Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning

In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimality gaps, which we call cascaded gaps, and we discuss their key components that adapt to the underlying structures of the problem. Based on the cascaded gaps, we derive non-asymptotic and logarithmic regret bounds for two model-free algorithms under episodic Markov decision processes. We show that, in appropriate settings, these bounds feature exponential improvement over existing ones that are independent of gaps. We also prove gap-dependent lower bounds, which certify the near optimality of the upper bounds.
Multiline Assessment of Narrow-line Regions in z ∼ 3 Radio Galaxies

In this paper, we use high-quality rest-UV spectra of three radio galaxies at z ∼ 3 observed with the FORS2 camera on the Very Large Telescope to measure the flux of several emission lines, including relatively faint ones, such as N iv]λ1486, O iii]λ1663, and [Ne iv]λ2424. Additionally, we collect fluxes of faint rest-UV emission lines in 12 z ∼ 3 radio galaxies from the literature. Previously, physical and chemical properties of narrow-line regions (NLRs) in high-z active galactic nuclei (AGNs) have been investigated mostly by using only strong rest-UV emission lines (e.g., N v λ1240, C iv λ1549, He ii λ1640, and C iii]λ1909). Such strong-line diagnostics are based on various assumptions due to the limitation in the number of available emission-line constraints. In this work, both physical and chemical properties of NLR clouds in each object are estimated by fitting detailed photoionization models to the measured emission-line fluxes. We confirm that the metallicity of NLRs in AGNs at z ∼ 3 is solar or supersolar, without assumptions about the gas density and ionization parameter thanks to the constraints from the faint emission lines. This result suggests that high-z radio galaxies are already chemically mature at z ∼ 3.
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs

Sparse linear regression with ill-conditioned Gaussian random designs is widely believed to exhibit a statistical/computational gap, but there is surprisingly little formal evidence for this belief, even in the form of examples that are hard for restricted classes of algorithms. Recent work has shown that, for certain covariance matrices, the broad class of Preconditioned Lasso programs provably cannot succeed on polylogarithmically sparse signals with a sublinear number of samples. However, this lower bound only shows that for every preconditioner, there exists at least one signal that it fails to recover successfully. This leaves open the possibility that, for example, trying multiple different preconditioners solves every sparse linear regression problem. In this work, we prove a stronger lower bound that overcomes this issue. For an appropriate covariance matrix, we construct a single signal distribution on which any invertibly-preconditioned Lasso program fails with high probability, unless it receives a linear number of samples. Surprisingly, at the heart of our lower bound is a new positive result in compressed sensing. We show that standard sparse random designs are with high probability robust to adversarial measurement erasures, in the sense that if $b$ measurements are erased, then all but $O(b)$ of the coordinates of the signal are still information-theoretically identifiable. To our knowledge, this is the first time that partial recoverability of arbitrary sparse signals under erasures has been studied in compressed sensing.
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking

Dialogue state tracking (DST) aims to extract essential information from multi-turn dialog situations and take appropriate actions. A belief state, one of the core pieces of information, refers to the subject and its specific content, and appears in the form of domain-slot-value. The trained model predicts “accumulated” belief states in every turn, and joint goal accuracy and slot accuracy are mainly used to evaluate the prediction; however, we specify that the current evaluation metrics have a critical limitation when evaluating belief states accumulated as the dialogue proceeds, especially in the most used MultiWOZ dataset. Additionally, we propose relative slot accuracy to complement existing metrics. Relative slot accuracy does not depend on the number of predefined slots, and allows intuitive evaluation by assigning relative scores according to the turn of each dialog. This study also encourages not solely the reporting of joint goal accuracy, but also various complementary metrics in DST tasks for the sake of a realistic evaluation.
Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answering

Knowledge-based visual question answering (VQA) is a vision-language task that requires an agent to correctly answer image-related questions using knowledge that is not presented in the given image. It is not only a more challenging task than regular VQA but also a vital step towards building a general VQA system. Most existing knowledge-based VQA systems process knowledge and image information similarly and ignore the fact that the knowledge base (KB) contains complete information about a triplet, while the extracted image information might be incomplete as the relations between two objects are missing or wrongly detected. In this paper, we propose a novel model named dynamic knowledge memory enhanced multi-step graph reasoning (DMMGR), which performs explicit and implicit reasoning over a key-value knowledge memory module and a spatial-aware image graph, respectively. Specifically, the memory module learns a dynamic knowledge representation and generates a knowledge-aware question representation at each reasoning step. Then, this representation is used to guide a graph attention operator over the spatial-aware image graph. Our model achieves new state-of-the-art accuracy on the KRVQR and FVQA datasets. We also conduct ablation experiments to prove the effectiveness of each component of the proposed model.
CONTROL OF THE LINEARIZED STEFAN PROBLEM IN A PERIODIC BOX

