Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mixed Supervised Learning Framework For Target Sound Detection

—Target sound detection (TSD) aims to detect the target sound from mixture audio given the reference information. Previous works have shown that TSD models can be trained on fully-annotated (frame-level label) or weakly-annotated (clip-level label) data. However, there are some clear evidences show that the performance of the model trained on weakly-annotated data is worse than that trained on fully-annotated data. To ﬁll this gap, we provide a mixed supervision perspective, in which learning novel categories (target domain) using weak annotations with the help of full annotations of existing base categories (source domain). To realize this, a mixed supervised learning framework is proposed, which contains two mutually-helping student models ( f student and w student ) that learn from fully-annotated and weakly-annotated data, respectively. The motivation is that f student learned from fully-annotated data has a better ability to capture detailed information than w student . Thus, we ﬁrst let f student guide w student to learn the ability to capture details, so w student can perform better in the target domain. Then we let w student guide f student to ﬁne- tune on the target domain. The process can be repeated several times so that the two students perform very well in the target domain. To evaluate our method, we built three TSD datasets based on UrbanSound and Audioset. Experimental results show that our methods offer about 8% improvement in event-based F-score as compared with a recent baseline.
FlowNAS: Neural Architecture Search for Optical Flow Estimation

Existing optical flow estimators usually employ the network architectures typically designed for image classification as the encoder to extract per-pixel features. However, due to the natural difference between the tasks, the architectures designed for image classification may be sub-optimal for flow estimation. To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task. We first design a suitable search space including various convolutional operators and construct a weight-sharing super-network for efficiently evaluating the candidate architectures. Then, for better training the super-network, we propose Feature Alignment Distillation, which utilizes a well-trained flow estimator to guide the training of super-network. Finally, a resource-constrained evolutionary algorithm is exploited to find an optimal architecture (i.e., sub-network). Experimental results show that the discovered architecture with the weights inherited from the super-network achieves 4.67\% F1-all error on KITTI, an 8.4\% reduction of RAFT baseline, surpassing state-of-the-art handcrafted models GMA and AGFlow, while reducing the model complexity and latency. The source code and trained models will be released in https://github.com/VDIGPKU/FlowNAS.
Independent Evidence for Earlier Formation Epochs of Fossil Groups of Galaxies through the Intracluster Light: The Case for RX J100742.53+380046.6

Fossil groups (FG) of galaxies still present a puzzle to theories of structure formation. Despite the low number of bright galaxies, they have relatively high velocity dispersions and ICM temperatures often corresponding to cluster-like potential wells. Their measured concentrations are typically high, indicating early formation epochs as expected from the originally proposed scenario for their origin as being older undisturbed systems. This is, however, in contradiction with the typical lack of expected well developed cool cores. Here, we apply a cluster dynamical indicator recently discovered in the intracluster light fraction (ICLf) to a classic FG, RX J1000742.53+380046.6, to assess its dynamical state. We also refine that indicator to use as an independent age estimator. We find negative radial temperature and metal abundance gradients, the abundance achieving supersolar values at the hot core. The X-ray flux concentration is consistent with that of cool core systems. The ICLf analysis provides an independent probe of the system’s dynamical state and shows that the system is very relaxed, more than all clusters, where the same analysis has been performed. The specific ICLf is about 6 times higher, than any of the clusters previously analyzed, which is consistent with an older noninteractive galaxy system that had its last merging event within the last ∼5 Gyr. The specific ICLf is predicted to be an important new tool to identify fossil systems and to constrain the relative age of clusters.
Uniqueness of trace and C*-simplicity beyond regular representation

A discrete group Γ is C*-simple if the C*-algebra C ∗ λ ( Γ ) generated by the range of the left regular representation λ on ℓ 2 ( Γ ) is simple. In this case, Γ acts faithfully on the Furstenberg boundary ∂ F Γ and there is a unique trace on C ∗ λ ( Γ ). In this paper we study the unique trace property for the C*-algebra C ∗ π ( Γ ) generated by the range of an arbitrary unitary representation π : Γ → B ( H π ) and relate it to the faithfulness of the action of Γ on the Furstenberg-Hamana boundary B π . Similar relation is obtained between simplicity of C ∗ π ( Γ ) and (topological) freeness of the action of Γ on B π . Along the way, we extend the Connes-Sullivan and Powers averaging properties for a unitary representation π and relate them to simplicity and unique trace property of C ∗ π ( Γ ).
Harmonizer: Learning to Perform White-Box Image and Video Harmonization

