Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Access for Conjunctive Queries with Negation

Given a conjunctive query $Q$ and a database $\mathbf{D}$, a direct access to the answers of $Q$ over $\mathbf{D}$ is the operation of returning, given an index $j$, the $j^{\mathsf{th}}$ answer for some order on its answers. While this problem is $\#\mathsf{P}$-hard in general with respect to combined complexity, many conjunctive queries have an underlying structure that allows for a direct access to their answers for some lexicographical ordering that takes polylogarithmic time in the size of the database after a polynomial time precomputation. Previous work has precisely characterised the tractable classes and given fine-grained lower bounds on the precomputation time needed depending on the structure of the query. In this paper, we generalise these tractability results to the case of signed conjunctive queries, that is, conjunctive queries that may contain negative atoms. Our technique is based on a class of circuits that can represent relational data. We first show that this class supports tractable direct access after a polynomial time preprocessing. We then give bounds on the size of the circuit needed to represent the answer set of signed conjunctive queries depending on their structure. Both results combined together allow us to prove the tractability of direct access for a large class of conjunctive queries. On the one hand, we recover the known tractable classes from the literature in the case of positive conjunctive queries. On the other hand, we generalise and unify known tractability results about negative conjunctive queries -- that is, queries having only negated atoms. In particular, we show that the class of $\beta$-acyclic negative conjunctive queries and the class of bounded nest set width negative conjunctive queries admit tractable direct access.
Probing nuclear physics with supernova gravitational waves and machine learning

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 per cent for a set of four distinct EOS models.
Testing probabilistic adaptive real‐time flood forecasting models

Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data‐based mechanistic (DBM) models within the operational UK National Flood Forecasting System. The position of DBM models in the forecasting chain is considered along with their offline calibration and validation. The online adaptive implementation with assimilation of water level information as used for forecasting is outlined. Two example applications based upon UK locations where severe flooding has occurred, the River Eden at Carlisle and River Severn at Shrewsbury, are presented.
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning

The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution. Our key insight is that these inducing inputs can be replaced by the actual data, such that the variational distribution consists of a set of approximate likelihoods for each datapoint. This structure lends itself to amortised inference, in which the parameters of each approximate likelihood are obtained by passing each datapoint through a meta-model known as the inference network. By training this inference network across related datasets, we can meta-learn Bayesian inference over task-specific BNNs.
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA

Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed node towards the answer nodes. However, these messages are past-oriented, and they do not consider the full KG context. To make matters worse, KG nodes often represent proper noun entities and are sometimes encrypted, being uninformative in selecting between paths. To address these problems, we propose Neural Tree Search (NuTrea), a tree search-based GNN model that incorporates the broader KG context. Our model adopts a message-passing scheme that probes the unreached subtree regions to boost the past-oriented embeddings. In addition, we introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node embedding that considers the global KG context to better characterize ambiguous KG nodes. The general effectiveness of our approach is demonstrated through experiments on three major multi-hop KGQA benchmark datasets, and our extensive analyses further validate its expressiveness and robustness. Overall, NuTrea provides a powerful means to query the KG with complex natural language questions. Code is available at https://github.com/mlvlab/NuTrea.
DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This paper proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analysis and ablation studies are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of deploying DACOOP-A in physical systems.
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

While deep learning models have achieved remarkable success across a range of medical image analysis tasks, deployment of these models in real clinical contexts requires that they be robust to variability in the acquired images. While many methods apply predefined transformations to augment the training data to enhance test-time robustness, these transformations may not ensure the model's robustness to the diverse variability seen in patient images. In this paper, we introduce a novel three-stage approach based on transformers coupled with conditional diffusion models, with the goal of improving model robustness to the kinds of imaging variability commonly encountered in practice without the need for pre-determined data augmentation strategies. To this end, multiple image encoders first learn hierarchical feature representations to build discriminative latent spaces. Next, a reverse diffusion process, guided by the latent code, acts on an informative prior and proposes prediction candidates in a generative manner. Finally, several prediction candidates are aggregated in a bi-level aggregation protocol to produce the final output. Through extensive experiments on medical imaging benchmark datasets, we show that our method improves upon state-of-the-art methods in terms of robustness and confidence calibration. Additionally, we introduce a strategy to quantify the prediction uncertainty at the instance level, increasing their trustworthiness to clinicians using them in clinical practice.
Economic value of weather and climate forecasts

