Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations

Camera-based autonomous systems that emulate human perception are increasingly being integrated into safety-critical platforms. Consequently, an established body of literature has emerged that explores adversarial attacks targeting the underlying machine learning models. Adapting adversarial attacks to the physical world is desirable for the attacker, as this removes the need to compromise digital systems. However, the real world poses challenges related to the"survivability"of adversarial manipulations given environmental noise in perception pipelines and the dynamicity of autonomous systems. In this paper, we take a sensor-first approach. We present EvilEye, a man-in-the-middle perception attack that leverages transparent displays to generate dynamic physical adversarial examples. EvilEye exploits the camera's optics to induce misclassifications under a variety of illumination conditions. To generate dynamic perturbations, we formalize the projection of a digital attack into the physical domain by modeling the transformation function of the captured image through the optical pipeline. Our extensive experiments show that EvilEye's generated adversarial perturbations are much more robust across varying environmental light conditions relative to existing physical perturbation frameworks, achieving a high attack success rate (ASR) while bypassing state-of-the-art physical adversarial detection frameworks. We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks. Finally, we discuss mitigation strategies against the EvilEye attack.
Curve-lifted codes for local recovery using lines

In this paper, we introduce curve-lifted codes over fields of arbitrary characteristic, inspired by Hermitian-lifted codes over $\mathbb{F}_{2^r}$. These codes are designed for locality and availability, and their particular parameters depend on the choice of curve and its properties. Due to the construction, the numbers of rational points of intersection between curves and lines play a key role. To demonstrate that and generate new families of locally recoverable codes (LRCs) with high availabilty, we focus on norm-trace-lifted codes. In some cases, they are easier to define than their Hermitian counterparts and consequently have a better asymptotic bound on the code rate.
On optimal constant weight codes derived from $\omega$-circulant balanced generalized weighing matrices

A family of $\omega$-circulant balanced weighing matrices with classical parameters is used for the construction of optimal constant weight codes over an alphabet of size $g+1$ and length $n=(q^m -1)/(q-1)$, where $q$ is an odd prime power, $m>1$, and $g$ is a divisor of $q-1$.
The Subtle Effects of Mergers on Star Formation in Nearby Galaxies

Interactions and mergers play an important role in regulating the physical properties of galaxies, such as their morphology, gas content, and star formation rate (SFR). Controversy exists as to the degree to which these events, even gas-rich major mergers, enhance star formation activity. We study merger pairs selected from a sample of massive (M * ≥ 1010 M ⊙), low-redshift (z = 0.01–0.11) galaxies located in the Stripe 82 region of the Sloan Digital Sky Survey, using stellar masses, SFRs, and total dust masses derived from a new set of uniformly measured panchromatic photometry and spectral energy distribution analysis. The dust masses, when converted to equivalent total atomic and molecular hydrogen, probe gas masses as low as ∼108.5 M ⊙. Our measurements delineate a bimodal distribution on the M gas–M * plane: the gas-rich, star-forming galaxies that trace the well-studied gas mass main sequence, and passive galaxies that occupy a distinct, gas-poor regime. These two populations, in turn, map into a bimodal distribution on the relation between SFR and gas mass surface density. Among low-redshift galaxies, galaxy mergers, including those that involve gas-rich and nearly equal-mass galaxies, exert a minimal impact on their SFR, specific SFR, or star formation efficiency. Starbursts are rare. The star formation efficiency of gas-rich, minor mergers even appears suppressed. This study stresses the multiple, complex factors that influence the evolution of the gas and its ability to form stars in mergers.
Insights into Cognitive Engagement: Comparing the Effectiveness of Game-Based and Video-Based Learning

