Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Pyramid Network for Action Recognition

Visual tempo characterizes the dynamics and the temporal scale of an action. Modeling such visual tempos of different actions facilitates their recognition. Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle. In this work we propose a generic Temporal Pyramid Network (TPN) at the feature-level, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner. Two essential components of TPN, the source of features and the fusion of features, form a feature hierarchy for the backbone so that it can capture action instances at various tempos. TPN also shows consistent improvements over other challenging baselines on several action recognition datasets. Specifically, when equipped with TPN, the 3D ResNet-50 with dense sampling obtains a 2\% gain on the validation set of Kinetics-400. A further analysis also reveals that TPN gains most of its improvements on action classes that have large variances in their visual tempos, validating the effectiveness of TPN.
Exemplar Auditing for Multi-Label Biomedical Text Classification

Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than that of the available human labels. This is often the scenario faced in healthcare applications as coarse, high-level labels (e.g., billing codes) are often the only sources that are readily available. These challenges are compounded for modalities such as text, where the feature space is very high-dimensional, and often contains considerable amounts of noise. In this work, we generalize a recently proposed zero-shot sequence labeling method, "binary labeling via a convolutional decomposition", to the case where the available document-level human labels are themselves relatively high-dimensional. The approach yields classification with "introspection", relating the fine-grained features of an inference-time prediction to their nearest neighbors from the training set, under the model. The approach is effective, yet parsimonious, as demonstrated on a well-studied MIMIC-III multi-label classification task of electronic health record data, and is useful as a tool for organizing the analysis of neural model predictions and high-dimensional datasets. Our proposed approach yields both a competitively effective classification model and an interrogation mechanism to aid healthcare workers in understanding the salient features that drive the model's predictions.
A new polynomial criterion for periodic knots

The purpose of this paper is to present a new periodicity criterion. For that purpose, we study the HOMFLY-PT polynomial and the Kauffman polynomial of cables of periodic links. Furthermore, we exhibit a couple of examples for which our criterion is stronger than many previously know criteria, among which is the Khovanov homology criterion of Borodzik and the second author.
Orthogonal pulsars as a key test for pulsar evolution

At present, there is no direct information about evolution of inclination angle χ between magnetic and rotational axes in radio pulsars. As to theoretical models of pulsar evolution, they predict both the alignment, i.e. evolution of inclination angle χ to 0°, and its counter-alignment, i.e. evolution to 90°. In this paper, we demonstrate that the statistics of interpulse pulsars can give us the key test to solve the alignment/counter-alignment problem as the number of orthogonal interpulse pulsars (χ ≈ 90°) drastically depends on the evolution trajectory.
Vulnerabilities Mapping based on OWASP-SANS: a Survey for Static Application Security Testing (SAST)

The delivery of a framework in place for secure application development is of real value for application development teams to integrate security into their development life cycle, especially when a mobile or web application moves past the scanning stage and focuses increasingly on the remediation or mitigation phase based on static application security testing (SAST). For the first time, to the author’s knowledge, the industry-standard Open Web Application Security Project (OWASP) top 10 vulnerabilities and CWE/SANS top 25 most dangerous software errors are synced up in a matrix with Checkmarx vulnerability queries, producing an application security framework that helps development teams review and address code vulnerabilities, minimise false positives discovered in static scans and penetration tests, targeting an increased accuracy of the findings. A case study is conducted for vulnerabilities scanning of a proof-of-concept mobile malware detection app. Mapping the OWASP/SANS with Checkmarx vulnerabilities queries, flaws and vulnerabilities are demonstrated to be mitigated with improved efficiency.
Model-Agnostic Characterization of Fairness Trade-offs

There exist several inherent trade-offs in designing a fair model, such as those between the model's predictive performance and fairness, or even among different notions of fairness. In practice, exploring these trade-offs requires significant human and computational resources. We propose a diagnostic that enables practitioners to explore these trade-offs without training a single model. Our work hinges on the observation that many widely-used fairness definitions can be expressed via the fairness-confusion tensor, an object obtained by splitting the traditional confusion matrix according to protected data attributes. Optimizing accuracy and fairness objectives directly over the elements in this tensor yields a data-dependent yet model-agnostic way of understanding several types of trade-offs. We further leverage this tensor-based perspective to generalize existing theoretical impossibility results to a wider range of fairness definitions. Finally, we demonstrate the usefulness of the proposed diagnostic on synthetic and real datasets.
5G Radio Access Network Architecture for Terrestrial Broadcast Services

