Miracle-dz/UniFAR-FTUs huggingface dataset:
On the persistence of the ether as absolute space

We analyse how the concept of the ether, playing the role of absolute space, is still present in physics. When the problem is considered in the context of classical mechanics, we show that vestiges of absolute space can be found in the standard presentation of inertial systems. We offer an alternative -- fully relational -- definition of inertial systems which not only eliminates the problem but it further shows that the equivalence principle is just a particular consequence of the No Arbitrariness Principle. In terms of Special Relativity, the non-existence of relative velocities implies a constructive contradiction (their existence is assumed in the construction). The problem is inherited from Lorentz' use of the ether, developed in his interpretation of Maxwell's electrodynamics. In summary, the velocities in the Lorentz transformations must be considered velocities relative to the ether (absolute space) if the theory is not to fall apart for being inconsistent. We discuss the relevance of the phenomenological map, and how previous works have failed to acknowledge that the consistency problem is not in the exposed part of the theory but in the supporting phenomenological map which, rather than being constructed anew, it transports concepts of classical mechanics by habit, without revising their validity in the context of Special Relativity.
Intense Dark Exciton Emission from Strongly Quantum Confined CsPbBr3 Nanocrystals.

Dark exciton as the lowest-energy (ground) exciton state in metal halide perovskite nanocrystals is a subject of much interest. This is because the superior performance of perovskites as the photon source combined with long lifetime of dark exciton can be attractive for many applications of exciton. However, the direct observation of the intense and long-lived dark exciton emission, indicating facile access to dark ground exciton state, has remained elusive. Here, we report the intense photoluminescence from dark exciton with microsecond lifetime in strongly confined CsPbBr3 nanocrystals and reveal the crucial role of confinement in accessing the dark ground exciton state. This study establishes the potential of strongly quantum confined perovskite nanostructures as the excellent platform to harvest the benefits of extremely long-lived dark exciton.
Constructive Game Logic ⋆

Game Logic is an excellent setting to study proofs-about-programs via the interpretation of those proofs as programs, because constructive proofs for games correspond to e(cid:27)ective winning strategies to follow in response to the opponent’s actions. We thus develop Constructive Game Logic , which extends Parikh’s Game Logic (GL) with constructivity and with (cid:28)rst-order programs (cid:224) la Pratt’s (cid:28)rst-order dynamic logic (DL). Our major contributions include: 1. a novel realizability semantics capturing the adversarial dynamics of games, 2. a natural deduction calculus and operational semantics describing the computational meaning of strategies via proof-terms, and 3. theoretical results including soundness of the proof calculus w.r. t. realizability semantics, progress and preservation of the operational semantics of proofs, and Existential Properties on support of the extraction of computational artifacts from game proofs. Together, these results provide the most general account of a Curry-Howard interpretation for any program logic to date, and the (cid:28)rst at all for Game Logic.
Adaptive Temporal Difference Learning with Linear Function Approximation

This paper revisits the temporal difference (TD) learning algorithm for the policy evaluation tasks in reinforcement learning. Typically, the performance of TD(0) and TD() is very sensitive to the choice of stepsizes. Oftentimes, TD(0) suffers from slow convergence. Motivated by the tight link between the TD(0) learning algorithm and the stochastic gradient methods, we develop a provably convergent adaptive projected variant of the TD(0) learning algorithm with linear function approximation that we term AdaTD(0). In contrast to the TD(0), AdaTD(0) is robust or less sensitive to the choice of stepsizes. Analytically, we establish that to reach an accuracy, the number of iterations needed is O(2 ln4 1 / ln4 1) in general case, where represents the speed of the underlying Markov chain converges to the stationary distribution. This implies that the iteration complexity of AdaTD(0) is no worse than that of TD(0) in the worst case. When the stochastic semi-gradients are sparse, we provide theoretical acceleration of AdaTD(0). Going beyond TD(0), we develop an adaptive variant of TD(), which is referred to as AdaTD(). Empirically, we evaluate the performance of AdaTD(0) and AdaTD() on several standard reinforcement learning tasks, which demonstrate the effectiveness of our new approaches.
The appearance of a compact jet in the soft–intermediate state of 4U 1543−47

