Miracle-dz/UniFAR-FTUs huggingface dataset:
Black holes with Lagrange multiplier and potential in mimetic-like gravitational theory: multi-horizon black holes

In this paper, we consider the mimetic-like field equations coupled with the Lagrange multiplier and the potential to derive non-trivial spherically symmetric black hole (BH) solutions. We divided this study into three cases: in the first one, we choose the Lagrange multiplier and the potential to vanish and derive a BH solution that coincides with the BH of the Einstein general relativity despite the non-vanishing value of the mimetic-like scalar field. The first case is consistent with the previous studies in the literature where the mimetic theory coincides with GR [1]. In the second case, we derive a solution with a constant value of the potential and a dynamical value of the Lagrange multiplier. This solution has no horizon, and therefore, the obtained space-time does not correspond to the BH. In this solution, there appears a region of the Euclidian signature where the signature of the diagonal components of the metric is (+,+,+,+) or the region with two times where the signature is (+,+,-,-). Finally, we derive a BH solution with non-vanishing values of the Lagrange multiplier, potential, and mimetic-like scalar field. This BH shows a soft singularity compared with the Einstein BH solution. The relevant physics of the third case is discussed by showing their behavior of the metric potential at infinity, calculating their energy conditions, and studying their thermodynamical quantities. We give a brief discussion on how our third case can generate a BH with three horizons as in the de Sitter-Reissner-Nordström black hole space-time, where the largest horizon is the cosmological one and two correspond to the outer and inner horizons of the BH. Even in the third case, the region of the Euclidian signature or the region with two times appears. We give a condition that such unphysical region(s) is hidden inside the black hole horizon and the existence of the region(s) becomes less unphysical. We also study the thermodynamics of the multi-horizon BH and consider the extremal case, where the radii of two horizons coincide with each other. We observe that the Hawking temperature and the heat capacity vanish in the extremal limit. Finally, we would like to stress the fact that in spite that the field equations we use have no cosmological constant, our BH solutions of the second and third case behave asymptotically as AdS/dS.
Automated Remote Sensing Forest Inventory Using Satelite Imagery

For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms on satellite imagery is a rising topic of research. Although satellite imagery quality is relatively low, additional spectral channels provide a sufficient amount of information for tree crown classification tasks. Assuming that tree crowns are detected already, we use embeddings of tree crowns generated by Autoencoders as a data set to train classical Machine Learning algorithms. We compare our Autoencoder (AE) based approach to traditional convolutional neural networks (CNN) end-to-end classifiers.
Location Information Assisted Beamforming Design for Reconfigurable Intelligent Surface Aided Communication Systems

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.
Critical non-Abelian vortices and holography for little string theory

It has been shown that non-Abelian vortex string supported in four dimensional (4D) N = 2 supersymmetric QCD (SQCD) with the U(2) gauge group and Nf = 4 quark flavors becomes a critical superstring. This string propagates in the ten dimensional space formed by a product of the flat 4D space and an internal space given by a Calabi-Yau non-compact threefold, namely, the conifold. The spectrum of closed string states of the associated string theory was obtained using the equivalence between the critical string on the conifold and the non-critical string on the semiinfinite cigar described by SL(2,R)/U(1) Wess-Zumino-Novikov-Witten model. This spectrum was identified with the spectrum of hadrons in 4DN = 2 SQCD. In order to describe effective interactions of these 4D hadrons in this paper we study correlation functions of normalizable vertex operators localized near the tip of the SL(2,R)/U(1) cigar. We also compare our solitonic string approach to the gauge-string duality to the AdS/CFT-type holography for little string theories (LSTs). The latter relates off mass-shell correlation functions on the field theory side to correlation functions of non-normalizable vertex operators on the cigar. We show that in most channels holographic approach works in our theory because normalizable and non-normalizable vertex operators with the same conformal dimension are related due to the reflection from the tip of the cigar. However, we find that holography does not work for lightest hadrons with given baryonic charge. ar X iv :2 11 0. 08 54 6v 1 [ he pth ] 1 6 O ct 2 02 1
Electronic pumping of heat without charge transfer