Recent works on image harmonization solve the problem as a pixel-wise image translation task via large autoencoders. They have unsatisfactory performances and slow inference speeds when dealing with high-resolution images. In this work, we observe that adjusting the input arguments of basic image filters, e.g. , brightness and contrast, is sufficient for humans to produce realistic images from the composite ones. Hence, we frame image harmonization as an image-level regression problem to learn the arguments of the filters that humans use for the task. We present a Harmonizer framework for image harmonization. Unlike prior methods that are based on black-box autoencoders, Harmonizer contains a neural network for filter argument prediction and several white-box filters (based on the predicted arguments) for image harmonization. We also introduce a cascade regressor and a dynamic loss strategy for Harmonizer to learn filter arguments more stably and precisely. Since our network only outputs image-level arguments and the filters we used are efficient, Harmonizer is much lighter and faster than existing methods. Comprehensive experiments demonstrate that Harmonizer surpasses existing methods notably, especially with high-resolution inputs. Finally, we apply Harmonizer to video harmonization, which achieves consistent results across frames and 56 fps at 1080P resolution. Code and models are available at: https://github.com/ZHKKKe/Harmonizer. 4.1). We also conduct ablation experiments to evaluate the effectiveness of individual components in Harmonizer (Sec. 4.3). We further show the effectiveness of adapting Harmonizer to video harmonization (Sec. ? ). Fi-nally, we demonstrate the advantages of Harmonizer in real-world image/video harmonization applications through user studies (Sec. 4.4).
Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques

We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for reducing the debug effort. This includes the heuristics for engineering features from logs of many tests, and the data analytics techniques for generating powerful debug hints. As a case study, we used these techniques for root-causing failures of the Power Management (PM) design feature Package-C8 and showed their effectiveness. Furthermore, we propose an approach for mining the root-causing experience and results for reuse, to accelerate future debug activities and reduce dependency on validation experts. We believe that these techniques are beneficial also for other validation activities at different levels of abstraction, for complex hardware, software and firmware systems, both pre-silicon and post-silicon.
Recommendation Systems with Distribution-Free Reliability Guarantees

When building recommendation systems, we seek to output a helpful set of items to the user. Under the hood, a ranking model predicts which of two candidate items is better, and we must distill these pairwise comparisons into the user-facing output. However, a learned ranking model is never perfect, so taking its predictions at face value gives no guarantee that the user-facing output is reliable. Building from a pre-trained ranking model, we show how to return a set of items that is rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate (FDR), regardless of the (unknown) data distribution. Moreover, our calibration algorithm enables the easy and principled integration of multiple objectives in recommender systems. As an example, we show how to optimize for recommendation diversity subject to a user-specified level of FDR control, circumventing the need to specify ad hoc weights of a diversity loss against an accuracy loss. Throughout, we focus on the problem of learning to rank a set of possible recommendations, evaluating our methods on the Yahoo! Learning to Rank and MSMarco datasets.
FRAME: Evaluating Simulatability Metrics for Free-Text Rationales

Free-text rationales aim to explain neural language model (LM) behavior more ﬂexibly and intuitively via natural language. To ensure rationale quality, it is important to have metrics for measuring rationales’ faithfulness (re-ﬂects LM’s actual behavior) and plausibility (convincing to humans). All existing free-text rationale metrics are based on simulatability (association between rationale and LM’s predicted label), but there is no protocol for assessing such metrics’ reliability. To investigate this, we propose FRAME, a framework for evaluating free-text rationale simulatability metrics. FRAME is based on three axioms: (1) good metrics should yield highest scores for reference rationales, which maximize rationale-label association by construction; (2) good metrics should be appropriately sensitive to semantic perturbation of rationales; and (3) good metrics should be robust to variation in the LM’s task performance. Across three text classiﬁcation datasets, we show that existing simulatability metrics cannot satisfy all three FRAME axioms, since they are implemented via model pretraining which muddles the metric’s signal. We introduce a non-pretraining simulatability variant that improves performance on (1) and (3) by an average of 41.7% and 42.9%, respectively, while performing competitively on (2).
Population properties and multimessenger prospects of neutron star-black hole mergers following GWTC-3

Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm rate ≤1 yr−1 to constrain the mass and spin distributions and multimessenger prospects of these systems. We find that the black holes in NSBHs are both less massive and have smaller dimensionless spins than those in black hole binaries. We also find evidence for a mass gap between the most massive neutron stars and least massive black holes in NSBHs at 98.6% credibility. Using an approach driven by gravitational-wave data rather than binary simulations, we find that fewer than 14% of NSBH mergers detectable in gravitational waves will have an electromagnetic counterpart. While the inferred presence of a mass gap and fraction of sources with a counterpart depend on the event selection and prior knowledge of source classification, the conclusion that the black holes in NSBHs have lower masses and smaller spin parameters than those in black hole binaries is robust. Finally, we propose a method for the multimessenger analysis of NSBH mergers based on the nondetection of an electromagnetic counterpart and conclude that, even in the most optimistic case, the constraints on the neutron star equation of state that can be obtained with multimessenger NSBH detections are not competitive with those from gravitational-wave measurements of tides in binary neutron star mergers and radio and X-ray pulsar observations.
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-shot image classification are attributes which are annotations for class-level visual characteristics. However, the current methods often fail to discriminate those subtle visual distinctions between images due to not only the shortage of fine-grained annotations, but also the attribute imbalance and co-occurrence. In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pre-trained language models (PLMs) via a self-supervised multi-modal learning paradigm. Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives. We find that our DUET can achieve state-of-the-art performance on three standard ZSL benchmarks and a knowledge graph equipped ZSL benchmark. Its components are effective and its predictions are interpretable.
Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach

While constructing polar codes for successive-cancellation decoding can be implemented efficiently by sorting the bit channels, finding optimal polar codes for cyclic-redundancy-check-aided successive-cancellation list (CA-SCL) decoding in an efficient and scalable manner still awaits investigation. This paper first maps a polar code to a unique heterogeneous graph called the polar-code-construction message-passing (PCCMP) graph. Next, a heterogeneous graph-neural-network-based iterative message-passing (IMP) algorithm is proposed which aims to find a PCCMP graph that corresponds to the polar code with minimum frame error rate under CA-SCL decoding. This new IMP algorithm’s major advantage lies in its scalability power. That is, the model complexity is independent of the blocklength and code rate, and a trained IMP model over a short polar code can be readily applied to a long polar code’s construction. Numerical experiments show that IMP-based polar-code constructions outperform classical constructions under CA-SCL decoding. In addition, when an IMP model trained on a length-128 polar code directly applies to the construction of polar codes with different code rates and blocklengths, simulations show that these polar-code constructions deliver comparable performance to the 5G polar codes.
How to Sample Approval Elections?

We extend the map-of-elections framework to the case of approval elections. While doing so, we study a number of statistical cultures, including some new ones, and we analyze their properties. We find that approval elections can be understood in terms of the average number of approvals in the votes, and the extent to which the votes are chaotic.
Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution

Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each local structure starts at different times and lasts for various durations. To maintain asynchronous structural evolutions within the graph, we innovatively formulate dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). Then, a time-aware Transformer is proposed to embed vertices’ dynamic connections and ToEs into the learned vertex representations. Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information. Extensive evaluations on several datasets show that our approach outperforms the state-of-the-art in a wide range of graph mining tasks. At the same time, it is very efficient and scalable for embedding large-scale dynamic graphs.
It's all About Consistency: A Study on Memory Composition for Replay-Based Methods in Continual Learning

