Miracle-dz/UniFAR-FTUs huggingface dataset:
Room temperature electron–hole liquid phase in monolayer MoSi2Z4 (Z = pinctogen)

Photo-excited electrons and holes in insulators, above a critical density and below a critical temperature, can condense to form an electron–hole liquid (EHL) phase. However, observing the EHL phase at room temperature is extremely challenging. Here, we introduce the monolayer MoSi2Z4 (Z = N, As, P) series of compounds as a promising platform for observing the EHL phase at room temperature. The higher impact of the Coulomb interactions in two dimensions helps these monolayers support the EHL phase with an increased EHL binding energy and transition temperature, along with strongly bound excitons. Our findings motivate further exploration of the MoSi2Z4 monolayers for realizing the EHL phase at high temperatures to harness collective phenomena for optoelectronic applications.
Parent structures of near-ambient nitrogen-doped lutetium hydride superconductor

Recently, near-ambient superconductivity has been experimentally evidenced in a nitrogen-doped lutetium hydride by Dasenbrock-Gammon \emph{et al.} [Nature 615, 244 (2023)], which yields a remarkable maximum $T_c$ of 294 K at just 10 kbar. However, due to the difficulty of x-ray diffraction (XRD) in identifying light elements such as hydrogen and nitrogen, the crystal structure of the superconductor remains elusive, in particular for the actual stoichiometry of hydrogen and nitrogen and their atomistic positions. This holds even for its parent structure. Here, we set out to address this issue by performing a thorough density functional theory study on the structural, electronic, dynamical, and optical properties of lutetium hydrides. Through thermal and lattice dynamic analysis as well as XRD and superconductor color comparisons, we unambiguously clarified that the parent structures are a mixture of dominant LuH$_2$ phase of the CaF$_2$-type (instead of originally proposed LuH$_3$ structure of $Fm\bar{3}m$ space group) and minor LuH phase of the NaCl-type.
Building Krylov complexity from circuit complexity

Krylov complexity has emerged as a new probe of operator growth in a wide range of non-equilibrium quantum dynamics. However, a fundamental issue remains in such studies: the definition of the distance between basis states in Krylov space is ambiguous. Here, we show that Krylov complexity can be rigorously established from circuit complexity when dynamical symmetries exist. Whereas circuit complexity characterizes the geodesic distance in a multi-dimensional operator space, Krylov complexity measures the height of the final operator in a particular direction. The geometric representation of circuit complexity thus unambiguously designates the distance between basis states in Krylov space. This geometric approach also applies to time-dependent Liouvillian superoperators, where a single Krylov complexity is no longer sufficient. Multiple Krylov complexity may be exploited jointly to fully describe operator dynamics.
Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.
Some good quaternary additive codes outperform linear counterparts

It is well known that additive codes may have better parameters than linear codes. However, it is still a challenging problem to efﬁciently construct additive codes that outperform linear codes, especially those with greater distance than linear codes of the same length and dimension. To advance this problem, this paper focuses on constructing additive codes that outperform linear codes using quasi-cyclic codes and combinatorial methods. Firstly, we propose a lower bound on the minimum symplectic distance of 1-generator quasi-cyclic codes of index even. Further, we get many binary quasi-cyclic codes with large symplectic distances utilizing computer-supported combination and search methods, all corresponding to good quaternary additive codes. Notably, 15 additive codes have greater distances than best-known quaternary linear codes in Grassl’s code table (bounds on the minimum distance of quaternary linear codes http://www.codetables.de) for the same lengths and dimensions. Moreover, employing a combinatorial approach, we partially determine the parameters of optimal quaternary additive 3.5-dimensional codes with lengths from 28 to 254 . Finally, as an extension, we also construct some good additive complementary dual codes with larger distances than best-known quaternary linear complementary dual codes in the literature.
Distributionally Robust Chance-Constrained Optimization for Hierarchical UAV-based MEC