A mechanism of electron-mediated pumping of heat in the absence of net charge transfer is proposed. It may be realized in charge-neutral electron systems, such as graphene, coupled to an external electric potential. The flow of heat in this pumping cycle is not accompanied by a buildup of voltage along the system, which offers advantages over traditional thermoelectric cooling setups. Efficiency of heat pumping and magnitude of heat flux are studied in the hydrodynamic regime for weak disorder. In a pristine system, even for an infinitesimal pumping potential the heat flux remains finite. In particular, for a potential in the form of a traveling wave moving with velocity c the pumping is perfect; the entire heat content of the electron liquid is advected with velocity c. For a general pumping cycle the heat flux is determined by the cycle geometry and disorder strength.
Self-Supervised Monocular Depth Estimation with Internal Feature Fusion

Self-supervised learning for depth estimation uses geometry in image sequences for supervision and shows promising results. Like many computer vision tasks, depth network performance is determined by the capability to learn accurate spatial and semantic representations from images. Therefore, it is natural to exploit semantic segmentation networks for depth estimation. In this work, based on a well-developed semantic segmentation network HRNet, we propose a novel depth estimation network DIFFNet, which can make use of semantic information in down and upsampling procedures. By applying feature fusion and an attention mechanism, our proposed method outperforms the state-of-the-art monocular depth estimation methods on the KITTI benchmark. Our method also demonstrates greater potential on higher resolution training data. We propose an additional extended evaluation strategy by establishing a test set of challenging cases, empirically derived from the standard benchmark.
A Learning-based Approach Towards Automated Tuning of SSD Configurations

Thanks to the mature manufacturing techniques, solid-state drives (SSDs) are highly customizable for applications today, which brings opportunities to further improve their storage performance and resource utilization. However, the SSD efficiency is usually determined by many hardware parameters, making it hard for developers to manually tune them and determine the optimal SSD configurations. In this paper, we present an automated learning-based framework, named LearnedSSD, that utilizes both supervised and unsupervised machine learning (ML) techniques to drive the tuning of hardware configurations for SSDs. LearnedSSD automatically extracts the unique access patterns of a new workload using its block I/O traces, maps the workload to previously workloads for utilizing the learned experiences, and recommends an optimal SSD configuration based on the validated storage performance. LearnedSSD accelerates the development of new SSD devices by automating the hard-ware parameter configurations and reducing the manual efforts. We develop LearnedSSD with simple yet effective learning algorithms that can run efficiently on multi-core CPUs. Given a target storage workload, our evaluation shows that LearnedSSD can always deliver an optimal SSD configuration for the target workload, and this configuration will not hurt the performance of non-target workloads.
Enabling Large-Reach TLBs for High-Throughput Processors by Exploiting Memory Subregion Contiguity

Accelerators, like GPUs, have become a trend to deliver future performance desire, and sharing the same virtual memory space between CPUs and GPUs is increasingly adopted to simplify programming. However, address translation, which is the key factor of virtual memory, is becoming the bottleneck of performance for GPUs. In GPUs, a single TLB miss can stall hundreds of threads due to the SIMT execute model, degrading performance dramatically. Through real system analysis, we observe that the OS shows an advanced contiguity (e.g., hundreds of contiguous pages), and more large memory regions with advanced contiguity tend to be allocated with the increase of working sets. Leveraging the observation, we propose MESC to improve the translation efficiency for GPUs. The key idea of MESC is to divide each large page frame (2MB size) in virtual memory space into memory subregions with fixed size (i.e., 64 4KB pages), and store the contiguity information of subregions and large page frames in L2PTEs. With MESC, address translations of up to 512 pages can be coalesced into single TLB entry, without the needs of changing memory allocation policy (i.e., demand paging) and the support of large pages. In the experimental results, MESC achieves 77.2% performance improvement and 76.4% reduction in dynamic translation energy for translation-sensitive workloads.
Improving Compositional Generalization with Self-Training for Data-to-Text Generation

Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs. In this work, we systematically study the compositional generalization of the state-of-the-art T5 models in few-shot data-to-text tasks. We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model’s generalization capability. To further improve the model’s performance, we propose an approach based on self-training using fine-tuned BLEURT for pseudo-response selection. On the commonly-used SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46\%+ and reduces the slot error rates by 73\%+ over the strong T5 baselines in few-shot settings.
Goal Agnostic Planning using Maximum Likelihood Paths in Hypergraph World Models

