensions that broaden the scope considered so far and offer a number of examples to illustrate what this would mean for our research agendas. First, trustworthiness is a prerequisite for people, organizations, and societies to use AI-based, and, especially, machine learning- based systems in general, and information retrieval systems in particular. Trust can be gained in an intrinsic manner by revealing the inner workings of an AI-based system, i.e., through explainability. Or it can be gained extrinsically by showing, in a principled or empirical manner, that a system upholds verifiable guarantees. Such guarantees should obtained for the following dimensions (at a minimum): (i) accuracy, including well-defined and explained contexts of usage; (ii) reliability, including exhibiting parity with respect to sensitive attributes; (iii) repeatable and reproducible results, including audit trails; (iv) resilience to adversarial examples, distributional shifts; and (v) safety, including privacy-preserving search and recommendation. Second, in information retrieval, our experiments are mostly conducted in controlled laboratory environments. Extrapolating this information to evaluate the real-world effects often remains a challenge. This is particularly true when measuring the impact of information retrieval systems across broader scales, both temporally and spatially. Conducting controlled experimental trials for evaluating real-world impacts of information retrieval systems can result in depicting a snapshot situation, where systems are tailored towards that specific environment. As society is constantly changing, the requirements set for information retrieval systems are changing as well, resulting in short-term and long-term feedback loops with interactions between society and information retrieval systems.
Learning and Maximizing Influence in Social Networks Under Capacity Constraints

Influence maximization (IM) refers to the problem of finding a subset of nodes in a network through which we could maximize our reach to other nodes in the network. This set is often called the "seed set", and its constituent nodes maximize the social diffusion process. IM has previously been studied in various settings, including under a time deadline, subject to constraints such as that of budget or coverage, and even subject to measures other than the centrality of nodes. The solution approach has generally been to prove that the objective function is submodular, or has a submodular proxy, and thus has a close greedy approximation. In this paper, we explore a variant of the IM problem where we wish to reach out to and maximize the probability of infection of a small subset of bounded capacity K. We show that this problem does not exhibit the same submodular guarantees as the original IM problem, for which we resort to the theory of gamma-weakly submodular functions. Subsequently, we develop a greedy algorithm that maximizes our objective despite the lack of submodularity. We also develop a suitable learning model that out-competes baselines on the task of predicting the top-K infected nodes, given a seed set as input.
DOCoR: Document-level OpenIE with Coreference Resolution

Open Information Extraction (OpenIE) extracts relational fact tuples in the form of from text. Most existing OpenIE solutions operate at sentence level and extract relational tuples solely from a sentence. However, many sentences exist as a part of paragraph or a document, where coreferencing is common. In this demonstration, we present a system which refines the semantic tuples generated by OpenIE with the aid of a coreference resolution tool. Specifically, all coreferential mentions across the entire document are identified and grouped into coreferential clusters. Objects and subjects in the extracted tuples from OpenIE which match any coreferential mentions are then resolved with a suitable representative term. In this way, our system is able to resolve both anaphoric and cataphoric references, to achieve Document-level OpenIE with Coreference Resolution (DOCoR). The demonstration video can be viewed at https://youtu.be/o9ZSWCBvlDs
MMBench: The Match Making Benchmark

Video gaming has gained huge popularity over the last few decades. As reported, there are about 2.9 billion gamers globally. Among all genres, competitive games are one of the most popular ones. Matchmaking is a core problem for competitive games, which determines the player satisfaction, hence influences the game success. Most matchmaking systems group the queuing players into opposing teams with similar skill levels. The key challenge is to accurately rate the players' skills based on their match performances. There has been an increasing amount of effort on developing such rating systems such as Elo, Glicko. However, games with different game-plays might have different game modes, which might require an extensive amount of effort for rating system customization. Even though there are many rating system choices and various customization strategies, there is a clear lack of a systematic framework with which different rating systems can be analysed and compared against each other. Such a framework could help game developers to identify the bottlenecks of their matchmaking systems and enhance the performance of their matchmaking systems. To bridge the gap, we present MMBench, the first benchmark framework for evaluating different rating systems. It serves as a fair means of comparison for different rating systems and enables a deeper understanding of different rating systems. In this paper, we will present how MMBench could benchmark the three major rating systems, Elo, Glicko, Trueskill in the battle modes of 1 vs 1, n vs n, battle royal and teamed battle royal over both real and synthetic datasets.
A Causal View for Item-level Effect of Recommendation on User Preference

Recommender systems not only serve users but also affect user preferences through personalized recommendations. Recent researches investigate the effects of the entire recommender system on user preferences, i.e., system-level effects, and find that recommendations may lead to problems such as echo chambers and filter bubbles. To properly alleviate the problems, it is necessary to estimate the effects of recommending a specific item on user preferences, i.e., item-level effects. For example, by understanding whether recommending an item aggravates echo chambers, we can better decide whether to recommend it or not. This work designs a method to estimate the item-level effects from the causal perspective. We resort to causal graphs to characterize the average treatment effect of recommending an item on the preference of another item. The key to estimating the effects lies in mitigating the confounding bias of time and user features without the costly randomized control trials. Towards the goal, we estimate the causal effects from historical observations through a method with stratification and matching to address the two confounders, respectively. Nevertheless, directly implementing stratification and matching is intractable, which requires high computational cost due to the large sample size. We thus propose efficient approximations of stratification and matching to reduce the computation complexity. Extensive experimental results on two real-world datasets validate the effectiveness and efficiency of our method. We also show a simple example of using the item-level effects to provide insights for mitigating echo chambers.