The 3rd Generation Partnership Project (3GPP) has defined based on the Long Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a set of new features to support the distribution of Terrestrial Broadcast services in Release 14. On the other hand, a new 5th Generation (5G) system architecture and radio access technology, 5G New Radio (NR), are being standardized from Release 15 onwards, which so far have only focused on unicast connectivity. This may change in Release 17 given a new Work Item set to specify basic Radio Access Network (RAN) functionalities for the provision of multicast/broadcast communications for NR. This work initially excludes some of the functionalities originally supported for Terrestrial Broadcast services under LTE, e.g., free to air, receive-only mode, large-area single frequency networks, etc. This paper proposes an enhanced Next Generation RAN architecture based on 3GPP Release 15 with a series of architectural and functional enhancements, to support an efficient, flexible and dynamic selection between unicast and multicast/broadcast transmission modes and also the delivery of Terrestrial Broadcast services. The paper elaborates on the Cloud-RAN based architecture and proposes new concepts such as the RAN Broadcast/Multicast Areas that allows a more flexible deployment in comparison to eMBMS. High-level assessment methodologies including complexity analysis and inspection are used to evaluate the feasibility of the proposed architecture design and compare it with the 3GPP architectural requirements.
Interview: A Large-Scale Open-Source Corpus of Media Dialog

Existing conversational datasets consist either of written proxies for dialog or small-scale transcriptions of natural speech. We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts. Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance on existing spoken dialog datasets, demonstrating its usefulness in modeling real-world conversations. 'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems. In fact, experiments on two dialog tasks show that leveraging such labels improves performance over strong speaker-agnostic baselines, and enabling models to generate more specific and inquisitive responses in interview-style conversations.
Testing Pre-trained Transformer Models for Lithuanian News Clustering

A recent introduction of Transformer deep learning architecture made breakthroughs in various natural language processing tasks. However, non-English languages could not leverage such new opportunities with the English text pre-trained models. This changed with research focusing on multilingual models, where less-spoken languages are the main beneficiaries. We compare pre-trained multilingual BERT, XLM-R, and older learned text representation methods as encodings for the task of Lithuanian news clustering. Our results indicate that publicly available pre-trained multilingual Transformer models can be fine-tuned to surpass word vectors but still score much lower than specially trained doc2vec embeddings.
Determination of K4000 of potential blazar candidates among EGRET unidentified gamma-ray sources

Abstract Blazars are radio-loud Active Galactic Nuclei (AGN) with relativistic jets oriented towards the observer’s line-of-sight. Based on their optical spectra, blazars may be classified as flat-spectrum radio quasars (FSRQs) or BL Lacs. FSRQs are more luminous blazars with both narrow and broad emission and absorption lines, while BL Lacs are less luminous and featureless. Recent studies show that blazars dominate (˜93%) the already-identified EGRET sources (142), suggesting that among the unidentified sources (129) there could still be faint blazars. Due to the presence of a strong non-thermal component inside their jets, blazars are found to display a weaker depression at ˜4000 Å (K 4000 ≤ 0.4). In this study, we aimed at determining the K 4000 break for a selected sample among the potential blazar candidates from unidentified EGRET sources to confirm their blazar nature. We used two blazar candidates, 3EG J1800-0146 and 3EG J1709-0817 associated with radio counterparts, J1802-0207 and J1713-0817, respectively. Their optical counterparts were obtained through spectroscopic observations using Robert Stobie spectrograph (RSS) at the Southern African Large Telescope (SALT) in South Africa. The observed Ca II H & K lines depression at ˜4000 Å in spectra of these sources show a shallow depression, K 4000 = 0.35 ± 0.02 and 0.24 ± 0.01, respectively, suggesting that these sources are blazar candidates. Moreover, the redshifts z = 0.165 and 0.26 measured in their spectra confirm the extragalactic nature of these sources.
An upper limit on the initial temperature of the radiation-dominated Universe