Continual Learning methods strive to mitigate Catastrophic Forgetting (CF), where knowledge from previously learned tasks is lost when learning a new one. Among those algorithms, some maintain a subset of samples from previous tasks when training. These samples are referred to as a memory . These methods have shown outstanding performance while being conceptually simple and easy to implement. Yet, despite their popularity, little has been done to understand which elements to be included into the memory. Currently, this memory is often ﬁlled via random sampling with no guiding principles that may aid in retaining previous knowledge. In this work, we propose a criterion based on the learning consistency of a sample called Consistency AWare Sampling (CAWS). This criterion prioritizes samples that are easier to learn by deep networks. We perform studies on three different memory-based methods: AGEM, GDumb, and Experience Replay, on MNIST, CIFAR-10 and CIFAR-100 datasets. We show that using the most consistent elements yields performance gains when constrained by a compute budget; when under no such constrain, random sampling is a strong baseline. However, using CAWS on Experience Replay yields improved performance over the random baseline. Finally, we show that CAWS achieves similar results to a popular memory selection method while requiring signiﬁcantly less computational resources.
Free factors and profinite completions

Can one detect free products of groups via their profinite completions? We answer positively among virtually free groups. More precisely, we prove that a subgroup of a finitely generated virtually free group $G$ is a free factor if and only if its closure in the profinite completion of $G$ is a profinite free factor. This generalises results by Parzanchevski and Puder (later also proved by Wilton) for free groups. Our methods are entirely different to theirs, combining homological properties of profinite groups and the decomposition theory of Dicks and Dunwoody.
Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies

Harmful repercussions from sharing sensitive or personal data can hamper institutions' willingness to engage in data exchange. Thus, institutions consider Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) to engage in Sovereign Data Exchange (SDE), i.e., sharing data with third parties without compromising their own or their users' data sovereignty. However, these technologies are often technically complex, which impedes their adoption. To support practitioners select PETs and AETs for SDE use cases and highlight SDE challenges researchers and practitioners should address, this study empirically constructs a challenge-oriented technology mapping. First, we compile challenges of SDE by conducting a systematic literature review and expert interviews. Second, we map PETs and AETs to the SDE challenges and identify which technologies can mitigate which challenges. We validate the mapping through investigator triangulation. Although the most critical challenge concerns data usage and access control, we find that the majority of PETs and AETs focus on data processing issues.
A Hybrid Optimal Control Approach to Multi-Aircraft Formation Flying

A Hybrid Optimal Control (HOC) framework which can be used in conjunction with existing optimal control software is presented. By using hybrid optimal control theory, trajectory optimization problems for systems that are both discrete time and continuous time from a mathematical perspective can be solved. A case study on multi-aircraft formation flying for civil aviation is performed which demonstrates the capabilities of the designed method. Whilst previous research has dealt with either high accuracy trajectories for small formation flying problems or low accuracy modelling of very large formation flying trajectories, in this thesis HOC is used to achieve a high accuracy optimal trajectory for formations of three aircraft and larger. Given the number of flights that are performed every day, the impact of saving fuel just by optimizing their trajectories using formation flying can be significant on a global scale, both environmentally and economically.
Error bound and exact penalty method for optimization problems with nonnegative orthogonal constraint

This paper is concerned with a class of optimization problems with the non-negative orthogonal constraint, in which the objective function is $L$-smooth on an open set containing the Stiefel manifold $\textrm {St}(n,r)$. We derive a locally Lipschitzian error bound for the feasible points without zero rows when $n>r>1$, and when $n>r=1$ or $n=r$ achieve a global Lipschitzian error bound. Then, we show that the penalty problem induced by the elementwise $\ell _1$-norm distance to the non-negative cone is a global exact penalty, and so is the one induced by its Moreau envelope under a lower second-order calmness of the objective function. A practical penalty algorithm is developed by solving approximately a series of smooth penalty problems with a retraction-based nonmonotone line-search proximal gradient method, and any cluster point of the generated sequence is shown to be a stationary point of the original problem. Numerical comparisons with the ALM [Wen, Z. W. & Yin, W. T. (2013, A feasible method for optimization with orthogonality constraints. Math. Programming, 142, 397–434),] and the exact penalty method [Jiang, B. , Meng, X. , Wen, Z. W. & Chen, X. J. (2022, An exact penalty approach for optimization with nonnegative orthogonality constraints. Math. Programming. https://doi.org/10.1007/s10107-022-01794-8)] indicate that our penalty method has an advantage in terms of the quality of solutions despite taking a little more time.