In this paper, we present a hypergraph--based machine learning algorithm, a datastructure--driven maintenance method, and a planning algorithm based on a probabilistic application of Dijkstra's algorithm. Together, these form a goal agnostic automated planning engine for an autonomous learning agent which incorporates beneficial properties of both classical Machine Learning and traditional Artificial Intelligence. We prove that the algorithm determines optimal solutions within the problem space, mathematically bound learning performance, and supply a mathematical model analyzing system state progression through time yielding explicit predictions for learning curves, goal achievement rates, and response to abstractions and uncertainty. To validate performance, we exhibit results from applying the agent to three archetypal planning problems, including composite hierarchical domains, and highlight empirical findings which illustrate properties elucidated in the analysis.
Think Before You Speak: Using Self-talk to Generate Implicit Commonsense Knowledge for Response Generation

Implicit knowledge, such as common sense, is key to ﬂuid human conversations. Current neural response generation (RG) models are trained end-to-end, omitting unstated implicit knowledge. In this paper, we present a self-talk approach that ﬁrst generates the implicit commonsense knowledge and then generates response by referencing the exter-nalized knowledge, all using one generative model. We analyze different choices to collect knowledge-aligned dialogues, represent implicit knowledge, and elicit knowledge and responses. We introduce three evaluation aspects: knowledge quality , knowledge-response connection , and response quality and perform extensive human evaluations. Our experimental results show that compared with end-to-end RG models, self-talk models that externalize the knowledge grounding process by explicitly generating implicit knowledge also produce responses that are more informative, speciﬁc, and follow common sense. We also ﬁnd via human evaluation that self-talk models generate high-quality knowledge around 75% of the time. We hope that our ﬁndings encourage further work on different approaches to modeling implicit commonsense knowledge and training knowledgeable RG models.
On the Pareto Frontier of Regret Minimization and Best Arm Identification in Stochastic Bandits

We study the Pareto frontier of two archetypal objectives in stochastic bandits, namely, regret minimization (RM) and best arm identiﬁcation (BAI) with a ﬁxed horizon. It is folklore that the balance between exploitation and exploration is crucial for both RM and BAI, but exploration is more critical in achieving the optimal performance for the latter objective. To make this precise, we ﬁrst design and analyze the B O BW-LIL ’UCB ( γ ) algorithm, which achieves order-wise optimal performance for RM or BAI under different values of γ . Com-plementarily, we show that no algorithm can simultaneously perform optimally for both the RM and BAI objectives. More precisely, we establish non-trivial lower bounds on the regret achievable by any algorithm with a given BAI failure probability. This analysis shows that in some regimes B O BW-LIL ’UCB ( γ ) achieves Pareto-optimality up to constant or small terms. Numerical experiments further demonstrate that when applied to difﬁcult instances, B O BW-LIL ’UCB outperforms a close competitor UCB α (Degenne et al., 2019), which is designed for RM and BAI with a ﬁxed conﬁdence.
Low-Precision Quantization for Efficient Nearest Neighbor Search

Fast k-Nearest Neighbor search over real-valued vector spaces (KNN) is an important algorithmic task for information retrieval and recommendation systems. We present a method for using reduced precision to represent vectors through quantized integer values, enabling both a reduction in the memory overhead of indexing these vectors and faster distance computations at query time. While most traditional quantization techniques focus on minimizing the reconstruction error between a point and its uncompressed counterpart, we focus instead on preserving the behavior of the underlying distance metric. Furthermore, our quantization approach is applied at the implementation level and can be combined with existing KNN algorithms. Our experiments on both open source and proprietary datasets across multiple popular KNN frameworks validate that quantized distance metrics can reduce memory by 60% and improve query throughput by 30%, while incurring only a 2% reduction in recall.
Random Formula Generators

In this article, we provide three generators of propositional formulae for arbitrary languages, which uniformly sample three different formulae spaces. They take the same three parameters as input, namely, a desired depth, a set of atomics and a set of logical constants (with specified arities). The first generator returns formulae of exactly the given depth, using all or some of the propositional letters. The second does the same but samples up-to the given depth. The third generator outputs formulae with exactly the desired depth and all the atomics in the set. To make the generators uniform (i.e. to make them return every formula in their space with the same probability), we will prove various cardinality results about those spaces.
PG2Net: Personalized and Group Preferences Guided Network for Next Place Prediction

