Miracle-dz/UniFAR-FTUs huggingface dataset:
Agile Modeling: From Concept to Classifier in Minutes

The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "gourmet tuna"). However, empowering any user to develop a classifier for their concept is technically difficult: users are neither machine learning experts nor have the patience to label thousands of examples. In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through real-time user-in-the-loop interactions. We instantiate an Agile Modeling prototype for image classification and show through a user study (N=14) that users can create classifiers with minimal effort in under 30 minutes. We compare this user driven process with the traditional crowdsourcing paradigm and find that the crowd’s notion often differs from that of the user’s, especially as the concepts become more subjective. Finally, we scale our experiments with simulations of users training classifiers for ImageNet21k categories to further demonstrate the efficacy of the approach.
Kugelblitz: Streamlining Reconfigurable Packet Processing Pipeline Design and Evaluation

Reconfigurable packet processing pipelines have emerged as a common building block for offloading fast and efficient packet processing to programmable switches and SmartNICs. However, designing or extending such architectures is complex, laborious, and resource intensive, due to the large design space coupled with the need for building architecture-specific compilers and RTL implementations before any evaluation is feasible. In this paper, we present Kugelblitz, a framework for rapidly designing, implementing and evaluating reconfigurable packet processing pipeline architectures. Kugelblitz comprises domain specific abstractions for separately representing pipeline architectures and protocol processing programs, a compiler for automatically mapping programs to specific architectures, and a hardware generator outputting complete RTL implementations for specific pipeline architectures. Kugelblitz enables meaningful full-system evaluation including chip area and timing from VLSI synthesis of the generated RTL, and end-to-end system benchmarks running real application workloads in full system simulation. In our evaluation, we demonstrate that Kugelblitz generates realistic and efficient RTL implementations, scales to large pipelines, enables fast design exploration, and provides meaningful application offload performance results.
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations

Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extremely useful in low label-resource settings. However, the success of SSL models has yet to transfer to utterance-level tasks such as speaker, emotion, and language recognition, which still require supervised fine-tuning of the SSL models to obtain good performance. We argue that the problem is caused by the lack of disentangled representations and an utterance-level learning objective for these tasks. Inspired by how HuBERT uses clustering to discover hidden acoustic units, we formulate a factor analysis (FA) model that uses the discovered hidden acoustic units to align the SSL features. The underlying utterance-level representations are disentangled from the content of speech using probabilistic inference on the aligned features. Furthermore, the variational lower bound derived from the FA model provides an utterance-level objective, allowing error gradients to be backpropagated to the Transformer layers to learn highly discriminative acoustic units. When used in conjunction with HuBERT's masked prediction training, our models outperform the current best model, WavLM, on all utterance-level non-semantic tasks on the SUPERB benchmark with only 20% of labeled data.
CO multi-line imaging of nearby galaxies (COMING). XII. CO-to-H2 conversion factor and dust-to-gas ratio

We simultaneously measured the spatially resolved CO-to-H2 conversion factor (αCO) and dust-to-gas ratio (DGR) in nearby galaxies on a kiloparsec scale. In this study, we used 12CO(J = 1–0) data obtained by the Nobeyama 45 m radio telescope with H i and dust mass surface densities. We obtained the values of global αCO and DGR in 22 nearby spiral galaxies, with averages of 2.66 ± 1.36 M⊙ pc−2 (K km s−1)−1 and 0.0052 ± 0.0026, respectively. Furthermore, the radial variations of αCO and DGR in four barred spiral galaxies (IC 342, NGC 3627, NGC 5236, and NGC 6946) were obtained by dividing them into inner and outer regions with a boundary of 0.2 R25, where R25 is the isophotal radius at 25 mag arcsec−2 in the B band. The averages of αCO and DGR in the inner region (≤0.2 R25) are 0.36 ± 0.08 M⊙ pc−2 (K km s−1)−1 and 0.0199 ± 0.0058, while those in the outer region (>0.2 R25) are 1.49 ± 0.76 M⊙ pc−2 (K km s−1)−1 and 0.0084 ± 0.0037, respectively. The value of αCO in the outer region is 2.3 to 5.3 times larger than that of the inner region. When separated into the inner and outer regions, we find that αCO and DGR correlate with the metallicity and the star formation rate surface density. The value of αCO derived in this study tends to be smaller than those obtained in previous studies for the Milky Way and nearby star-forming galaxies. This fact can be attributed to our measurements being biased toward the inner region; we measured αCO at 0.85 and 0.76 times smaller in radius than the previous works for nearby star-forming galaxies and the Milky Way, respectively.
Optimal quantum speed for mixed states

