Miracle-dz/UniFAR-FTUs huggingface dataset:
A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS). In this paper, we take a closer look at PSS and propose three fundamental improvements over current methods. The segmentation task is critical, as generating a correct summary requires each step of the procedure to be correctly identified. However, current segmentation metrics often overestimate the segmentation quality because they do not consider the temporal order of segments. In our first contribution, we propose a new segmentation metric that takes into account the order of segments, giving a more reliable measure of the accuracy of a given predicted segmentation. Current PSS methods are typically trained by proposing segments, matching them with the ground truth and computing a loss. However, much like segmentation metrics, existing matching algorithms do not consider the temporal order of the mapping between candidate segments and the ground truth. In our second contribution, we propose a matching algorithm that constrains the temporal order of segment mapping, and is also differentiable. Lastly, we introduce multi-modal feature training for PSS, which further improves segmentation. We evaluate our approach on two instructional video datasets (YouCook2 and Tasty) and observe an improvement over the state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and summarization, respectively.
Nonautonomous discrete rogue waves and interaction in the generalized Ablowitz-Ladik-Hirota lattice with variable coefficients

We analytically investigate the nonautonomous discrete rogue wave solutions and their interaction in the generalized Ablowitz-Ladik-Hirota lattice with variable coefficients, which possess complicated wave propagations in time and are beyond the usual discrete rogue waves. When the amplitude of the tunnel coupling coefficient between sites decreases, these nonautonomous discrete rogue wave solutions become localized in time after they propagate over some certain large critical values. Moreover, we find that the interaction between nonautonomous discrete rogue waves is elastic. In particular, these results can reduce to the usual discrete rogue wave solutions when the gain or loss term is ignored.
Reverse circular Bragg phenomenon

The axial propagation of circularly polarized light in an optically active structurally chiral medium is exactly solved via full electromagnetic analysis. Some symmetries of the system's characteristic matrix reveal new insights, which are confirmed by coupled wave theory. For extreme values of chirality, now accessible via metamaterials, a reverse circular Bragg resonance arises in the negative refraction regime where handedness reversal of counterpart modes occurs. A condition is identified under which optical activity offsets structural chirality, rendering the medium simply birefringent.
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds

Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case. The likelihood of a match can be for instance decreased by setting a high threshold in case of a payment transaction veriﬁcation. Prior work in face recognition has unfortunately showed that error rates are usually higher for certain demographic groups. These disparities have hence brought into question the fairness of systems empowered with face biometrics. In this paper, we investigate the extent to which disparities among demographic groups change under di ﬀ erent security levels. Our analysis includes ten face recognition models, three security thresholds, and six demographic groups based on gender and ethnicity. Experiments show that the higher the security of the system is, the higher the disparities in usability among demographic groups are. Compelling unfairness issues hence exist and urge countermeasures in real-world high-stakes environments requiring severe security levels.
Sparse Mixture-of-Experts are Domain Generalizable Learners

Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) aims to close this gap, with existing DG methods mainly focusing on the loss function design. In this paper, we propose to explore an orthogonal direction, i.e., the design of the backbone architecture. It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-of-the-art (SOTA) DG algorithms on multiple DG datasets. We develop a formal framework to characterize a network's robustness to distribution shifts by studying its architecture's alignment with the correlations in the dataset. This analysis guides us to propose a novel DG model built upon vision transformers, namely Generalizable Mixture-of-Experts (GMoE). Extensive experiments on DomainBed demonstrate that GMoE trained with ERM outperforms SOTA DG baselines by a large margin. Moreover, GMoE is complementary to existing DG methods and its performance is substantially improved when trained with DG algorithms.
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors. Recent research has developed a variety of camera-only methods, where features are differentiably “lifted” from the multi-camera images onto the 2D ground plane, yielding a “bird's eye view” (BEV) feature representation of the 3D space around the vehicle. This line of work has produced a variety of novel “lifting” methods, but we observe that other details in the training setups have shifted at the same time, making it unclear what really matters in top-performing methods. We also observe that using cameras alone is not a real-world constraint, considering that additional sensors like radar have been integrated into real vehicles for years already. In this paper, we first of all attempt to elucidate the high-impact factors in the design and training protocol of BEV perception models. We find that batch size and input resolution greatly affect performance, while lifting strategies have a more modest effect-even a simple parameter-free lifter works well. Second, we demonstrate that radar data can provide a substantial boost to performance, helping to close the gap between camera-only and LiDAR-enabled systems. We analyze the radar usage details that lead to good performance, and invite the community to re-consider this commonly-neglected part of the sensor platform.
TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs

We propose TopoOpt, a novel direct-connect fabric for deep neural network (DNN) training workloads. TopoOpt co-optimizes the distributed training process across three dimensions: computation, communication, and network topology. We demonstrate the mutability of AllReduce traffic, and leverage this property to construct efficient network topologies for DNN training jobs. TopoOpt then uses an alternating optimization technique and a group theory-inspired algorithm called TotientPerms to find the best network topology and routing plan, together with a parallelization strategy. We build a fully functional 12-node direct-connect prototype with remote direct memory access (RDMA) forwarding at 100 Gbps. Large-scale simulations on real distributed training models show that, compared to similar-cost Fat-Tree interconnects, TopoOpt reduces DNN training time by up to 3.4x.
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation

Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to a student network learning from another modality. In contrast to the empirical success reported in prior works, the working mechanism of crossmodal KD remains a mystery. In this paper, we present a thorough understanding of crossmodal KD. We begin with two case studies and demonstrate that KD is not a universal cure in crossmodal knowledge transfer. We then present the modality Venn diagram to understand modality relationships and the modality focusing hypothesis revealing the decisive factor in the efficacy of crossmodal KD. Experimental results on 6 multimodal datasets help justify our hypothesis, diagnose failure cases, and point directions to improve crossmodal knowledge transfer in the future.
FixEval: Execution-based Evaluation of Program Fixes for Programming Problems

The complexity of modern software has led to a drastic increase in the time and cost associated with detecting and rectifying software bugs. In response, researchers have explored various methods to automatically generate fixes for buggy code. However, due to the large combinatorial space of possible fixes for any given bug, few tools and datasets are available to evaluate model-generated fixes effectively. To address this issue, we introduce FIXEvAL, a benchmark comprising of buggy code submissions to competitive programming problems and their corresponding fixes. FixeValoffers an extensive collection of unit tests to evaluate the correctness of model-generated program fixes and assess further information regarding time, memory constraints, and acceptance based on a verdict. We consider two Transformer language models pretrained on programming languages as our baseline and compare them using match-based and execution-based evaluation metrics. Our experiments show that match-based metrics do not reflect model-generated program fixes accurately. At the same time, execution-based methods evaluate programs through all cases and scenarios designed explicitly for that solution. Therefore, we believe FixeValprovides a step towards real-world automatic bug fixing and model-generated code evaluation. The dataset and models are open-sourced.11https://github.com/FixEval/FixEval_official
Magnetic field models

Magnetic field models for Saturn based on data obtained by the Pioneer 11, Voyager 1, and Voyager 2 spacecraft are discussed. Saturn is distinguished form earth and Jupiter by the symmetry of its internal field about its axis of rotation; however, the strong periodic modulation of Saturn's radio emission is strong evidence of some departure from axial symmetry. Analysis of energetic charged particle absorption signatures observed by Pioneer and Voyager spacecraft are consistent with axisymmetric models of Saturn's magnetic field and demonstrate that any equatorial displacement of the dipole is limited to less than 0.01 R(S). Saturn's magnetosphere appears to be intermediate in configuration to those of earth and Jupiter. An equatorial ring current of about 10 million A, confined to a 5 R(S) thick annulus with inner and outer radii of 8 and 16 R(S), has a major effect on the geometry of the outer magnetosphere. Field lines are moderately but measurably stretched out in the equatorial plane.
Efficacy of indwelling trocar therapy for postherpetic neuralgia: A retrospective study of an acupuncture method

Postherpetic neuralgia (PHN) is a common complication of herpes zoster. As a kind of continuous acupuncture, indwelling trocar therapy (ITT) involves inserting a trocar into the skin and retaining the soft cannula in the body for 24 h. However, the efficacy and safety of ITT on PHN require further verification. In this study, the medical records of 122 patients with PHN were retrospectively analyzed. Patients were divided into the control group (patients who received conventional drug therapy) and the ITT group (patients who underwent ITT combined with conventional drug therapy). The Visual Analog Scale (VAS), Quality of Sleep (QS), 36‐Item Short Form Health Survey (SF‐36), dosage of drug and adverse events were analyzed at days 1, 3, 7, 14, 28, 90, and 180 after treatment. The total efficiency rate (TER) was analyzed after 6 months of follow‐up. The VAS, QS and SF‐36 scores in the ITT group improved substantially compared with those in the control group after 6 months of follow‐up (p < 0.001). The average dosage of anticonvulsants and analgesics decreased significantly in the ITT group (p < 0.001). The TER in the control group was 52.46%, compared with 73.77% in the ITT group (p < 0.05). There were no adverse events, such as bleeding and infection, observed in the ITT group. For PHN patients, the combination of ITT and medicine therapy reduced VAS, improved quality of life, increased the efficiency rate, remarkably reduced the dosage of traditional medicine, and had no significant side effects. In addition, ITT was more effective in patients with a short duration of PHN than in chronic PHN patients.
Origin, production and molecular determinants of macrophages for their therapeutic targeting