The analysis of brain signals holds considerable importance in enhancing our comprehension of diverse learning techniques and cognitive mechanisms. Game-based learning is increasingly being recognized for its interactive and engaging educational approach. A pilot study of twelve participants divided into experimental and control groups was conducted to understand its effects on cognitive processes. Both groups were provided with the same contents regarding the basic structure of the graph. The participants in the experimental group engaged in a quiz-based game, while those in the control group watched a pre-recorded video. Functional Near-Infrared Spectroscopy (fNIRS) was employed to acquire cerebral signals, and a series of pre and post-tests were administered. The findings of our study indicate that the group engaged in the game activity displayed elevated levels of oxygenated hemoglobin ( Δ 𝐻𝑏𝑂 ) compared to the group involved in watching videos. Conversely, the deoxygenated hemoglobin levels ( Δ 𝐻𝑏𝑅 ) remained relatively consistent across both groups throughout the learning process. The aforementioned findings suggest that the use of game-based learning has a substantial influence on cognitive processes. Furthermore, it is evident that both the game and video groups exhibited higher neural activity in the Lateral Prefrontal cortex (PFC). The Δ 𝐻𝑏𝑂 ratio demonstrates that the game group had 2.33 times more neural processing in the Lateral PFC than the video group. This data is further supported by the knowledge gain analysis, which indicates that the game-based approach resulted in a 47.74% higher knowledge gain than the video group, as calculated from the difference in pre-and post-test scores.
A Soft Robotic Gripper with Active Palm for In-Hand Object Reorientation

The human hand has an inherent ability to manipulate and re-orientate objects without external assistance. As a consequence, we are able to operate tools and perform an array of actions using just one hand, without having to continuously re-grasp objects. Emulating this functionality in robotic end-effectors remains a key area of study with efforts being made to create advanced control systems that could be used to operate complex manipulators. In this paper, a three fingered soft gripper with an active rotary palm is presented as a simpler, alternative method of performing in-hand rotations. The gripper, complete with its pneumatic suction cup to prevent object slippage, was tested and found to be able to effectively grasp and rotate a variety of objects both quickly and precisely.
Register Minimization of Cost Register Automata over a Field

Weighted automata (WA) are an extension of finite automata that defines functions from words to values in a given semi-ring. An alternative model that is deterministic, called Cost Register Automata (CRA), was introduced by Alur et al. It enriches deterministic finite automata with a finite number of registers, which store values, are updated at each transition using the operations of the semi-ring, and are combined to produce the output. The expressiveness of a CRA depends on the number of its registers and the type of register updates allowed for each of its transitions. In particular, the class of functions computable by a CRA with register updates defined by linear (or affine) maps correspond exactly with rational functions (functions computable by a WA). A natural problem for CRA is the register minimization problem: given a function defined by a CRA, what is the minimal number of registers needed to realize this function? In this paper, we solve the register minimization problem for CRA over a field with linear (or affine) register updates, using an algebraic invariant of a WA introduced recently by Bell and Smertnig, the so-called the linear hull of the automaton. This invariant being computable, we are able to explicitly compute a CRA with linear (or affine) updates, using the minimal number of registers. Using these techniques, we are also able to solve the more general CRA minimisation problem: given a CRA and integers $k,d$, is there an equivalent linear (resp.~affine) CRA using at most $k$ states and $d$ registers?
The radio emission in radio-quiet quasars: the VLBA perspective

The origin of the radio emission in radio-quiet quasars (RQQ) is not established yet. We present new VLBA observations at 1.6 and 4.9 GHz of ten RQQ (nine detected), which together with published earlier observations of eight RQQ (five detected), forms a representative sample of 18 RQQ drawn from the Palomar-Green sample of low z (<0.5) AGN. The spectral slope of the integrated emission extends from very steep (α < −1.98) to strongly inverted (α = +2.18), and the slopes of nine of the 14 objects are flat (α > −0.5). Most objects have an unresolved flat-spectrum core, which coincides with the optical Gaia position. The extended emission is generally steep-spectrum, has a low brightness temperature (<107 K), and is displaced from the optical core (the Gaia position) by ∼5 − 100 pc. The VLBA core flux is tightly correlated with the X-ray flux, and follows a radio to X-ray luminosity relation of log LR/LX ≃ −6, for all objects with a black hole mass log MBH/M⊙ < 8.5. The flatness of the core emission implies a compact source size (≲ 0.1 pc), which likely originates from the accretion disk corona. The mas-scale extended emission is optically thin and of clumpy structure, and is likely produced by an outflow from the center. Radio observations at higher frequencies can further test the accretion disk coronal emission interpretation for the core emission in RQQ.
Circle packings and total geodesic curvatures in hyperbolic background geometry