Multi-access edge computing (MEC) is regarded as a promising technology in the sixth-generation communication. However, the antenna gain is always affected by the environment when unmanned aerial vehicles (UAVs) are served as MEC platforms, resulting in unexpected channel errors. In order to deal with the problem and reduce the power consumption in the UAV-based MEC, we jointly optimize the access scheme and power allocation in the hierarchical UAV-based MEC. Specifically, UAVs are deployed in the lower layer to collect data from ground users. Moreover, a UAV with powerful computation ability is deployed in the upper layer to assist with computing. The goal is to guarantee the quality of service and minimize the total power consumption. We consider the errors caused by various perturbations in realistic circumstances and formulate a distributionally robust chance-constrained optimization problem with an uncertainty set. The problem with chance constraints is intractable. To tackle this issue, we utilize the conditional value-at-risk method to reformulate the problem into a semidefinite programming form. Then, a joint algorithm for access scheme and power allocation is designed. Finally, we conduct simulations to demonstrate the efficiency of the proposed algorithm.
Live, Rich, and Composable: Qualities for Programming Beyond Static Text

Efforts to push programming beyond static textual code have sought to imbue programming with multiple distinct qualities. One long-acknowledged quality is liveness: providing programmers with in-depth feedback about a program's dynamic behavior as the program is edited. A second quality, long-explored but lacking a shared term of art, is richness: allowing programmers to edit programs though domain-specific representations and interactions rather than solely through text. In this paper, we map the relationship between these two qualities and survey past work that exemplifies them. We observe that systems combining liveness and richness often do so at the cost of an essential quality of traditional programming: composability. We argue that, by combining liveness, richness, and composability, programming systems can better capture the full potential of interactive computation without leaving behind the expressivity of traditional code.
SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction

3D human motion prediction is a research area of high significance and a challenge in computer vision. It is useful for the design of many applications including robotics and autonomous driving. Traditionally, autogregressive models have been used to predict human motion. However, these models have high computation needs and error accumulation that make it difficult to use them for realtime applications. In this paper, we present a non-autogressive model for human motion prediction. We focus on learning spatio-temporal representations non-autoregressively for generation of plausible future motions. We propose a novel architecture that leverages the recently proposed Transformers. Human motion involves complex spatio-temporal dynamics with joints affecting the position and rotation of each other even though they are not connected directly. The proposed model extracts these dynamics using both convolutions and the self-attention mechanism. Using specialized spatial and temporal self-attention to augment the features extracted through convolution allows our model to generate spatio-temporally coherent predictions in parallel independent of the activity. Our contributions are threefold: (i) we frame human motion prediction as a sequence-to-sequence problem and propose a non-autoregressive Transformer to forecast a sequence of poses in parallel; (ii) our method is activity agnostic; (iii) we show that despite its simplicity, our approach is able to make accurate predictions, achieving better or comparable results compared to the state-of-the-art on two public datasets, with far fewer parameters and much faster inference.
Kagome Quantum Oscillations in Graphene Superlattices

Electronic spectra of solids subjected to a magnetic field are often discussed in terms of Landau levels and Hofstadter-butterfly-style Brown–Zak minibands manifested by magneto-oscillations in two-dimensional electron systems. Here, we present the semiclassical precursors of these quantum magneto-oscillations which appear in graphene superlattices at low magnetic field near the Lifshitz transitions and persist at elevated temperatures. These oscillations originate from Aharonov–Bohm interference of electron waves following open trajectories that belong to a kagome-shaped network of paths characteristic for Lifshitz transitions in the moire superlattice minibands of twistronic graphenes.
Beyond Fish and Bicycles: Exploring the Varieties of Online Women’s Ideological Spaces