The question of how fast a quantum state can evolve is considered. Using the definition of squared speed based on the Euclidean distance given in [Phys. Rev. Research, {\bf 2}, 033127 (2019)], we present a systematic framework to obtain the optimal speed of a $d$-dimensional system evolved unitarily under a time-independent Hamiltonian. Among the set of mixed quantum states having the same purity, the optimal state is obtained in terms of its purity parameter. We show that for an arbitrary $d$, the optimal state is represented by a $X$-state with an additional property of being symmetric with respect to the secondary diagonal. For sufficiently low purities for which the purity exceeds the purity of maximally mixed state $\Id/d$ by at most $2/d^2$, the only nonzero off-diagonal entry of the optimal state is $\varrho_{1d}$, corresponding to the transition amplitude between two energy eigenstates with minimum and maximum eigenvalues, respectively. For larger purities, however, whether or not the other secondary diameter entries $\varrho_{i,d-i+1}$ take nonzero values depends on their relative energy gaps $|E_{d-i+1}-E_{i}|$. The effects of coherence and entanglement, with respect to the energy basis, are also examined and found that for optimal states both resources are monotonic functions of purity, so they can cause speed up quantum evolution leading to a smaller quantum speed limit. Our results show that although the coherence of the states is responsible for the speed of evolution, only the coherence caused by some off-diagonal entries located on the secondary diagonal play a role in the fastest states.
Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility

In machine learning (ML), Python serves as a convenient abstraction for working with key libraries such as PyTorch, scikit-learn, and others. Unlike DBMS, however, Python applications may lose important data, such as trained models and extracted features, due to machine failures or human errors, leading to a waste of time and resources. Specifically, they lack four essential properties that could make ML more reliable and user-friendly---durability, atomicity, replicability, and time-versioning (DART). This paper presents our vision of Transactional Python that provides DART without any code modifications to user programs or the Python kernel, by non-intrusively monitoring application states at the object level and determining a minimal amount of information sufficient to reconstruct a whole application. Our evaluation of a proof-of-concept implementation with public PyTorch and scikit-learn applications shows that DART can be offered with overheads ranging 1.5%--15.6%.
Abelian quotients for groups generated by cubed half-twists

Below we construct non-cyclic and torsion-free abelian quotients for subgroups of braid groups generated by cube powers of half-twists. In the case of 3 and 4 strands we compute the abelianization of these groups. Also, we get abelianizations of level-3 congruence subgroups of braid groups on 3 and 4 strands. Moreover, we construct non-cyclic and torsion-free abelian quotients for level-3 congruence subgroups of braid groups. The constructions are based on the original construction of Dennis Johnson \cite{DJ}, who computed an abelian quotient of the Torelli group.
Leveraging Graph Embeddings for Opinion Leader Detection

Nowadays, social media plays an important role in many fields, such as the promotion of measures against major infectious diseases, merchandising, etc. In social media, some people are known as opinion leaders due to their strong ability to influence the opinions of others. The detection of opinion leaders has become an important task in social network analysis. Social networks are often represented in the form of graphs which allows a large number of graph analysis methods to be used for opinion leader detection. Some studies have attempted to apply graph representation learning for opinion leader detection and achieved good results. In this paper, we propose a model-agnostic framework that formulate the opinion leader detection problem as a ranking task of node embeddings. A variety of methods and datasets are chosen to analyze the performance of our framework both qualitatively and quantitatively. Based on the analysis results, we propose a strategy that combines opinion leaders detected by two different ranking algorithms to obtain a more comprehensive set of opinion leaders. And we analyze the temporal changes of the opinion leaders in one of the dynamic social networks.
Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses

The performance and capacity of solid-state drives (SSDs) are continuously improving to meet the increasing demands of modern data-intensive applications. Unfortunately, communication between the SSD controller and memory chips (e.g., 2D/3D NAND flash chips) is a critical performance bottleneck for many applications. SSDs use a multi-channel shared bus architecture where multiple memory chips connected to the same channel communicate to the SSD controller with only one path. As a result, path conflicts often occur during the servicing of multiple I/O requests, which significantly limits SSD parallelism. It is critical to handle path conflicts well to improve SSD parallelism and performance. Our goal is to fundamentally tackle the path conflict problem by increasing the number of paths between the SSD controller and memory chips at low cost. To this end, we build on the idea of using an interconnection network to increase the path diversity between the SSD controller and memory chips. We propose Venice, a new mechanism that introduces a low-cost interconnection network between the SSD controller and memory chips and utilizes the path diversity to intelligently resolve path conflicts. Venice employs three key techniques: 1) a simple router chip added next to each memory chip without modifying the memory chip design, 2) a path reservation technique that reserves a path from the SSD controller to the target memory chip before initiating a transfer, and 3) a fully-adaptive routing algorithm that effectively utilizes the path diversity to resolve path conflicts. Our experimental results show that Venice 1) improves performance by an average of 2.65×/1.67× over a baseline performance-optimized/cost-optimized SSD design across a wide range of workloads, 2) reduces energy consumption by an average of 61% compared to a baseline performance-optimized SSD design. Venice's benefits come at a relatively low area overhead.
A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy Search Over Policy Trees

The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing good solutions for problems with continuous action spaces remains challenging. To ease this challenge, we propose a simple online POMDP solver, called Lazy Cross-Entropy Search Over Policy Trees (LCEOPT). At each planning step, our method uses a novel lazy Cross-Entropy method to search the space of policy trees, which provide a simple policy representation. Specifically, we maintain a distribution on promising finite-horizon policy trees. The distribution is iteratively updated by sampling policies, evaluating them via Monte Carlo simulation, and refitting them to the top-performing ones. Our method is lazy in the sense that it exploits the policy tree representation to avoid redundant computations in policy sampling, evaluation, and distribution update. This leads to computational savings of up to two orders of magnitude. Our LCEOPT is surprisingly simple as compared to existing state-of-the-art methods, yet empirically outperforms them on several continuous-action POMDP problems, particularly for problems with higher-dimensional action spaces.
QAOA-MC: Markov chain Monte Carlo enhanced by Quantum Alternating Operator Ansatz

Quantum computation is expected to accelerate certain computational task over classical counterpart. Its most primitive advantage is its ability to sample from classically intractable probability distributions. A promising approach to make use of this fact is the so-called quantum-enhanced Markov chain Monte Carlo (MCMC) [D. Layden, et al., arXiv:2203.12497 (2022)] which uses outputs from quantum circuits as the proposal distributions. In this work, we propose the use of Quantum Alternating Operator Ansatz (QAOA) for quantum-enhanced MCMC and provide a strategy to optimize its parameter to improve convergence speed while keeping its depth shallow. The proposed QAOA-type circuit is designed to satisfy the specific constraint which quantum-enhanced MCMC requires with arbitrary parameters. Through our extensive numerical analysis, we find a correlation in certain parameter range between an experimentally measurable value, acceptance rate of MCMC, and the spectral gap of the MCMC transition matrix, which determines the convergence speed. This allows us to optimize the parameter in the QAOA circuit and achieve quadratic speedup in convergence. Since MCMC is used in various areas such as statistical physics and machine learning makes, this work represents an important step toward realizing practical quantum advantage with currently available quantum computers through quantum-enhanced MCMC.
Learning More Discriminative Local Descriptors for Few-shot Learning

Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled images and generalize over the new tasks. In this paper, motivated by the idea of Fisher Score, we propose a Discriminative Local Descriptors Attention (DLDA) model that adaptively selects the representative local descriptors and does not introduce any additional parameters, while most of the existing local descriptors based methods utilize the neural networks that inevitably involve the tedious parameter tuning. Moreover, we modify the traditional $k$-NN classification model by adjusting the weights of the $k$ nearest neighbors according to their distances from the query point. Experiments on four benchmark datasets show that our method not only achieves higher accuracy compared with the state-of-art approaches for few-shot learning, but also possesses lower sensitivity to the choices of $k$.
Continual Multimodal Knowledge Graph Construction

Multimodal Knowledge Graph Construction (MKGC) involves creating structured representations of entities and relations using multiple modalities, such as text and images. However, existing MKGC models face challenges in handling the addition of new entities and relations in dynamic real-world scenarios. The current continual setting for knowledge graph construction mainly focuses on entity and relation extraction from text data, overlooking other multimodal sources. Therefore, there arises the need to explore the challenge of continual MKGC to address the phenomenon of catastrophic forgetting and ensure the retention of past knowledge extracted from different forms of data. This research focuses on investigating this complex topic by developing lifelong MKGC benchmark datasets. Based on the empirical findings that several typical MKGC models, when trained on multimedia data, might unexpectedly underperform compared to those solely utilizing textual resources in a continual setting, we propose a Lifelong MultiModal Consistent Transformer Framework (LMC) for continual MKGC, which plays the strengths of the consistent multimodal optimization in continual learning and leads to a better stability-plasticity trade-off. Our experiments demonstrate the superior performance of our method over prevailing continual learning techniques or multimodal approaches in dynamic scenarios. Code and datasets can be found at https://github.com/zjunlp/ContinueMKGC.
Vitamin D Status Is Associated With Immune Checkpoint Inhibitor Efficacy and Immune-related Adverse Event Severity in Lung Cancer Patients: A Prospective Cohort Study

Vitamin D (VitD) is potentially immunomodulatory, so here we aimed to explore the relationships between serum VitD levels, immune checkpoint inhibitor (ICI) efficacy, and immune-related adverse events (irAEs). Serum 25-hydroxyvitamin D [25(OH)D] levels were quantified before and after ICI treatment in prospectively enrolled patients with advanced lung cancers. Of 77 enrolled patients, 29 developed 42 irAEs. Baseline 25(OH)D levels of partial response (PRs) patients were significantly higher than non-PR patients (19.39±7.16 vs. 16.28±5.99 ng/mL, P=0.04). The area under the curve of 25(OH)D >15.73 ng/mL to identify PR was 0.63 (95% CI, 0.51–0.76, P=0.047), and baseline 25(OH)D levels >15.73 ng/mL (odds ratio: 2.93, 95% CI, 1.10–7.79, P=0.03) and prior targeted therapy (odds ratio: 0.30, 95% CI, 0.10–0.92, P=0.04) were independent predictors of PR as best efficacy by multivariable logistic regression. With respect to irAEs, baseline 25(OH)D levels were higher in grade 1 irAE patients than in grade 2/3/4 irAE patients (20.07±8.64 vs. 15.22±2.30 ng/mL, P=0.02). However, the area under the curve was only 0.56 (95% CI, 0.42–0.70, P=0.39) for a baseline 25(OH)D of 20.99 ng/mL for predicting irAE occurrence. There was a direct monotonic relationship and U-shaped relationship between baseline 25(OH)D levels and ICI efficacy and irAE occurrence, respectively. Overall survival was significantly different between VitD sufficient, insufficient, and deficient patients (log-rank P=0.01), which remained after adjustment in Cox proportional hazards regression models. Baseline 25(OH)D levels seem to be associated with ICI efficacy and prognosis, it might be helpful to assess the baseline VitD status, and supplementation with VitD might bring some benefit to enhance ICI efficacy and reduce moderate-severe irAEs.
The inferior alveolar nerve and its relationship to the mandibular canal