Macrophages, the most heterogeneous cells of the hematopoietic system and the giant eaters of the immune system that present either as tissue‐resident cells or infiltrated immune cells, eliminate foreign pathogens and microbes and also play different physiological roles to maintain the body's immune response. In this review, we basically provide a broad overview of macrophages from their origin, functional diversity to M1‐M2 polarization, specialized markers, and their role as important therapeutic targets in different diseases based on the current research and evidence. Apart from this, we have precisely discussed about tumor‐associated macrophages (TAMs) and their role in tumor progression and newly discovered lesser‐known markers of TAMs that could be used as potential therapeutic targets to treat life‐threatening diseases. It is really very important to understand the diversity of macrophages to develop TAM‐modulating strategies to activate our own immune system against diseases and to overcome immune resistance.
Towards an understanding of the pathological basis of senile depression and incident dementia: Implications for treatment

Senile depression (SD) is a heterogeneous syndrome. Several clinical profiles are more likely to appear in SD than in early‐life depression, but it remains unclear whether the pathophysiology is different. The prevalence of dementia increases with aging, and the underlying pathophysiological processes in the preclinical phase begin even before cognitive deficits or neurological signs appear. SD may be either a risk factor for developing dementia or a prodromal stage of dementia. The inconsistent findings regarding the association between SD and incident dementia may be attributable to the neuropathological heterogeneity underlying SD. Most studies have focused on patients with the clinical diagnosis of Alzheimer disease (AD) as an outcome, but several clinicopathological studies suggest that primary age‐related tauopathy and argyrophilic grain disease may account for a proportion of cases clinically misdiagnosed as AD in the elderly population. Furthermore, most AD cases have additional neuropathologic changes such as cerebrovascular disease and Lewy body disease. Here, we review the neuropathological findings linking SD to incident dementia, focusing on common age‐related neuropathologies. In particular, the roles of disturbance of neural circuity, imbalance of monoaminergic systems, dysregulation of the hypothalamic–pituitary–adrenal axis, and elevated neuroinflammatory status are discussed. Finally, we review the current treatment of SD in the context of age‐related neuropathological changes.
The XMM deep survey in the CDF-S: II. A 9-20 keV selection of heavily obscured active galaxies at z > 1.7

We present results on a search of heavily obscured active galaxies z > 1.7 using the rest-frame 9−20 keV excess for X-ray sources detected in the deep XMM-CDFS survey. Out of 176 sources selected with the conservative detection criteria (>8σ) in the first source catalogue of Ranalli et al. (in prep.), 46 objects lie in the redshift range of interest with the median redshift ˜ � 2.5. Their typical rest-frame 10−20 keV luminosity is 10 44 erg s −1 , as observed. Among optically faint objects that lack spectroscopic redshift, four were found to be strongly absorbed X-ray sources, and the enhanced Fe K emission or absorption features in their X-ray spectra were used to obtain X-ray spectroscopic redshifts. Using the X-ray colour–colour diagram based on the rest-frame 3− 5k eV, 5− 9k eV, and 9−20 keV bands, seven objects were selected for their 9−20 keV excess and were found to be strongly absorbed X-ray sources with column density of NH ≥ 0.6 × 10 24 cm −2 , including two possible Compton thick sources. While they are emitting at quasar luminosity, ∼3/4 of the sample objects are found to be absorbed by NH > 10 22 cm −2 . A comparison with local AGN at the matched luminosity suggests an increasing trend of the absorbed source fraction for high-luminosity AGN towards high redshifts.
Exosomes as novel player in dermatologic field

Exosomes are nano‐sized extracellular vesicles released by the majority of the cell types. Exosomes play a major role in intercellular communication via transferring cargoes between cells and altering specific functions of the target cells. The interest in the biological activities of exosomes has been increasing and their therapeutic role has already been demonstrated in various diseases. Recently, there is growing evidence that stem cell‐derived exosomes (including mesenchymal stem cell, umbilical cord‐derived mesenchymal cell, adipose‐derived stem cell, and pluripotent stem cell) can also be used in a variety of skin conditions due to their regenerative and anti‐inflammatory capacity. In this paper, we will provide a brief overview of recent studies on practical applications of exosomes in several dermatologic conditions and their potential mechanisms. By elucidating the different mechanisms and therapeutic roles of exosomes in various disease conditions, we hope dermatologists and other clinicians to establish better strategies for disease treatment with further research.
Nodeless multiband superconductivity in stoichiometric single-crystalline CaKFe4As4

