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A common step at the core of many RNA transcript assembly tools is to find a set of weighted paths that best explain the weights of a DAG. While such problems easily become NP-hard, scalable solvers exist only for a basic error-free version of this problem, namely minimally decomposing a network flow into weighted paths. The main result of this paper is to show that we can achieve speedups of two orders of magnitude also for path-finding problems in the realistic setting (i.e., the weights do not induce a flow). We obtain these by employing the safety information that is encoded in the graph structure inside Integer Linear Programming (ILP) solvers for these problems. We first characterize the paths that appear in all path covers of the DAG, generalizing a graph reduction commonly used in the error-free setting (e.g. by Kloster et al. [ALENEX~2018]). Secondly, following the work of Ma, Zheng and Kingsford [RECOMB 2021], we characterize the \emph{sequences} of arcs that appear in all path covers of the DAG. We experiment with a path-finding ILP model (least squares) and with a more recent and accurate one. We use a variety of datasets originally created by Shao and Kingsford [TCBB, 2017], as well as graphs built from sequencing reads by the state-of-the-art tool for long-read transcript discovery, IsoQuant [Prjibelski et al., Nat.~Biotechnology~2023]. The ILPs armed with safe paths or sequences exhibit significant speed-ups over the original ones. On graphs with a large width, average speed-ups are in the range $50-160\times$ in the latter ILP model and in the range $100-1000\times$ in the least squares model. Our scaling techniques apply to any ILP whose solution paths are a path cover of the arcs of the DAG. As such, they can become a scalable building block of practical RNA transcript assembly tools, avoiding heuristic trade-offs currently needed on complex graphs.

In group testing, the task is to identify defective items by testing groups of them together using as few tests as possible. We consider the setting where each item is defective with a constant probability $\alpha$, independent of all other items. In the (over-)idealized noiseless setting, tests are positive exactly if any of the tested items are defective. We study a more realistic model in which observed test results are subject to noise, i.e., tests can display false positive or false negative results with constant positive probabilities. We determine precise constants $c$ such that $cn\log n$ tests are required to recover the infection status of every individual for both adaptive and non-adaptive group testing: in the former, the selection of groups to test can depend on previously observed test results, whereas it cannot in the latter. Additionally, for both settings, we provide efficient algorithms that identify all defective items with the optimal amount of tests with high probability. Thus, we completely solve the problem of binary noisy group testing in the studied setting.

Quantum computing introduces many problems rooted in physics, asking to compute information from input quantum states. Determining the complexity of these problems has implications for both computer science and physics. However, as existing complexity theory primarily addresses problems with classical inputs and outputs, it lacks the framework to fully capture the complexity of quantum-input problems. This gap is relevant when studying the relationship between quantum cryptography and complexity theory, especially within Impagliazzo's five worlds framework, as characterizing the security of quantum cryptographic primitives requires complexity classes for problems involving quantum inputs. To bridge this gap, we examine the complexity theory of quantum promise problems, which determine if input quantum states have certain properties. We focus on complexity classes p/mBQP, p/mQ(C)MA, $\mathrm{p/mQSZK_{hv}}$, p/mQIP, and p/mPSPACE, where "p/mC" denotes classes with pure (p) or mixed (m) states corresponding to any classical class C. We establish structural results, including complete problems, search-to-decision reductions, and relationships between classes. Notably, our findings reveal differences from classical counterparts, such as p/mQIP $\neq$ p/mPSPACE and $\mathrm{mcoQSZK_{hv}} \neq \mathrm{mQSZK_{hv}}$. As an application, we apply this framework to cryptography, showing that breaking one-way state generators, pseudorandom states, and EFI is bounded by mQCMA or $\mathrm{mQSZK_{hv}}$. We also show that the average-case hardness of $\mathrm{pQCZK_{hv}}$ implies the existence of EFI. These results provide new insights into Impagliazzo's worlds, establishing a connection between quantum cryptography and quantum promise complexity theory. We also extend our findings to quantum property testing and unitary synthesis, highlighting further applications of this new framework.