In this paper we consider the linearized one-phase Stefan problem with surface tension, set in the strip T× (−1, 1), thus with periodic boundary conditions respect to the horizontal direction x1 ∈ T. When the support of the control is not localized in x1, namely, is of the form ω = T × (c, d), we prove that the system is null-controllable in any positive time. We rely on a Fourier decomposition with respect to x1, and controllability results which are uniform with respect to the Fourier frequency parameter for the resulting family of one-dimensional systems. The latter results are also novel, as we compute the full spectrum of the underlying operator for the non-zero Fourier modes. The zeroth mode system, on the other hand, is seen as a controllability problem for the linear heat equation with a finite-dimensional constraint. We extend the controllability result to the setting of controls with a support localized in a box: ω = (a, b) × (c, d), through an argument inspired by the method of Lebeau and Robbiano, under the assumption that the initial data are of zero mean. Numerical experiments motivate several challenging open problems, foraying even beyond the specific setting we deal with herein.
Adaptive Security and Trust Management for Autonomous Messaging Systems

With society's increased dependence on information communication systems, the need for dependable, trustable, robust, and secure adaptive systems becomes ever more acute. Modern autonomic message-oriented middleware platforms have stringent requirements for self-healing, adapting, evolving, fault-tolerance, security, and active vulnerability assessment, especially when the internal working model of a system and the environmental influences on the system are uncertain during run-time. In this paper, we present an adaptive and evolving security approach, and adaptive trust management approach to autonomous messaging middleware systems. This approach learns, anticipates, evolves, and adapts to a changing environment at run-time in the face of changing threats. The approach combines adaptive risk-based security, trust-based security, and security-based trust: the resultant supra-additive synergy improves and increases the strength of security and the degree of trust in the system. The approach also integrates different metrics, assessment tools, and observation tools that improve and increase the assessability and verifiability of the trustworthiness of the system. Validation of results is through industrial case studies and end-user assessment.
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation

This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic localization map, we investigate if the transformer model can also effectively capture class-specific attention for more discriminative object localization by learning multiple class tokens within the transformer. To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens. The proposed MCTformer can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens. We also propose to use a patch-level pairwise affinity, which is extracted from the patch-to-patch transformer attention, to further refine the localization maps. Moreover, the proposed framework is shown to fully complement the Class Activation Mapping (CAM) method, leading to remarkably superior WSSS results on the PASCAL VOC and MS COCO datasets. These results underline the importance of the class token for WSSS. 11https://github.com/xulianuwa/MCTformer
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation

Multi-Agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions across agents. In this article, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting. First, we design a new communication protocol that exploits the ability of depthwise convolution to efficiently extract local relations and learn local communication between neighboring agents. To facilitate multi-agent coordination, we explicitly learn the effect of joint actions by taking the policies of neighboring agents as inputs. Second, we introduce the mean-field approximation into our method to reduce the scale of agent interactions. To more effectively coordinate behaviors of neighboring agents, we enhance the mean-field approximation by a supervised policy rectification network (PRN) for rectifying real-time agent interactions and by a learnable compensation term for correcting the approximation bias. The proposed method enables efficient coordination as well as outperforms several baseline approaches on the adaptive traffic signal control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).
A Glyph-driven Topology Enhancement Network for Scene Text Recognition

—Attention-based methods by establishing one-dimensional (1D) and two-dimensional (2D) mechanisms with an encoder-decoder framework have dominated scene text recognition (STR) tasks due to their capabilities of building implicit language representations. However, 1D attention-based mechanisms suffer from alignment drift on latter characters. 2D attention-based mechanisms only roughly focus on the spatial regions of characters without excavating detailed topological structures, which reduces the visual performance. To mitigate the above issues, we propose a novel Glyph-driven Topology Enhancement Network (GTEN) to improve topological features representations in visual models for STR. Speciﬁcally, an unsupervised method is ﬁrst employed to exploit 1D sequence-aligned attention weights. Second, we construct a supervised segmentation module to capture 2D ordered and pixel-wise topological information of glyphs without extra character-level annotations. Third, these resulting outputs fuse enhanced topological features to enrich semantic feature representations for STR. Experiments demonstrate that GTEN achieves competitive performance on IIIT5K-Words, Street View Text, ICDAR-series, SVT Perspective, and CUTE80 datasets.
Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments

Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task completion using primitive objects in a simulation environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of object scenarios with a total of 1000 test runs in simulation. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches. Demo video, trained models, and source code for the results reproducibility purpose are publicly available. https://sites.google. com/view/pushandgrasp/home
AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30–250$\mu$s). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100$\mu$s) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive applications.AW drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of savinglrestoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) reraining context in the power-ungated domain. Second, AW eliminates rhe flush latency (several tens of microseconds) of the LllL2 caches when entering a deep idle state by keeping LllL2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core’s voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power cost.Our evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumprion of Memcached by up to 71% (35% on average) with<1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka). AW’s new deep C-states C6A and C6AE reduce transition-time by up to 900$\times$ as compared to the deepest existing idle state C6, while consuming only 7% and 5% of the active state (C0) power, respectively.
Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments From Occupancy Grid Maps