In this paper, we study a new type of circle packings in hyperbolic background geometry. Horocycles and hypercycles are also considered in this packing. We give the existence and rigidity of this type of circle packing with conical singularities in terms of the total geodesic curvature. Moreover, we introduce the combinatorial curvature flow on surfaces to find the desired circle packing with the prescribed total geodesic curvature.
Feature Importance Measurement based on Decision Tree Sampling

Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampler, a SAT-based method for measuring feature importance in tree-based model. Our method has fewer parameters than random forest and provides higher interpretability and stability for the analysis in real-world problems. An implementation of DT-Sampler is available at https://github.com/tsudalab/DT-sampler.
A low confinement loss double-photonic crystal fibre over 850 nm bandwidth with 26 orbital angular momentum modes transmission

ABSTRACT In this paper, a new double-photonic crystal fibre (D-PCF) is proposed and numerically investigated, result shows that can support angular momentum (OAM) mode transmission up to 26. Based on As2S3 substrate, the proposed D-PCF is composed of three well-ordered air-hole rings in the cladding and another several air-hole rings at the centre. Due to the large effective index difference greater than 10−4 between eigenmodes, OAM can be well separated with the proposed D-PCF. In addition, the confinement loss of less than 7 × 10−9 dB/m for all eigenmodes is obtained with wavelengths spanning from 1.25 to 2.1 µm, where the optical wave can be confined closely within the core of D-PCF. With these beneficial properties, the designed D-PCF could enable large-capacity transmission in high-speed communications.
A Multicut Approach to Compute Upper Bounds for Risk-Averse SDDP

Stochastic Dual Dynamic Programming (SDDP) is a widely used and fundamental algorithm for solving multistage stochastic optimization problems. Although SDDP has been frequently applied to solve risk-averse models with the Conditional Value-at-Risk (CVaR), it is known that the estimation of upper bounds is a methodological challenge, and many methods are computationally intensive. In practice, this leaves most SDDP implementations without a practical and clear stopping criterion. In this paper, we propose using the information already contained in a multicut formulation of SDDP to solve this problem with a simple and computationally efficient methodology. The multicut version of SDDP, in contrast with the typical average cut, preserves the information about which scenarios give rise to the worst costs, thus contributing to the CVaR value. We use this fact to modify the standard sampling method on the forward step so the average of multiple paths approximates the nested CVaR cost. We highlight that minimal changes are required in the SDDP algorithm and there is no additional computational burden for a fixed number of iterations. We present multiple case studies to empirically demonstrate the effectiveness of the method. First, we use a small hydrothermal dispatch test case, in which we can write the deterministic equivalent of the entire scenario tree to show that the method perfectly computes the correct objective values. Then, we present results using a standard approximation of the Brazilian operation problem and a real hydrothermal dispatch case based on data from Colombia. Our numerical experiments showed that this method consistently calculates upper bounds higher than lower bounds for those risk-averse problems and that lower bounds are improved thanks to the better exploration of the scenarios tree.
Fundamental causal bounds of quantum random access memories

Quantum devices should operate in adherence to quantum physics principles. Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tasks such as linear algebra, data search, and machine learning, is often proposed to offer $\mathcal{O}(\log N)$ circuit depth for $\mathcal{O}(N)$ data size, given $N$ qubits. However, this claim appears to breach the principle of relativity when dealing with a large number of qubits in quantum materials interacting locally. In our study we critically explore the intrinsic bounds of rapid quantum memories based on causality, employing the relativistic quantum field theory and Lieb-Robinson bounds in quantum many-body systems. In this paper, we consider a hardware-efficient QRAM design in hybrid quantum acoustic systems. Assuming clock cycle times of approximately $10^{-3}$ seconds and a lattice spacing of about 1 micrometer, we show that QRAM can accommodate up to $\mathcal{O}(10^7)$ logical qubits in 1 dimension, $\mathcal{O}(10^{15})$ to $\mathcal{O}(10^{20})$ in various 2D architectures, and $\mathcal{O}(10^{24})$ in 3 dimensions. We contend that this causality bound broadly applies to other quantum hardware systems. Our findings highlight the impact of fundamental quantum physics constraints on the long-term performance of quantum computing applications in data science and suggest potential quantum memory designs for performance enhancement.
Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems

Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs require heavy utilization of embedded hardware typically found in AVs. For both components, there is a tradeoff between non-functional and functional performance, and both impact a vehicle's safety. For instance, giving an OOD detector a longer response time can increase its accuracy at the expense of the LEC. We consider an LEC with binary output like an autonomous emergency braking system (AEBS) and use risk, the combination of severity and occurrence of a failure, to model the effect of both components' design parameters on each other's functional and non-functional performance, as well as their impact on system safety. We formulate a co-design methodology that uses this risk model to find the design parameters for an OOD detector and LEC that decrease risk below that of the baseline system and demonstrate it on a vision based AEBS. Using our methodology, we achieve a 42.3% risk reduction while maintaining equivalent resource utilization.
Towards Bridging the Digital Language Divide

It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages. ' The goal of our paper is to bring attention to a phenomenon that we call linguistic bias: multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. Linguistic bias is manifested in uneven per-language performance even in the case of similar test conditions. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented, and that can even become ethically problematic as they disregard valuable aspects of diversity as well as the needs of the language communities themselves. As our attempt at building diversity-aware language resources, we present a new initiative that aims at reducing linguistic bias through both technological design and methodology, based on an eye-level collaboration with local communities.
Transfer Learning for Portfolio Optimization

In this work, we explore the possibility of utilizing transfer learning techniques to address the financial portfolio optimization problem. We introduce a novel concept called"transfer risk", within the optimization framework of transfer learning. A series of numerical experiments are conducted from three categories: cross-continent transfer, cross-sector transfer, and cross-frequency transfer. In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of"transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.
Evaluating Large Language Models for Radiology Natural Language Processing

The rise of large language models (LLMs) has marked a pivotal shift in the ﬁeld of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a signiﬁcant impact in the medical ﬁeld. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proﬁcient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Speciﬁcally, the ability to derive impressions from radiologic ﬁndings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives

Group activity recognition is a hot topic in computer vision. Recognizing activities through group relationships plays a vital role in group activity recognition. It holds practical implications in various scenarios, such as video analysis, surveillance, automatic driving, and understanding social activities. The model's key capabilities encompass efficiently modeling hierarchical relationships within a scene and accurately extracting distinctive spatiotemporal features from groups. Given this technology's extensive applicability, identifying group activities has garnered significant research attention. This work examines the current progress in technology for recognizing group activities, with a specific focus on global interactivity and activities. Firstly, we comprehensively review the pertinent literature and various group activity recognition approaches, from traditional methodologies to the latest methods based on spatial structure, descriptors, non-deep learning, hierarchical recurrent neural networks (HRNN), relationship models, and attention mechanisms. Subsequently, we present the relational network and relational architectures for each module. Thirdly, we investigate methods for recognizing group activity and compare their performance with state-of-the-art technologies. We summarize the existing challenges and provide comprehensive guidance for newcomers to understand group activity recognition. Furthermore, we review emerging perspectives in group activity recognition to explore new directions and possibilities.
Work Statistics and Entanglement Across the Fermionic Superfluid‐Insulator Transition

Entanglement in many‐body systems may display quantum phase transition signatures, and analogous insights are emerging in the study of work fluctuations. Here, the fermionic superfluid‐to‐insulator transition (SIT) is considered and related to its entanglement properties and its work distribution statistics. Using the attractive fermionic Hubbard model with randomly distributed impurities, the work distribution is analyzed under two quench protocols triggering the SIT. In the first, the concentration of impurities is increased; in the second, the impurities' disorder strength is varied. The results indicate that, at criticality, the entanglement is minimized while the average work is maximized. This study demonstrates that, for this state, density fluctuations vanish at all orders, resulting in all central moments of the work probability distribution being precisely zero. For systems undergoing a precursor to the transition (short chains with finite impurity potential) numerical results confirm these predictions, with higher moments further from the ideal results. For both protocols, at criticality, the system absorbs the most energy with almost no penalty in terms of fluctuations: ultimately this feature can be used to implement a quantum critical battery. The impact of temperature on this critical behaviour is also investigated and shown to favor work extraction for high enough temperatures.