Previous work on the mandibular canal, mental foramen, and mandibular foramen has focused on humans and some other non‐primate mammals (with small sample sizes), but little work has investigated the mandibular canal and inferior alveolar nerve (IAN) across primates. However, it is important to understand the relationship between the IAN and mandibular canal due to the IAN's close relationship to the teeth and mastication, and thus dietary adaptations. While it is assumed that most bony canals within the skull grow around and form to pre‐existing nervous structures, this relationship has never been validated for the IAN and mandibular canal. MicroCT scans of 273 individuals (131 females, 134 males, and 8 unknown sex) from 68 primate species and three mammalian outgroups, and diceCT scans of 66 individuals (35 females, 23 males, and 8 unknown sex) from 33 primate species and the same mammalian outgroups were used to create 3D models of the IAN and mandibular canal from which cross‐sectional areas were taken at various points on the structures. Using qualitative descriptions, phylogenetic generalized least squares analysis, and phylogenetic ANOVAs, we were able to establish three main conclusions: (1) the mandibular canal is most often not a defined canal within the mandible of primates, (2) when the canal can be identified, the IAN does not comprise most of the space within, and (3) there are significant relationships between the IAN and the corresponding canals, with most showing isometry and the mental foramen/nerve showing negative allometry.
Perioperative Acute Ischemic Stroke in Patients with Atrial Fibrillation

Anticoagulation therapy is commonly interrupted in patients with atrial fibrillation (AF) for elective procedures. However, the risk factors of acute ischemic stroke (AIS) during the periprocedural period remain uncertain. We performed a nationwide analysis to evaluate AIS risk factors in patients with AF undergoing elective surgical procedures.
Spin in the Abstracts of Meta-analyses and Systematic Reviews: Quadriceps Tendon Graft for Anterior Cruciate Ligament Reconstruction

Background: Spin is a reporting bias that misrepresents research. Ultimately it can affect surgeon decision making and patient care. Anterior cruciate ligament (ACL) reconstruction is common, but debate continues over optimal treatment modalities. Purpose: To identify the prevalence of spin in meta-analysis and systematic review abstracts regarding the treatment of ACL injuries with quadriceps tendon graft. Study Design: Cross-sectional study. Methods: Electronic libraries (MEDLINE, Embase, Web of Science, Google Scholar) were searched for meta-analyses and systematic reviews regarding the treatment of ACL tears with quadriceps tendon graft. The 9 most severe types of spin commonly found in abstracts were used as an evaluation tool to assess the articles. Two reviewers each performed a blinded assessment of each article for spin. A third reviewer helped after review was done to address any discrepancies between the original reviewers. Further evaluation included year of publication, number of citations, journal impact factor, and A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2) score. Results: The electronic database search resulted in 986 articles, of which 13 met our inclusion criteria. After review, we found that 53.8% (7/13) of the included articles contained 1 of the 9 most severe forms of spin. Of the 13 articles, 15.4% (n = 2) contained 2 types of spin, and 38.5% (n = 5) contained 1 type of spin. No studies contained ≥3 types of spin. Of the types of spin evaluated, the most prevalent (n = 4; 30.8%) was type 3 (“selective reporting of or overemphasis on efficacy outcomes or analysis favoring the beneficial effect of the experimental intervention”). All studies, regardless of the presence of spin, were found to be low or critically low quality according to the AMSTAR-2 assessment. Conclusion: This study demonstrated the presence of spin in 53.8% of meta-analysis and systematic review abstracts pertaining to quadriceps tendon graft for ACL reconstruction. Orthopaedic surgeons should learn to recognize spin as they review articles when deciding the treatment course for ACL injuries. Additionally, strict criteria should be considered to reduce the prevalence of spin in orthopaedic literature.