Identifying the environment’s structure, through detecting core components such as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable objects), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>) of 2D occupancy maps that may be cluttered and incomplete. ROSE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> is tested in several real-world publicly available cluttered maps obtained in different conditions. The results show that it can robustly identify the environment structure in 2D occupancy maps suffering from clutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation ROSE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> consistently achieves higher performance than the state-of-the-art methods, against which it is compared.
X-Ray Emission from the Jets and Lobes of the Spiderweb

Deep Chandra and Very Large Array imaging reveals a clear correlation between X-ray and radio emission on scales ∼100 kpc in the Spiderweb radio galaxy at z = 2.16. The X-ray emission associated with the extended radio source is likely dominated by inverse Compton upscattering of cosmic microwave background photons by the radio-emitting relativistic electrons. For regions dominated by high surface brightness emission, such as hot spots and jet knots, the implied magnetic fields are ∼50–70 μG. The nonthermal pressure in these brighter regions is then ∼9 × 10−10 dynes cm−2, or three times larger than the nonthermal pressure derived assuming minimum energy conditions, and an order of magnitude larger than the thermal pressure in the ambient cluster medium. Assuming ram pressure confinement implies an average advance speed for the radio source of ∼2400 km s−1 and a source age of ∼3 × 107 yr. Considering the lower surface brightness, diffuse radio-emitting regions, we identify an evacuated cavity in the Lyα emission coincident with the tail of the eastern radio lobe. Making reasonable assumptions for the radio spectrum, we find that the relativistic electrons and fields in the lobe are plausibly in pressure equilibrium with the thermal gas and close to a minimum energy configuration. The radio morphology suggests that the Spiderweb is a high-z example of the rare class of hybrid morphology radio sources (or HyMoRS), which we attribute to interaction with the asymmetric gaseous environment indicated by the Lyα emission.
Massive Black Hole Formation in Dense Stellar Environments: Enhanced X-Ray Detection Rates in High-velocity Dispersion Nuclear Star Clusters

We analyze Chandra X-ray Observatory imaging of 108 galaxies hosting nuclear star clusters (NSCs) to search for signatures of massive black holes (BHs). NSCs are extremely dense stellar environments with conditions that can theoretically facilitate massive BH formation. Recent work by Stone et al. finds that sufficiently dense NSCs should be unstable to the runaway growth of a stellar-mass BH into a massive BH via tidal captures. Furthermore, there is a velocity dispersion threshold (40 km s−1) above which NSCs should inevitably form a massive BH. To provide an observational test of these theories, we measure X-ray emission from NSCs and compare it to the measured velocity dispersion and tidal capture runaway timescale. We find that NSCs above the 40 km s−1 threshold are X-ray detected at roughly twice the rate of those below (after accounting for contamination from X-ray binaries). These results are consistent with a scenario in which dense, high-velocity NSCs can form massive BHs, providing a formation pathway that does not rely on conditions found only at high redshift.
SocioFillmore: A Tool for Discovering Perspectives

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE’s development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.
Towards Managing Industrial Robot Fleets with Hyperledger Fabric Blockchain and ROS 2

— Trust is increasingly becoming a key consideration in the design of autonomous robotic systems. In industrial applications, security and trust in the system are requirements for widespread adoption. Blockchain technologies have emerged as a potential solution to address identity management and secure data aggregation and control. However, the vast majority of works to date utilize Ethereum and smart contracts that are not scalable or well suited for industrial applications. This paper presents what is, to the best of our knowledge, the ﬁrst integration of ROS 2 with the Hyperledger Fabric blockchain. With a framework that leverages Fabric smart contracts and ROS 2 through a Go application, we delve into the potential of using blockchain for controlling robots, and gathering and processing their data. We demonstrate the applicability of the proposed framework to an inventory management use-case where different robots are used to detect objects of interest in a given area. Designed to meet the requirements of distributed robotic systems, we show that the performance of the robots is not impacted signiﬁcantly by the blockchain layer. At the same time, we provide examples for developing other applications that integrate Fabric smart contracts with ROS 2. Our results pave the way for further adoption of blockchain technologies in autonomous robotic systems for building trustable data sharing.
A Scenario Approach to Risk-Aware Safety-Critical System Verification

With the growing interest in deploying robots in unstructured and uncertain environments, there has been increasing interest in factoring risk into safety-critical control development. Similarly, the authors believe risk should also be accounted in the verification of these controllers. In pursuit of sample-efficient methods for uncertain black-box verification then, we first detail a method to estimate the Value-at-Risk of arbitrary scalar random variables without requiring \textit{apriori} knowledge of its distribution. Then, we reformulate the uncertain verification problem as a Value-at-Risk estimation problem making use of our prior results. In doing so, we provide fundamental sampling requirements to bound with high confidence the volume of states and parameters for a black-box system that could potentially yield unsafe phenomena. We also show that this procedure works independent of system complexity through simulated examples of the Robotarium.