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It is argued that recent experiments refuting nonlocal realism, can also be considered as experiments refuting time-ordered nonlocality and, hence, confirming the result of the before-before experiment. However, the before-before experiment provides a broader refutation because it also falsifies the testable relativistic version of Bohm's nonlocal model. All this stresses the interest of a new before-before experiment demonstrating together the failure of time-ordered nonlocality and the violation of the Leggett's inequality.

Flares are a major explosive event in our solar system. They are often followed by coronal mass ejection that has a potential to trigger the geomagnetic storms. There are various studies aiming to predict when and where the flares are likely to occur. Most of these studies mainly discuss the photospheric and chromospheric activity before the flare onset. In this paper we study the coronal features before the famous large flare occurrence on December 13th, 2006. Using the data from Hinode/EUV Imaging Spectrometer (EIS), X-Ray Telescope (XRT), and Solar and Heliospheric Observatory (SOHO) /Extreme ultraviolet Imaging Telescope (EIT), we discuss the coronal features in the large scale (~ a few 100 arcsec) before the flare onset. Our findings are as follows: 1) The upflows in and around active region start growing from ~10 to 30 km /s a day before the flare. 2) The expanding coronal loops are clearly observed a few hours before the flare. 3) Soft X-ray and EUV intensity are gradually reduced. 4) The upflows are further enhanced after the flare. From these observed signatures, we conclude that the outer part of active region loops with low density were expanding a day before the flare o

Physical phenomena observed before strong earthquake have been reported over centuries. Radon anomalies, electrical signals, water level changes, earthquake lights near the epicenter are recognized as pre-earthquake signals to approach earthquake prediction. Anomalous negative signals observed by ground-based atmospheric electric field instrument under fair weather open up a new way to earthquake prediction. Abnormal heat radiation before the earthquake bring fair weather around the epicenter in theory. In order to figure out the weather conditions around the epicenter before earthquakes, 213 global earthquake events with magnitude of 6 or above from 2013 to 2020 were collected. Based on our definition of fair weather, in 96.7% of the events in the statistics, the weather before the earthquake is fair. Besides, the fair state before the earthquake lasted more than 7 hours, leaving us with enough early warming time.

Existing language model benchmarks provide contradictory model rankings, even for benchmarks that aim to capture similar skills. This dilemma of conflicting rankings hampers model selection, clouds model comparisons, and adds confusion to a growing ecosystem of competing models. In this paper, we take a different perspective on model comparison: instead of relying on out-of-the-box performance via direct evaluation, we compare model potential by providing each model with identical benchmark-specific fine-tuning before evaluation. We call this approach train-before-test. Our primary contribution is a comprehensive empirical evaluation of model potential across 24 benchmarks and 61 models. First, we demonstrate that model potential rankings obtained through train-before-test exhibit remarkable consistency across all benchmarks. Whereas traditional rankings demonstrate little external validity under direct evaluation, they enjoy a significant degree of external validity when applying train-before-test: model potential rankings transfer gracefully from one benchmark to another. Second, train-before-test restores the connection between perplexity and downstream task performance, lost un

The conservation of information of evaporating black holes is a very natural consequence of unitarity which is the fundamental symmetry of quantum mechanics. In order to study the conservation of information, we need to understand the nature of the entanglement entropy. The entropy of Hawking radiation is approximately equal to the maximum of entanglement entropy if a black hole is in a state before the Page time, i.e., when the entropy of Hawking radiation is smaller than the entropy of the black hole. However, if there exists a process generating smaller entanglements rather than maximal entanglements, the entropy of Hawking radiation will become smaller than the maximum of the entanglement entropy before the Page time. If this process accumulates, even though the probability is small, the emitted radiation can eventually be distinguished from the exactly thermal state. In this paper, we provide several interpretations of this phenomenon: (1) information of the collapsed matter is emitted before the Page time, (2) there exists a firewall or a non-local effect before the Page time, or (3) the statistical entropy is greater than the areal entropy; a monster is formed. Our conclusio