While many works have studied statistical data fusion, they typically assume that the various datasets are given in advance. However, in practice, estimation requires difficult data collection decisions like determining the available data sources, their costs, and how many samples to collect from each source. Moreover, this process is often sequential because the data collected at a given time can improve collection decisions in the future. In our setup, given access to multiple data sources and budget constraints, the agent must sequentially decide which data source to query to efficiently estimate a target parameter. We formalize this task using Online Moment Selection, a semiparametric framework that applies to any parameter identified by a set of moment conditions. Interestingly, the optimal budget allocation depends on the (unknown) true parameters. We present two online data collection policies, Explore-then-Commit and Explore-then-Greedy, that use the parameter estimates at a given time to optimally allocate the remaining budget in the future steps. We prove that both policies achieve zero regret (assessed by asymptotic MSE) relative to an oracle policy. We empirically validate our methods on both synthetic and real-world causal effect estimation tasks, demonstrating that the online data collection policies outperform their fixed counterparts.

The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning representations (or features) of the data that make it easier to extract useful information when building classifiers or other predictors". In this paper, we propose a novel Denoising Model for Representation Learning (DenoiseRep) to improve feature discrimination with joint feature extraction and denoising. DenoiseRep views each embedding layer in a backbone as a denoising layer, processing the cascaded embedding layers as if we are recursively denoise features step-by-step. This unifies the frameworks of feature extraction and denoising, where the former progressively embeds features from low-level to high-level, and the latter recursively denoises features step-by-step. After that, DenoiseRep fuses the parameters of feature extraction and denoising layers, and theoretically demonstrates its equivalence before and after the fusion, thus making feature denoising computation-free. DenoiseRep is a label-free algorithm that incrementally improves features but also complementary to the label if available. Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03, vehicleID), image classification (ImageNet, UB200, Oxford-Pet, Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements. We also validate its effectiveness on the CNN (ResNet) and Transformer (ViT, Swin, Vmamda) architectures.

In the rapidly advancing field of multi-agent systems, ensuring robustness in unfamiliar and adversarial settings is crucial. Notwithstanding their outstanding performance in familiar environments, these systems often falter in new situations due to overfitting during the training phase. This is especially pronounced in settings where both cooperative and competitive behaviours are present, encapsulating a dual nature of overfitting and generalisation challenges. To address this issue, we present Multi-Agent Diagnostics for Robustness via Illuminated Diversity (MADRID), a novel approach for generating diverse adversarial scenarios that expose strategic vulnerabilities in pre-trained multi-agent policies. Leveraging the concepts from open-ended learning, MADRID navigates the vast space of adversarial settings, employing a target policy's regret to gauge the vulnerabilities of these settings. We evaluate the effectiveness of MADRID on the 11vs11 version of Google Research Football, one of the most complex environments for multi-agent reinforcement learning. Specifically, we employ MADRID for generating a diverse array of adversarial settings for TiZero, the state-of-the-art approach which "masters" the game through 45 days of training on a large-scale distributed infrastructure. We expose key shortcomings in TiZero's tactical decision-making, underlining the crucial importance of rigorous evaluation in multi-agent systems.

The delay monad provides a way to introduce general recursion in type theory. To write programs that use a wide range of computational effects directly in type theory, we need to combine the delay monad with the monads of these effects. Here we present a first systematic study of such combinations. We study both the coinductive delay monad and its guarded recursive cousin, giving concrete examples of combining these with well-known computational effects. We also provide general theorems stating which algebraic effects distribute over the delay monad, and which do not. Lastly, we salvage some of the impossible cases by considering distributive laws up to weak bisimilarity.

Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at //github.com/hyintell/awesome-refreshing-llms

Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish some tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/ weaknesses, we have simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. While many different methods have been proposed, there is a lack of a unifying framework that would lead to state-of-the-art results. Here we develop PathCon, a knowledge graph completion method that harnesses four novel insights to outperform existing methods. PathCon predicts relations between a pair of entities by: (1) Considering the Relational Context of each entity by capturing the relation types adjacent to the entity and modeled through a novel edge-based message passing scheme; (2) Considering the Relational Paths capturing all paths between the two entities; And, (3) adaptively integrating the Relational Context and Relational Path through a learnable attention mechanism. Importantly, (4) in contrast to conventional node-based representations, PathCon represents context and path only using the relation types, which makes it applicable in an inductive setting. Experimental results on knowledge graph benchmarks as well as our newly proposed dataset show that PathCon outperforms state-of-the-art knowledge graph completion methods by a large margin. Finally, PathCon is able to provide interpretable explanations by identifying relations that provide the context and paths that are important for a given predicted relation.