Measurements of the London penetration depth Δλ(T) and tunneling conductance in single crystals of the recently discovered stoichiometric iron-based superconductor CaKFe4As4 (CaK1144) show nodeless, two-effective-gap superconductivity with a larger gap of about 6–10 meV and a smaller gap of about 1–4 meV. Having a critical temperature Tc,onset ≈ 35.8 K, this material behaves similar to slightly overdoped (Ba1–xKx)Fe2As2 (e.g., x = 0.54,Tc ≈ 34 K), a known multigap s± superconductor. Here, we conclude that the superconducting behavior of stoichiometric CaK1144 demonstrates that two-gap s± superconductivity is an essential property of high-temperature superconductivity in iron-based superconductors, independent of the degree of substitutional disorder.
Focal cortical dysplasia pathology: diagnostic difficulty, classification, and utility for pathogenesis.

OBJECTIVE In the histopathological examination of treatment-resistant epilepsy, focal cortical dysplasia (FCD) is the most common diagnosis in the pediatric group. FCD is classified histopathologically according to the International League Against Epilepsy (ILAE) classification. In the last decade since the ILAE classification has been released, molecular genetic studies have revealed mTOR pathway-related mutations as a major etiology. The objective of this study was to determine the incidence of FCD in treatment-resistant epilepsy patients, explore histomorphological and immunohistochemical features, examine clinicopathological correlation, demonstrate mTOR pathway activation using a pS6 antibody immunohistochemically, and try to introduce a candidate for possible targeted therapies. METHODS Paraffin blocks and slides of tissue from patients with treatment-resistant epilepsy were reexamined retrospectively. Histopathological subtypes of FCD were determined according to the ILAE classification. NeuN and neurofilament H (NF-H) staining were performed, and additionally a pS6 antibody was used to demonstrate mTOR pathway activation. RESULTS In 32 cases diagnosed with FCD, or 17.5% of 183 surgical epilepsy materials, there were no significant differences in the statistical analysis of clinical variables between the ILAE FCD subtypes. Recommended antibody NeuN revealed microcolumnar alignment in the FCD type Ia and IIIa groups and the loss of lamination in the type Ib group. Another recommended antibody, NF-H, was not found to be useful in discriminating between normal and dysmorphic neurons. pS6 expression, showing mTOR pathway activation, was observed in dysmorphic neurons and balloon cells in all FCD type II cases. CONCLUSIONS Significant pS6 expression in FCD type II represents the genomic nature of the disease noted in the literature. Nevertheless, the known MTOR gene and mTOR pathway-related mutations remain behind proportionally to explain the mTOR pathway activation in all FCD type II cases. Clinicopathologically and genetically integrated classification and usage of mTOR pathway inhibitors in treatment are expected as a recent evolution.
The diagnostic and prognostic value of pseudogene SIGLEC17P in lung adenocarcinoma and a preliminary functional study

Among malignant tumors, lung adenocarcinoma (LUAD) is the leading cause of death worldwide. This study explored the diagnostic, prognostic value, and preliminary functional verification of sialic acid binding Ig like lectin 17, pseudogene (SIGLEC17P) in LUAD. Prognostic lncRNAs for LUAD were identified by The Cancer Genome Atlas and quantitative real‐time PCR (qRT‐PCR) was used to detect the expression of SIGLEC17P in LUAD and paracarcinoma tissues. Subsequently, lentiviral vectors were used to overexpress SIGLEC17P in A549 and H1299 cells. The effects of SIGLEC17P overexpression on the proliferation, migration, and invasiveness of LUAD cells (A549 and H1299) were evaluated by Cell Counting Kit‐8, wound healing, and transwell migration assays, respectively. Bioinformatics analyses were performed to reveal the potential pathways in which SIGLEC17P is involved in LUAD. qRT‐PCR results revealed low SIGLEC17P expression in LUAD tissues and a significant association with the N stage, T stage, and tumor node metastasis stage. Furthermore, the receiver operating characteristic curve demonstrated a reliable diagnostic value. The proliferation, migration, and invasion of LUAD cells were inhibited by overexpression of SIGLEC17P. Bioinformatics analyses suggested that SIGLEC17P might exert antioncogenic effects in LUAD through the mir‐20‐3p/ADH1B or mir‐4476‐5p/DPYSL axis. In summary, our results revealed that SIGLEC17P acts as a prognostic biomarker, independent prognostic factor, and potential therapeutic target for patients with LUAD.