Preface 1. Weather prediction Joseph J. Tribbia 2. Forecast verification Allan H. Murphy 3. The value of weather information Stanley R. Johnson and Matthew T. Holt 4. Forecast value: prescriptive decision studies Daniel S. Wilks 5. Forecast value: descriptive decision studies Thomas R. Stewart 6. Forecast value: prototype decision-making models Richard W. Katz and Allan H. Murphy Index.
A növénytermesztés környezeti hatásainak értékelése,különös tekintettel a biodiverzitásra. Egy intenzív- és egy ökogazdaság externális hatásai = Evaluation of environmental impacts of crop production,with particular focus on biodiversity. External impacts of an intensive farm and an ecological farm

A disszertacionak ket fő celja volt: egyreszt a szantofoldi novenytermesztes externalis kornyezeti hatasainak (talaj, viz es levegőszennyezes, emberi egeszseg, biodiverzitas, tajkep) egyuttes, technologia-specifikus, gazdasagi szintű ertekelese, masreszt a biodiverzitasra gyakorolt hatas ertekelese soran modszertani előrelepes elerese. A hatasok ertekelesere kivalasztott modszereket a holisztikus szemlelet okan alkalmaztuk, amely altal teljesebb kepet alkothattunk az externalis kornyezeti hatasokrol. A modszereket „felfűztuk a temara”, azaz a modszervalasztast elsősorban a kornyezeti hatasok jellegzetessegei, a szakirodalmi gyakorlat hatarozta meg. Osszesen ot empirikus vizsgalat (kvalitativ ertekeles, tanacskozo monetaris ertekeles, felteteles ertekeles, felteteles valasztas, hatas-utvonal ertekeles) kepezte a kutatas alapjat.
Investigating the gaze control ability of VALORANT players using a Python based tool

The current study investigated the gaze movements of FPS gamers in actual game environments. We developed a low-cost analysis tool using Python to identify gaze movements in real-world gaming environments. In Experiment 1, 11 middle-skilled and ten high-skilled FPS gamers performed a task under the experimental condition. Gaze position, reaction time, and accuracy were calculated during the task. Reaction time exhibited a significant positive correlation with task accuracy, suggesting that speed and accuracy were associated with higher game performance. The middle-skilled gamers had a significantly wider horizontal gaze distribution than the high-skilled gamers, and gaze distribution and reaction time showed a negative correlation. These results suggested that high-skilled players utilize peripheral vision during gameplay. In Experiment 2, 15 middle-skilled and 12 high-skilled FPS gamers performed an actual FPS game match. The gaze distribution, kill/death/assist ratio (KDA), and percentage of gaze on game information were calculated. In experiment 2, gaze locations in less important areas were positively correlated with KDA. Thus, performance was determined by the important areas where the gaze was focused rather than by the coordination of gaze position alone. Therefore, a broader range of environments is necessary to comprehend the superior performance of FPS gamers.
AMG: Automated Efficient Approximate Multiplier Generator for FPGAs via Bayesian Optimization

We propose AMG, an open-source automated approximate multiplier generator for FPGAs driven by Bayesian optimization (BO) with parallel evaluation. The proposed method simplifies the exact half adders (HAs) for the initial partial product (PP) compression in a multiplier while preserving coarse-grained additions for the following accumulation. The generated multipliers can be effectively mapped to lookup tables (LUTs) and carry chains provided by modern FPGAs. Compared with 1167 multipliers from previous works, our generated multipliers can form a Pareto front with 28.70%-38.47% improvements in terms of the product of hardware cost and error on average. All source codes, reproduced multipliers, and our generated multipliers are available at https://github.com/phyzhenli/AMG.
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization

signSGD is popular in nonconvex optimization due to its communication efficiency. Yet, existing analyses of signSGD rely on assuming that data are sampled with replacement in each iteration, contradicting the practical implementation where data are randomly reshuffled and sequentially fed into the algorithm. We bridge this gap by proving the first convergence result of signSGD with random reshuffling (SignRR) for nonconvex optimization. Given the dataset size $n$, the number of epochs of data passes $T$, and the variance bound of a stochastic gradient $\sigma^2$, we show that SignRR has the same convergence rate $O(\log(nT)/\sqrt{nT} + \|\sigma\|_1)$ as signSGD \citep{bernstein2018signsgd}. We then present SignRVR and SignRVM, which leverage variance-reduced gradients and momentum updates respectively, both converging at $O(\log (nT)/\sqrt{nT} + \log (nT)\sqrt{n}/\sqrt{T})$. In contrast with the analysis of signSGD, our results do not require an extremely large batch size in each iteration to be of the same order as the total number of iterations \citep{bernstein2018signsgd} or the signs of stochastic and true gradients match element-wise with a minimum probability of 1/2 \citep{safaryan2021stochastic}. We also extend our algorithms to cases where data are distributed across different machines, yielding dist-SignRVR and dist-SignRVM, both converging at $O(\log (n_0T)/\sqrt{n_0T} + \log (n_0T)\sqrt{n_0}/\sqrt{T})$, where $n_0$ is the dataset size of a single machine. We back up our theoretical findings through experiments on simulated and real-world problems, verifying that randomly reshuffled sign methods match or surpass existing baselines.
Effects of a soft surface layer on near-fault static and dynamic displacements