Predicting the next place to visit is a key in human mobility behavior modeling, which plays a significant role in various fields, such as epidemic control, urban planning, traffic management, and travel recommendation. To achieve this, one typical solution is designing modules based on RNN to capture their preferences to various locations. Although these RNN-based methods can effectively learn individual's hidden personalized preferences to her visited places, the interactions among users can only be weakly learned through the representations of locations. Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG$^2$Net), considering the users' preferences to various places at both individual and collective levels. Specifically, PG$^2$Net concatenates Bi-LSTM and attention mechanism to capture each user's long-term mobility tendency. To learn population's group preferences, we utilize spatial and temporal information of the visitations to construct a spatio-temporal dependency module. We adopt a graph embedding method to map users' trajectory into a hidden space, capturing their sequential relation. In addition, we devise an auxiliary loss to learn the vectorial representation of her next location. Experiment results on two Foursquare check-in datasets and one mobile phone dataset indicate the advantages of our model compared to the state-of-the-art baselines. Source codes are available at https://github.com/urbanmobility/PG2Net.
Polycyclic aromatic hydrocarbons and the ionized gas

We present a study for a sample of galaxies with active nuclei to characterize the main type of PAH molecules present in these objects and the local physical conditions of their irradiating sources, as well as the characteristics of the residing ionized gas, by combining optical and infrared data. Photoionization models were built with the CLOUDY code to reproduce optical emission line ratios in combination with PAH intensity ratios. We find that the species containing 10 − 82 carbon atoms are the most abundant in the sample. We suggest that family of species with only two or three fused rings of and a nitrogen hanging, such as small aromatic amides are important targets worthy of consideration in future experimental/theoretical as well as observational studies. We find that the AGN photoionization models reproduce most of the observational data in the log (6.2/11.3) versus log ([N ii]λ6584/Hα) diagram with the optical to X-ray spectral index of αox = −1.4. The flux of small PAH, as well as the flux of ionized PAHs and PANH, decrease as the logarithm of the ionization parameter (logU) increases. The 6.2/11.3 PAH intensity ratio presents a linear anti correlation between the oxygen abundance and log U, in the sense that the 6.2/11.3 ratio decreases as the oxygen abundance and log U increases. Finally, the results suggest that small species are the dominant emitting molecules in our sample.
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning

Standard few-shot relation classification (RC) is designed to learn a robust classifier with only few labeled data for each class. However, previous works rarely investigate the effects of a different number of classes (i.e., $N$-way) and number of labeled data per class (i.e., $K$-shot) during training vs. testing. In this work, we define a new task, \textit{inconsistent few-shot RC}, where the model needs to handle the inconsistency of $N$ and $K$ between training and testing. To address this new task, we propose Prototype Network-based cross-attention contrastive learning (ProtoCACL) to capture the rich mutual interactions between the support set and query set. Experimental results demonstrate that our ProtoCACL can outperform the state-of-the-art baseline model under both inconsistent $K$ and inconsistent $N$ settings, owing to its more robust and discriminate representations. Moreover, we identify that in the inconsistent few-shot learning setting, models can achieve better performance with \textit{less data} than the standard few-shot setting with carefully-selected $N$ and $K$. In the end of the paper, we provide further analyses and suggestions to systematically guide the selection of $N$ and $K$ under different scenarios.
Virtual Augmentation Supported Contrastive Learning of Sentence Representations

Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. This challenge is magnified in natural language processing, where no general rules exist for data augmentation due to the discrete nature of natural language. We tackle this challenge by presenting a Virtual augmentation Supported Contrastive Learning of sentence representations (VaSCL). Originating from the interpretation that data augmentation essentially constructs the neighborhoods of each training instance, we, in turn, utilize the neighborhood to generate effective data augmentations. Leveraging the large training batch size of contrastive learning, we approximate the neighborhood of an instance via its K-nearest in-batch neighbors in the representation space. We then define an instance discrimination task regarding the neighborhood and generate the virtual augmentation in an adversarial training manner. We access the performance of VaSCL on a wide range of downstream tasks and set a new state-of-the-art for unsupervised sentence representation learning.