Recent advancements in the understanding of jet–disc coupling in black hole candidate X-ray binaries (BHXBs) have provided close links between radio jet emission and X-ray spectral and variability behaviour. In ‘soft’ X-ray states the jets are suppressed, but the current picture lacks an understanding of the X-ray features associated with the quenching or recovering of these jets. Here, we show that a brief, ∼4 d infrared (IR) brightening during a predominantly soft X-ray state of the BHXB 4U 1543−47 is contemporaneous with a strong X-ray type B quasi-periodic oscillation, a slight spectral hardening and an increase in the rms variability, indicating an excursion to the soft–intermediate state (SIMS). This IR ‘flare’ has a spectral index consistent with optically thin synchrotron emission and most likely originates from the steady, compact jet. This core jet emitting in the IR is usually only associated with the hard state, and its appearance during the SIMS places the ‘jet line’ between the SIMS and the soft state in the hardness–intensity diagram for this source. IR emission is produced in a small region of the jets close to where they are launched (∼0.1 light-seconds), and the time-scale of the IR flare in 4U 1543−47 is far too long to be caused by a single, discrete ejection. We also present a summary of the evolution of the jet and X-ray spectral/variability properties throughout the whole outburst, constraining the jet contribution to the X-ray flux during the decay.
Optimally Guarding Perimeters and Regions with Mobile Range Sensors

We investigate the problem of using mobile robots equipped with 2D range sensors to optimally guard perimeters or regions, i.e., 1D or 2D sets. Given such a set of arbitrary shape to be guarded, and $k$ mobile sensors where the $i$-th sensor can guard a circular region with a variable radius $r_i$, we seek the optimal strategy to deploy the $k$ sensors to fully cover the set such that $\max r_i$ is minimized. On the side of computational complexity, we show that computing a $1.152$-optimal solution for guarding a perimeter or a region is NP-hard, i.e., the problem is hard to approximate. The hardness result on perimeter guarding holds when each sensor may guard at most two disjoint perimeter segments. On the side of computational methods, for the guarding perimeters, we develop a fully polynomial time approximation scheme (FPTAS) for the special setting where each sensor may only guard a single continuous perimeter segment, suggesting that the aforementioned hard-to-approximate result on the two-disjoint-segment sensing model is tight. For the general problem, we first describe a polynomial-time (2+$\epsilon)$-approximation algorithm as an upper bound, applicable to both perimeter guarding and region guarding. This is followed by a high-performance integer linear programming (ILP) based method that computes near-optimal solutions. Thorough computational benchmarks as well as evaluation on potential application scenarios demonstrate the effectiveness of these algorithmic solutions.
Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems

We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier multiuser single-input multiple-output (MU-SIMO) systems under fading channels. In particular, a single-user noncoherent EA-based (NC-EA) system, based on the multicarrier SIMO framework, is first proposed, where both the transmitter and receiver are represented by deep neural networks (DNNs), known as the encoder and decoder of an EA. Unlike existing systems, the decoder of the NC-EA is fed only with the energy combined from all receive antennas, while its encoder outputs a real-valued vector whose elements stand for the sub-carrier power levels. Using the NC-EA, we then develop two novel DNN structures for both uplink and downlink NC-EA multiple access (NC-EAMA) schemes, based on the multicarrier MU-SIMO framework. Note that NC-EAMA allows multiple users to share the same sub-carriers, thus enables to achieve higher performance gains than noncoherent orthogonal counterparts. By properly training, the proposed NC-EA and NC-EAMA can efficiently recover the transmitted data without any channel state information estimation. Simulation results clearly show the superiority of our schemes in terms of reliability, flexibility and complexity over baseline schemes.
MA-DST: Multi-Attention Based Scalable Dialog State Tracking

Task oriented dialog agents provide a natural language interface for users to complete their goal. Dialog State Tracking (DST), which is often a core component of these systems, tracks the system's understanding of the user's goal throughout the conversation. To enable accurate multi-domain DST, the model needs to encode dependencies between past utterances and slot semantics and understand the dialog context, including long-range cross-domain references. We introduce a novel architecture for this task to encode the conversation history and slot semantics more robustly by using attention mechanisms at multiple granularities. In particular, we use cross-attention to model relationships between the context and slots at different semantic levels and self-attention to resolve cross-domain coreferences. In addition, our proposed architecture does not rely on knowing the domain ontologies beforehand and can also be used in a zero-shot setting for new domains or unseen slot values. Our model improves the joint goal accuracy by 5% (absolute) in the full-data setting and by up to 2% (absolute) in the zero-shot setting over the present state-of-the-art on the MultiWoZ 2.1 dataset.
Ambient Signals based Load Modeling with Combined Gradient-based Optimization and Regression Method