In this paper, we explore the potential of server waiting before packet transmission in improving the Age of Information (AoI) in status update systems. We consider a non-preemptive queue with Poisson arrivals and independent general service distribution and we incorporate waiting before serving in two packet management schemes: M/GI/1/1 and M/GI/1/$2^*$. In M/GI/1/1 scheme, the server waits for a deterministic time immediately after a packet enters the server. In M/GI/1/$2^*$ scheme, depending on idle or busy system state, the server waits for a deterministic time before starting service of the packet. In both cases, if a potential newer arrival is captured existing packet is discarded. Different from most existing works, we analyze AoI evolution by indexing the incoming packets, which is enabled by an alternative method of partitioning the area under the evolution of instantaneous AoI to calculate its time average. We obtain expressions for average and average peak AoI for both queueing disciplines with waiting. Our numerical results demonstrate that waiting before service can bring significant improvement in average age, particularly, for heavy-tailed service distributions. This

We propose a method to predict toxicity and other textual attributes through the use of natural language processing (NLP) techniques for two recent events: the Ukraine-Russia and Hamas-Israel conflicts. This article provides a basis for exploration in future conflicts with hopes to mitigate risk through the analysis of social media before and after a conflict begins. Our work compiles several datasets from Twitter and Reddit for both conflicts in a before and after separation with an aim of predicting a future state of social media for avoidance. More specifically, we show that: (1) there is a noticeable difference in social media discussion leading up to and following a conflict and (2) social media discourse on platforms like Twitter and Reddit is useful in identifying future conflicts before they arise. Our results show that through the use of advanced NLP techniques (both supervised and unsupervised) toxicity and other attributes about language before and after a conflict is predictable with a low error of nearly 1.2 percent for both conflicts.

A Jupiter-family comet, 17P/Holmes, underwent outbursts in 1892 and 2007. In particular, the 2007 outburst is known as the greatest outburst over the past century. However, little is known about the activity before the outburst because it was unpredicted. In addition, the time evolution of the nuclear physical status has not been systematically studied. Here we study the activity of 17P/Holmes before and after the 2007 outburst through optical and mid-infrared observations. We found that the nucleus highly depleted its near-surface icy component before but became activated after the 2007 outburst. Assuming a conventional 1-um-sized grain model, we derived a surface fractional active area of 0.58% +- 0.14% before the outburst whereas it was enlarged by a factor of ~50 after the 2007 outburst. We also found that large (>=1 mm) particles could be dominant in the dust tail observed around aphelion. Based on the size of the particles, the dust production rate was >=170 kg/s at the heliocentric distance rh = 4.1 AU, suggesting that the nucleus still held active status around the aphelion passage. The nucleus color was similar to that of the dust particles and average for a Jupiter-

In the Frontier AI Safety Commitments, sixteen companies committed to "Assess the risks posed by their frontier models or systems across the AI lifecycle, including [...] as appropriate, before and during training" (I) and to "Provide public transparency on the implementation of the above (I-VI), except insofar as doing so would increase risk or divulge sensitive commercial information to a degree disproportionate to the societal benefit. They should still share more detailed information which cannot be shared publicly with trusted actors, including their respective home governments or appointed body, as appropriate" (VII). This short paper considers what information should be shared with whom before training begins. What information should be shared publicly and what only with trusted actors such as home governments? Sharing such information before a frontier training run can build shared awareness and preparedness, can improve risk assessment and management, and can contribute to greater predictability and accountability. Companies could share certain information before a training run including: Expected dates of beginning and end of training; Expected compute used (in FLOP); Des

We introduce a method to speed up training by 2x and inference by 3x in deep neural networks using structured pruning applied before training. Unlike previous works on pruning before training which prune individual weights, our work develops a methodology to remove entire channels and hidden units with the explicit aim of speeding up training and inference. We introduce a compute-aware scoring mechanism which enables pruning in units of sensitivity per FLOP removed, allowing even greater speed ups. Our method is fast, easy to implement, and needs just one forward/backward pass on a single batch of data to complete pruning before training begins.

The before-before experiment demonstrates free will acting from outside space-time. The experimental violation of the Leggett's inequality supports the view that it is not appropriate to attempt to limit this freedom in Nature by forcing it to mimic classical features.