SUMMARY While detailed crustal structure around a damaged area is surveyed for source parameters obtained by seismic waveform inversions (e.g. the ‘damage belt’ of the 1995 Hyogoken-nambu earthquake in Japan), the effects of a layered medium are often not considered in practical analyses of geodetic data. In this study, we calculate static as well as dynamic displacements with the discrete wavenumber method and compare them with analytical as well as numerical solutions for a half-space. The existence of a soft surface layer is found to affect both the amplitude and the spatial distribution of static displacement near a fault, in a different manner from those of dynamic displacement. The largest effect of a surface layer is shown in the case of a vertical strike-slip fault. While dynamic motions are amplified monotonically with the thickness of the surface layer, the amplification of static displacement is limited if the surface layer reaches the top of the fault because the moment release there is then reduced. Errors in the estimation of the fault depth and the amount of slip reaches as large as 30 per cent of the true values in the worst case. Our results strongly suggest that a strong bias may be introduced to the conventional estimation of fault parameters, assuming a medium to be a half-space, from static displacement or geodetic data by the existence of a low-velocity or low-rigidity surface layer.
The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not)

Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper, we introduce a framework for answering this question both intuitively and quantitatively. The underlying structure of the framework is a race between quantum and classical computers, where their relative strengths determine when each wins. While classical computers operate faster, quantum computers can sometimes run more efficient algorithms. Whether the speed advantage or the algorithmic advantage dominates determines whether a problem will benefit from quantum computing or not. Our analysis reveals that many problems, particularly those of small to moderate size that can be important for typical businesses, will not benefit from quantum computing. Conversely, larger problems or those with particularly big algorithmic gains will benefit from near-term quantum computing. Since very large algorithmic gains are rare in practice and theorized to be rare even in principle, our analysis suggests that the benefits from quantum computing will flow either to users of these rare cases, or practitioners processing very large data.
Compressive quantum waveform estimation

Quantum waveform estimation, in which quantum sensors sample entire time series, promises to revolutionize the sensing of weak and stochastic signals, such as the biomagnetic impulses emitted by firing neurons. For long duration signals with rapid transients, regular quantum sampling becomes prohibitively resource intensive as it demands many measurements with distinct control and readout. In this Manuscript, we demonstrate how careful choice of quantum measurements, along with the modern mathematics of compressive sensing, achieves quantum waveform estimation of sparse signals in a number of measurements far below the Nyquist requirement. We sense synthesized neural-like magnetic signals with radiofrequency-dressed ultracold atoms, retrieving successful waveform estimates with as few measurements as compressive theoretical bounds guarantee.
Dwork-type $q$-congruences through the $q$-Lucas theorem

Employing the $q$-Lucas theorem and some known $q$-supercongruences, we give some Dwork-type $q$-congruences, confirming three conjectures in [J. Combin. Theory, Ser. A 178 (2021), Art.~105362]. As conclusions, we obtain the following supercongruences: for any prime $p\equiv 1\pmod{4}$ and positive integer $r$, \begin{align*} \sum_{k=0}^{(p^r-1)/2} \frac{(\frac{1}{2})_k^3}{k! ^3}&\equiv -\Gamma_p(\tfrac{1}{4})^4 \sum_{k=0}^{(p^{r-1}-1)/2} \frac{(\frac{1}{2})_k^3}{k! ^3} \pmod{p^{r+1}}, \\ \sum_{k=0}^{p^r-1} \frac{(\frac{1}{2})_k^3}{k! ^3}&\equiv -\Gamma_p(\tfrac{1}{4})^4 \sum_{k=0}^{p^{r-1}-1} \frac{(\frac{1}{2})_k^3}{k! ^3} \pmod{p^{r+1}}, \end{align*} where $\Gamma_p(x)$ stands for the $p$-adic Gamma function. The first one confirms a weaker form of Swisher's (H.3) conjecture for $p\equiv 1\pmod{4}$, which originally predicts that the supercongruence is true modulo $p^{3r}$.
Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages

The class of tree-adjoining languages can be characterized by various two-level formalisms, consisting of a context-free grammar (CFG) or pushdown automaton (PDA) controlling another CFG or PDA. These four formalisms are equivalent to tree-adjoining grammars (TAG), linear indexed grammars (LIG), pushdown-adjoining automata (PAA), and embedded pushdown automata (EPDA). We define semiring-weighted versions of the above two-level formalisms, and we design new algorithms for computing their stringsums (the weight of all derivations of a string) and allsums (the weight of all derivations). From these, we also immediately obtain stringsum and allsum algorithms for TAG, LIG, PAA, and EPDA. For LIG, our algorithm is more time-efficient by a factor of $\mathcal{O}(n|\mathcal{N}|)$ (where $n$ is the string length and $|\mathcal{N}|$ is the size of the nonterminal set) and more space-efficient by a factor of $\mathcal{O}(|\Gamma|)$ (where $|\Gamma|$ is the size of the stack alphabet) than the algorithm of Vijay-Shanker and Weir (1989). For EPDA, our algorithm is both more space-efficient and time-efficient than the algorithm of Alonso et al. (2001) by factors of $\mathcal{O}(|\Gamma|^2)$ and $\mathcal{O}(|\Gamma|^3)$, respectively. Finally, we give the first PAA stringsum and allsum algorithms.
The Conspiracy Money Machine: Uncovering Telegram's Conspiracy Channels and their Profit Model

In recent years, major social media platforms have implemented increasingly strict moderation policies, resulting in bans and restrictions on conspiracy theory-related content. To circumvent these restrictions, conspiracy theorists are turning to alternatives, such as Telegram, where they can express and spread their views with fewer limitations. Telegram offers channels -- virtual rooms where only administrators can broadcast messages -- and a more permissive content policy. These features have created the perfect breeding ground for a complex ecosystem of conspiracy channels. In this paper, we illuminate this ecosystem. First, we propose an approach to detect conspiracy channels. Then, we discover that conspiracy channels can be clustered into four distinct communities comprising over 17,000 channels. Finally, we uncover the"Conspiracy Money Machine,"revealing how most conspiracy channels actively seek to profit from their subscribers. We find conspiracy theorists leverage e-commerce platforms to sell questionable products or lucratively promote them through affiliate links. Moreover, we observe that conspiracy channels use donation and crowdfunding platforms to raise funds for their campaigns. We determine that this business involves hundreds of donors and generates a turnover of over $90 million.
Language-driven Scene Synthesis using Multi-conditional Diffusion Model

Scene synthesis is a challenging problem with several industrial applications. Recently, substantial efforts have been directed to synthesize the scene using human motions, room layouts, or spatial graphs as the input. However, few studies have addressed this problem from multiple modalities, especially combining text prompts. In this paper, we propose a language-driven scene synthesis task, which is a new task that integrates text prompts, human motion, and existing objects for scene synthesis. Unlike other single-condition synthesis tasks, our problem involves multiple conditions and requires a strategy for processing and encoding them into a unified space. To address the challenge, we present a multi-conditional diffusion model, which differs from the implicit unification approach of other diffusion literature by explicitly predicting the guiding points for the original data distribution. We demonstrate that our approach is theoretically supportive. The intensive experiment results illustrate that our method outperforms state-of-the-art benchmarks and enables natural scene editing applications. The source code and dataset can be accessed at https://lang-scene-synth.github. io/.
On the minimal model of kinetic cooperativity. The case of glucokinase

The minimal 3-state scheme of kinetic cooperativity of monomeric enzymes is subjected to detailed analysis. The rigorous criteria of positive cooperativity and its sigmoidal version are established in terms of the system parameters (rate constants). It is shown that the cooperativity extent is especially sensitive to the rates and direction of the exchange between conformational states of the free enzyme. However, no necessity of the"kinetic resonance"(or, moreover, its generality claimed recently) for enhancing cooperativity is revealed. Overall, while the minimal 3-state model serves well for qualitative understanding the origin of kinetic cooperativity, it is hardly suitable for quantitative describing reactions of real enzymes, as it is shown with the case of glucokinase.