Load modeling has been an important issue in modeling a power system. Ambient signals based load modeling approach has recently been proposed to better track the time-varying changes of load models caused by the increasing uncertain factors in power loads. To improve the computation efficiency and the model structure complexity of the previous approaches, a combined gradient-based optimization and regression method is proposed in this paper to identify the load model parameters from ambient signals. An open static load model structure in which various static load models can be applied, together with the induction motor as the dynamic load model, are selected as the composite load model structure for parameter identification. Then, the static load model parameters are identified through regression, after which the induction motor parameters can be obtained through optimization with the regression residuals being the objective function. After the transformation of the induction motor model, the objective function is quasiconvex in most of the feasible region so that the gradient-based optimization algorithm can be applied. The case study results in Guangdong Power Grid have shown the effectiveness and the improvement in computation efficiency of the proposed approach.
Infrared variability due to magnetic pressure-driven jets, dust ejection and quasi-puffed-up inner rims

The interaction between a YSO stellar magnetic field and its protostellar disc can result in stellar accretional flows and outflows from the inner disc rim. Gas flows with a velocity component perpendicular to disc mid-plane subject particles to centrifugal acceleration away from the protostar, resulting in particles being catapulted across the face of the disc. The ejected material can produce a ‘dust fan’, which may be dense enough to mimic the appearance of a ‘puffed-up’ inner disc rim. We derive analytical equations for the time-dependent disc toroidal field, the disc magnetic twist, the size of the stable toroidal disc region, the jet speed, and the disc region of maximal jet flow speed. We show how the observed infrared variability of the pre-transition disc system LRLL 31 can be modelled by a dust ejecta fan from the inner-most regions of the disc whose height is partially dependent on the jet flow speed. The greater the jet flow speed, the higher is the potential dust fan scale height. An increase in mass accretion on to the star tends to increase the height and optical depth of the dust ejection fan, increasing the amount of 1–8 µm radiation. The subsequent shadow reduces the amount of light falling on the outer disc and decreases the 8–40 µm radiation. A decrease in the accretion rate reverses this scenario, thereby producing the observed ‘see-saw’ infrared variability.
A Recurrent Neural Network Based Patch Recommender for Linux Kernel Bugs

Software bugs in a production environment have an undesirable impact on quality of service, unplanned system downtime, and disruption in good customer experience, resulting in loss of revenue and reputation. Existing approaches to automated software bug repair focuses on known bug templates detected using static code analysis tools and test suites, and in automatic generation of patch code for these bugs. We describe the typical bug fixing process employed in the Linux kernel, and motivate the need for a new automated tool flow to fix bugs. We present an initial design of such an automated tool that uses Recurrent Neural Network (RNN) based Natural Language Processing to generate patch recommendations from user generated bug reports. At the 50th percentile of the test bugs, the correct patch occurs within the top 11.5 patch recommendations output by the model. Further, we present a Linux kernel developer's assessment of the quality of patches recommended for new unresolved kernel bugs.
Improved Approximate Degree Bounds For k-distinctness

An open problem that is widely regarded as one of the most important in quantum query complexity is to resolve the quantum query complexity of the k-distinctness function on inputs of size N. While the case of k=2 (also called Element Distinctness) is well-understood, there is a polynomial gap between the known upper and lower bounds for all constants k>2. Specifically, the best known upper bound is O(N^{(3/4)-1/(2^{k+2}-4)}) (Belovs, FOCS 2012), while the best known lower bound for k >= 2 is Omega(N^{2/3} + N^{(3/4)-1/(2k)}) (Aaronson and Shi, J.~ACM 2004; Bun, Kothari, and Thaler, STOC 2018). For any constant k >= 4, we improve the lower bound to Omega(N^{(3/4)-1/(4k)}). This yields, for example, the first proof that 4-distinctness is strictly harder than Element Distinctness. Our lower bound applies more generally to approximate degree. As a secondary result, we give a simple construction of an approximating polynomial of degree O(N^{3/4}) that applies whenever k <= polylog(N).
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model, making them risky and inefficient for decentralized private data. This work tackles a practical setting where only a trained source model is available and investigates how we can effectively utilize such a model without source data to solve UDA problems. We propose a simple yet generic representation learning framework, named \emph{Source HypOthesis Transfer} (SHOT). SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis. To verify its versatility, we evaluate SHOT in a variety of adaptation cases including closed-set, partial-set, and open-set domain adaptation. Experiments indicate that SHOT yields state-of-the-art results among multiple domain adaptation benchmarks.
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes

Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental assumption is that the output/group membership labels for all observations are known. This assumption is often violated in real applications. For instance, in healthcare data sets, sensitive attributes such as ethnicity are often missing or unreported. To this end, we introduce a weakly supervised multi-output model based on dependent Gaussian processes. Our approach is able to leverage data without complete group labels or possibly only prior belief on group memberships to enhance accuracy across all outputs. Through intensive simulations and case studies on insulin, testosterone and body fat data sets, we show that our model excels in multi-output settings with missing labels while being competitive in traditional fully labeled settings. We end by highlighting the possible use of our approach in fair inference and sequential decision making.
Embedding Graph Auto-Encoder with Joint Clustering via Adjacency Sharing

Graph convolution networks have attracted many attentions and several graph auto-encoder based clustering models are developed for attributed graph clustering. However, most existing approaches separate clustering and optimization of graph auto-encoder into two individual steps. In this paper, we propose a graph convolution network based clustering model, namely, Embedding Graph Auto-Encoder with JOint Clustering via Adjacency Sharing (\textit{EGAE-JOCAS}). As for the embedded model, we develop a novel joint clustering method, which combines relaxed k-means and spectral clustering and is applicable for the learned embedding. The proposed joint clustering shares the same adjacency within graph convolution layers. Two parts are optimized simultaneously through performing SGD and taking close-form solutions alternatively to ensure a rapid convergence. Moreover, our model is free to incorporate any mechanisms (e.g., attention) into graph auto-encoder. Extensive experiments are conducted to prove the superiority of EGAE-JOCAS. Sufficient theoretical analyses are provided to support the results.
Warm Starting Bandits with Side Information from Confounded Data

We study a variant of the multi-armed bandit problem where side information in the form of bounds on the mean of each arm is provided. We describe how these bounds on the means can be used efficiently for warm starting bandits. Specifically, we propose the novel UCB-SI algorithm, and illustrate improvements in cumulative regret over the standard UCB algorithm, both theoretically and empirically, in the presence of non-trivial side information. As noted in (Zhang & Bareinboim, 2017), such information arises, for instance, when we have prior logged data on the arms, but this data has been collected under a policy whose choice of arms is based on latent variables to which access is no longer available. We further provide a novel approach for obtaining such bounds from prior partially confounded data under some mild assumptions. We validate our findings through semi-synthetic experiments on data derived from real datasets.
Do We Need Zero Training Loss After Achieving Zero Training Error?

Overparameterized deep networks have the capacity to memorize training data with zero training error. Even after memorization, the training loss continues to approach zero, making the model overconfident and the test performance degraded. Since existing regularizers do not directly aim to avoid zero training loss, they often fail to maintain a moderate level of training loss, ending up with a too small or too large loss. We propose a direct solution called flooding that intentionally prevents further reduction of the training loss when it reaches a reasonably small value, which we call the flooding level. Our approach makes the loss float around the flooding level by doing mini-batched gradient descent as usual but gradient ascent if the training loss is below the flooding level. This can be implemented with one line of code, and is compatible with any stochastic optimizer and other regularizers. With flooding, the model will continue to "random walk" with the same non-zero training loss, and we expect it to drift into an area with a flat loss landscape that leads to better generalization. We experimentally show that flooding improves performance and as a byproduct, induces a double descent curve of the test loss.
Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching

Existing image-text matching approaches typically infer the similarity of an image-text pair by capturing and aggregating the affinities between the text and each independent object of the image. However, they ignore the connections between the objects that are semantically related. These objects may collectively determine whether the image corresponds to a text or not. To address this problem, we propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN). In particular, given an input image-text pair, our model reorders the image objects based on the positions of their most related words in the text. In the same way as extracting the hidden features from word embeddings, the model leverages RNN to extract high-level object features from the reordered object inputs. We validate that the high-level object features contain useful joint information of semantically related objects, which benefit the retrieval task. To compute the image-text similarity, we incorporate a Multi-attention Cross Matching Model into DP-RNN. It aggregates the affinity between objects and words with cross-modality guided attention and self-attention. Our model achieves the state-of-the-art performance on Flickr30K dataset and competitive performance on MS-COCO dataset. Extensive experiments demonstrate the effectiveness of our model.