The Internet has been instrumental in connecting under-represented and vulnerable groups of people. Platforms built to foster social interaction and engagement have enabled historically disenfranchised groups to have a voice. One such vulnerable group is women. In this paper, we explore the diversity in online women’s ideological spaces using a multi-dimensional approach. We perform a large-scale, data-driven analysis of over 6M Reddit comments and submissions from 14 subreddits. We elicit a diverse taxonomy of online women’s ideological spaces, ranging from counterparts to the so-called Manosphere to Gender-Critical Feminism. We then perform content analysis, finding meaningful differences across topics and communities. Finally, we explore two platforms, namely, ovarit.com and thepinkpill.co, where two toxic communities of online women’s ideological spaces (Gender-Critical Feminism and Femcels) migrated after their ban on Reddit.
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge as data evolves. Recently, pre-training has achieved substantial progress, making vast pre-trained models (PTMs) accessible for CIL. Contrary to traditional methods, PTMs possess generalizable embeddings, which can be easily transferred. In this work, we revisit CIL with PTMs and argue that the core factors in CIL are adaptivity for model updating and generalizability for knowledge transferring. 1) We first reveal that frozen PTM can already provide generalizable embeddings for CIL. Surprisingly, a simple baseline (SimpleCIL) which continually sets the classifiers of PTM to prototype features can beat state-of-the-art even without training on the downstream task. 2) Due to the distribution gap between pre-trained and downstream datasets, PTM can be further cultivated with adaptivity via model adapting. We propose ADapt And Merge (ADAM), which aggregates the embeddings of PTM and adapted models for classifier construction. ADAM is a general framework that can be orthogonally combined with any parameter-efficient tuning method, which holds the advantages of PTM's generalizability and adapted model's adaptivity. 3) Additionally, we find previous benchmarks are unsuitable in the era of PTM due to data overlapping and propose four new benchmarks for assessment, namely ImageNet-A, ObjectNet, OmniBenchmark, and VTAB. Extensive experiments validate the effectiveness of ADAM with a unified and concise framework.
Second law from the Noether current on null hypersurfaces

I study the balance law equation of surface charges in the presence of background fields. The construction allows a unified description of Noether's theorem for both global and local symmetries. From the balance law associated with some of these symmetries, I will discuss generalizations of Wald's Noether entropy formula and general entropy balance laws on null hypersurfaces based on the null energy conditions, interpreted as an entropy creation term. The entropy is generally the so-called improved Noether charge, a quantity that has recently been investigated by many authors, associated to null future-pointing diffeomorphisms. These local and dynamical definitions of entropy on the black hole horizon differ from the Bekenstein-Hawking entropy through terms proportional to the first derivative of the area along the null geodesics. Two different definitions of the dynamical entropy are identified, deduced from gravity symplectic potentials providing a suitable notion of gravitational flux which vanish on non-expanding horizons. The first one is proposed as a definition of the entropy for dynamical black holes by Wald and Zhang, and it satisfies the physical process first law locally. The second one vanishes on any cross section of Minkowski's light cone. I study general properties of its balance law. In particular, I look at first order perturbations around a non expanding horizon. Furthermore, I show that the dynamical entropy increases on the event horizon formed by a spherical symmetric collapse between the two stationary states of vanishing flux, i.e the initial flat light cone and the final stationary black hole. I compare this process to a phase transition, in which the symmetry group of the stationary black hole phase is enlarged by the supertranslations.
Encoding de Finetti's coherence within Lukasiewicz logic and MV-algebras

The present paper investigates proof-theoretical and algebraic properties for the probability logic FP(L,L), meant for reasoning on the uncertainty of Lukasiewicz events. Methodologically speaking, we will consider a translation function between formulas of FP(L,L) to the propositional language of Lukasiewicz logic that allows us to apply the latter and the well-developed theory of MV-algebras directly to probabilistic reasoning. More precisely, leveraging on such translation map, we will show proof-theoretical properties for FP(L,L) and introduce a class of algebras with respect to which FP(L,L) will be proved to be locally sound and complete. Finally, we will apply these previous results to investigate what we called"probabilistic unification problem". In this respect, we will prove that Ghilardi's algebraic view on unification can be extended to our case and, on par with the Lukasiewicz propositional case, we show that probabilistic unification is of nullary type.
Multi-Content Interaction Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) poses significant challenges due to limited support images and large intra-class appearance discrepancies. Most existing approaches focus on aligning the support-query correlations from the same layer of the frozen backbone while neglecting the bias between different tasks and different layers. In this paper, we propose a Multi-Content Interaction Network (MCINet) to remedy these issues by fully exploiting and interacting with the different contextual information contained in distinct branches. Specifically, MCINet improves FSS from three perspectives: 1) boosting the query representations through incorporating the independent information from another learnable branch into the features from the frozen backbone, 2) enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and 3) refining the predicted results with a multi-scale mask prediction strategy. Experiments on three benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset. Code will be released at: https://github.com/chenhao-zju/mcinet
SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency

This work presents an effective depth-consistency Self-Prompt Transformer, terms as SelfPromer, for image dehazing. It is motivated by an observation that the estimated depths of an image with haze residuals and its clear counterpart vary. Enforcing the depth consistency of dehazed images with clear ones, therefore, is essential for dehazing. For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration. Specifically, we first apply deep features extracted from the input images to the depth difference features for generating the prompt that contains the haze residual information in the input. Then we propose a prompt embedding module that is designed to perceive the haze residuals, by linearly adding the prompt to the deep features. Further, we develop an effective prompt attention module to pay more attention to haze residuals for better removal. By incorporating the prompt, prompt embedding, and prompt attention into an encoder-decoder network based on VQGAN, we can achieve better perception quality. As the depths of clear images are not available at inference, and the dehazed images with one-time feed-forward execution may still contain a portion of haze residuals, we propose a new continuous self-prompt inference that can iteratively correct the dehazing model towards better haze-free image generation. Extensive experiments show that our SelfPromer performs favorably against the state-of-the-art approaches on both synthetic and real-world datasets in terms of perception metrics including NIQE, PI, and PIQE. The source codes will be made available at https://github.com/supersupercong/SelfPromer.
Non-Coherent Active Device Identification for Massive Random Access

Massive Machine-Type Communications (mMTC) is a key service category in the current generation of wireless networks featuring an extremely high density of energy and resource-limited devices with sparse and sporadic activity patterns. In order to enable random access in such mMTC networks, base station needs to identify the active devices while operating within stringent access delay constraints. In this paper, an energy efficient active device identification protocol is proposed in which active devices transmit On-Off Keying (OOK) modulated preambles jointly and base station employs non-coherent energy detection avoiding channel estimation overheads. The minimum number of channel-uses required by the active user identification protocol is characterized in the asymptotic regime of total number of devices <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> when the number of active devices <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> scales as <inline-formula> <tex-math notation="LaTeX">$k=\Theta (1)$ </tex-math></inline-formula> along with an achievability scheme relying on the equivalence of activity detection to a group testing problem. Several practical schemes based on Belief Propagation (BP) and Combinatorial Orthogonal Matching Pursuit (COMP) are also proposed. Simulation results show that BP strategies outperform COMP significantly and can operate close to the theoretical achievability bounds. In a partial-recovery setting where few misdetections are allowed, BP continues to perform well.
Multilayer dynamic neural networks for non-linear system on-line identification

To identify on-line a quite general class of non-linear systems, this paper proposes a new stable learning law of the multilayer dynamic neural networks. A Lyapunov-like analysis is used to derive this stable learning procedure for the hidden layer as well as for the output layer. An algebraic Riccati equation is considered to construct a bound for the identification error. The suggested learning algorithm is similar to the well-known backpropagation rule of the multilayer perceptrons but with an additional term which assure the stability property of the identification error.
Status substitution and conspicuous consumption

This paper adapts ideas from social identity theory to set out a new framework for modelling conspicuous consumption. Agents derive status from their own conspicuous consumption and from belonging to an identity group with high conspicuous consumption. Importantly, these two sources of status are substitutes. Agents also feel pressure to conform with their neighbours in a network. This framework can rationalise a set of seemingly conflicting stylised facts about conspicuous consumption that are currently explained by different families of models. In addition, our model delivers new testable predictions regarding the effect of network structure and income inequality on conspicuous consumption.
Use perturbations when learning from explanations

Machine learning from explanations (MLX) is an approach to learning that uses human-provided explanations of relevant or irrelevant features for each input to ensure that model predictions are right for the right reasons. Existing MLX approaches rely on local model interpretation methods and require strong model smoothing to align model and human explanations, leading to sub-optimal performance. We recast MLX as a robustness problem, where human explanations specify a lower dimensional manifold from which perturbations can be drawn, and show both theoretically and empirically how this approach alleviates the need for strong model smoothing. We consider various approaches to achieving robustness, leading to improved performance over prior MLX methods. Finally, we show how to combine robustness with an earlier MLX method, yielding state-of-the-art results on both synthetic and real-world benchmarks.