Gravitational waves (GWs) are produced by colliding particles through the gravitational analogue of electromagnetic bremsstrahlung. We calculate the contribution of free-free emission in the radiation-dominated Universe to the stochastic GW background. We find that the energy density of the resulting GW radiation is heavily dependent on the number of elementary particles, Ntot, and the maximum initial temperature, Tmax. We rule out Ntot ≳ 102 × NSM for Tmax ∼ TPlanck ≈ 1019 GeV and Ntot ≳ 1016 × NSM for Tmax ∼ 1016 GeV, where NSM is the number of particles in the Standard Model. In the case of inflation, existing cosmological data constrain Tmax ≲ 1016 GeV . However, alternative models to inflation such as bouncing cosmologies allow for Tmax near TPlanck. At the energy scales we are considering, the extra number of particles arise naturally in models of extra dimensions.
The CARMA–NRO Orion Survey: Protostellar Outflows, Energetics, and Filamentary Alignment

We identify 45 protostellar outflows in CO maps of the Orion A giant molecular cloud from the Combined Array for Research in Millimeter-wave Astronomy–Nobeyama Radio Observatory Orion survey. Our sample includes 11 newly detected outflows. We measure the mass and energetics of the outflows, including material at low velocities, by correcting for cloud contributions. The total momentum and kinetic energy injection rates of outflows are comparable to the turbulent dissipation rate of the cloud. We also compare the outflow position angles to the orientation of C18O filaments. We find that the full sample of outflows is consistent with being randomly oriented with respect to the filaments. A subsample of the most reliable measurements shows a moderately perpendicular outflow-filament alignment that may reflect accretion of mass across filaments and onto the protostellar cores.
Approximating Min-Mean-Cycle for low-diameter graphs in near-optimal time and memory

We revisit Min-Mean-Cycle, the classical problem of finding a cycle in a weighted directed graph with minimum mean weight. Despite an extensive algorithmic literature, previous work falls short of a near-linear runtime in the number of edges $m$--in fact, there is a natural barrier which precludes such a runtime for solving Min-Mean-Cycle exactly. Here, we give a much faster approximation algorithm that, for graphs with polylogarithmic diameter, has near-linear runtime. In particular, this is the first algorithm whose runtime for the complete graph scales in the number of vertices $n$ as $\tilde{O}(n^2)$. Moreover--unconditionally on the diameter--the algorithm uses only $O(n)$ memory beyond reading the input, making it "memory-optimal". The algorithm is also simple to implement and has remarkable practical performance. Our approach is based on solving a linear programming (LP) relaxation using entropic regularization, which effectively reduces the LP to a Matrix Balancing problem--a la the popular reduction of Optimal Transport to Matrix Scaling. We then round the fractional LP solution using a variant of the classical Cycle-Cancelling algorithm that is sped up to near-linear runtime at the expense of being approximate, and implemented in a memory-optimal manner. We also provide an alternative algorithm with slightly faster theoretical runtime, albeit worse memory usage and practicality. This algorithm uses the same rounding procedure, but solves the LP relaxation by leveraging recent developments in area-convexity regularization. Its runtime scales inversely in the approximation accuracy, which we show is optimal--barring a major breakthrough in algorithmic graph theory, namely faster Shortest Paths algorithms.
Usable & Scalable Learning Over Relational Data With Automatic Language Bias.

Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. We propose AutoBias, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning systems. We show that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the running time of the learning algorithm.
3d Dynamic Point Cloud Inpainting Via Temporal Consistency On Graphs

With the development of 3D laser scanning techniques and depth sensors, 3D dynamic point clouds have attracted increasing attention as a representation of 3D objects in motion, enabling various applications such as 3D immersive tele-presence, gaming and navigation. However, dynamic point clouds usually exhibit holes of missing data, mainly due to the fast motion, the limitation of acquisition and complicated structure. Leveraging on graph signal processing tools, we represent irregular point clouds on graphs and propose a novel inpainting method exploiting both intra-frame self-similarity and inter-frame consistency in 3D dynamic point clouds. Specifically, for each missing region in every frame of the point cloud sequence, we search for its self-similar regions in the current frame and corresponding ones in adjacent frames as references. Then we formulate dynamic point cloud inpainting as an optimization problem based on the two types of references, which is regularized by a graph-signal smoothness prior. Experimental results show the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.
Robust Self-Supervised Convolutional Neural Network for Subspace Clustering and Classification

Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and classification problems. This paper proposes the robust formulation of the self-supervised convolutional subspace clustering network ($S^2$ConvSCN) that incorporates the fully connected (FC) layer and, thus, it is capable for handling out-of-sample data by classifying them using a softmax classifier. $S^2$ConvSCN clusters data coming from nonlinear manifolds by learning the linear self-representation model in the feature space. Robustness to data corruptions is achieved by using the correntropy induced metric (CIM) of the error. Furthermore, the block-diagonal (BD) structure of the representation matrix is enforced explicitly through BD regularization. In a truly unsupervised training environment, Robust $S^2$ConvSCN outperforms its baseline version by a significant amount for both seen and unseen data on four well-known datasets. Arguably, such an ablation study has not been reported before.
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents

The demand for more transparency of decision-making processes of deep reinforcement learning agents is greater than ever, due to their increased use in safety critical and ethically challenging domains such as autonomous driving. In this empirical study, we address this lack of transparency following an idea that is inspired by research in the field of neuroscience. We characterize the learned representations of an agent's policy network through its activation space and perform partial network ablations to compare the representations of the healthy and the intentionally damaged networks. We show that the healthy agent's behavior is characterized by a distinct correlation pattern between the network's layer activation and the performed actions during an episode and that network ablations, which cause a strong change of this pattern, lead to the agent failing its trained control task. Furthermore, the learned representation of the healthy agent is characterized by a distinct pattern in its activation space reflecting its different behavioral stages during an episode, which again, when distorted by network ablations, leads to the agent failing its trained control task. Concludingly, we argue in favor of a new perspective on artificial neural networks as objects of empirical investigations, just as biological neural systems in neuroscientific studies, paving the way towards a new standard of scientific falsifiability with respect to research on transparency and interpretability of artificial neural networks.
Aerosol characterization using satellite remote sensing of light pollution sources at night

A demanding challenge in atmospheric research is the night-time characterization of aerosols using passive techniques, that is, by extracting information from scattered light that has not been emitted by the observer. Satellite observations of artificial night-time lights have been used to retrieve some basic integral parameters, like the aerosol optical depth. However, a thorough analysis of the scattering processes allows one to obtain substantially more detailed information on aerosol properties. In this Letter we demonstrate a practicable approach for determining the aerosol particle size number distribution function in the air column, based on the measurement of the angular radiance distribution of the scattered light emitted by night-time lights of cities and towns, recorded from low Earth orbit. The method is self-calibrating and does not require the knowledge of the absolute city emissions. The input radiance data are readily available from several spaceborne platforms, like the VIIRS-DNB radiometer onboard the Suomi-NPP satellite.
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation

Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a Latent Disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a Counterfactual Generation to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.
Variable Autonomy of Whole-body Control for Inspection and Intervention in Industrial Environments using Legged Robots

The deployment of robots in industrial and civil scenarios is a viable solution to protect operators from danger and hazards. Shared autonomy is paramount to enable remote control of complex systems such as legged robots, allowing the operator to focus on the essential tasks instead of overly detailed execution. To realize this, we propose a comprehensive control framework for inspection and intervention using a legged robot and validate the integration of multiple loco-manipulation algorithms optimised for improving the remote operation. The proposed control offers 3 operation modes: fully automated, semi-autonomous, and the haptic interface receiving onsite physical interaction for assisting teleoperation. Our contribution is the design of a QP-based semi-analytical whole-body control, which is the key to the various task completion subject to internal and external constraints. We demonstrate the versatility of the whole-body control in terms of decoupling tasks, singularity tolerance and constraint satisfaction. We deploy our solution in field trials and evaluate in an emergency setting by an E-stop while the robot is clearing road barriers and traversing difficult terrains.