In 1975, E. R. Robinson conducted the hallmark study of the behavior of classical nova light curves before eruption, and this work has now become part of the standard knowledge of novae. He made three points; that 5 out of 11 novae showed pre-eruption rises in the years before eruption, that one nova (V446 Her) showed drastic changes in the variability across eruptions, and that all but one of the novae (excepting BT Mon) have the same quiescent magnitudes before and after the outburst. This work has not been tested since it came out. We have now tested these results by going back to the original archival photographic plates and measuring large numbers of pre-eruption magnitudes for many novae using comparison stars on a modern magnitude scale. We find in particular that four out of five claimed pre-eruption rises are due to simple mistakes in the old literature, that V446 Her has the same amplitude of variations across its 1960 eruption, and that BT Mon has essentially unchanged brightness across its 1939 eruption. Out of 22 nova eruptions, we find two confirmed cases of significant pre-eruption rises (for V533 Her and V1500 Cyg), while T CrB has a deep pre-eruption dip. These eve

The before-before experiment demonstrates that quantum randomness can be controlled by influences from outside spacetime, and therefore by immaterial free will. Rather than looking at quantum physics as the model for explaining free will, one should look at free will as a primitive principle for explaining why the laws of Nature are quantum.

We investigate whether the success of a zero-shot Chain-of-Thought (CoT) process can be predicted before completion. We discover that a probing classifier, based on LLM representations, performs well \emph{even before a single token is generated}, suggesting that crucial information about the reasoning process is already present in the initial steps representations. In contrast, a strong BERT-based baseline, which relies solely on the generated tokens, performs worse, likely because it depends on shallow linguistic cues rather than deeper reasoning dynamics. Surprisingly, using later reasoning steps does not always improve classification. When additional context is unhelpful, earlier representations resemble later ones more, suggesting LLMs encode key information early. This implies reasoning can often stop early without loss. To test this, we conduct early stopping experiments, showing that truncating CoT reasoning still improves performance over not using CoT at all, though a gap remains compared to full reasoning. However, approaches like supervised learning or reinforcement learning designed to shorten CoT chains could leverage our classifier's guidance to identify when early s

Pruning on neural networks before training not only compresses the original models, but also accelerates the network training phase, which has substantial application value. The current work focuses on fine-grained pruning, which uses metrics to calculate weight scores for weight screening, and extends from the initial single-order pruning to iterative pruning. Through these works, we argue that network pruning can be summarized as an expressive force transfer process of weights, where the reserved weights will take on the expressive force from the removed ones for the purpose of maintaining the performance of original networks. In order to achieve optimal expressive force scheduling, we propose a pruning scheme before training called Neural Network Panning which guides expressive force transfer through multi-index and multi-process steps, and designs a kind of panning agent based on reinforcement learning to automate processes. Experimental results show that Panning performs better than various available pruning before training methods.

This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all targets. Many conventional tracking methods rely on preprocessing for detection (e.g., thresholding), which severely limits tracking performance at a low signal-to-noise ratio (SNR). In contrast, the proposed method operates directly on raw sensor signals without requiring such preprocessing. Though there also exist methods applicable to raw sensor signals called track-before-detect, the proposed method has significant advantages over them. First, it is general without any restrictions on observation/process noise statistics (e.g., Gaussian) or the functional form of each target's contribution to the sensors (e.g., linear, separable, binary). Especially, it includes Salmond et al.'s track-before-detect particle filter for a single target as a particular example up to some implementation details. Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to a target birth/death model. We present a simulation example of radio-frequency tomography, where it sig

There is increasing evidence that causality provides useful bounds in determining the domain structure after a continuous transition. In devising their scaling laws for domain size after such a transition, Zurek and Kibble presented arguments in which causality is important both before and after the time at which the transition begins to be implemented. Using numerical simulations of kinks in 1+1 dimensions, we explain how the domain structure is determined exclusively by what happens after the transition, even though the correlation length freezes in before the transition.

Cross-validation (CV) is a technique used to estimate generalization error for prediction models. For pipeline modeling algorithms (i.e. modeling procedures with multiple steps), it has been recommended the entire sequence of steps be carried out during each replicate of CV to mimic the application of the entire pipeline to an external testing set. While theoretically sound, following this recommendation can lead to high computational costs when a pipeline modeling algorithm includes computationally expensive operations, e.g. imputation of missing values. There is a general belief that unsupervised variable selection (i.e. ignoring the outcome) can be applied before conducting CV without incurring bias, but there is less consensus for unsupervised imputation of missing values. We empirically assessed whether conducting unsupervised imputation prior to CV would result in biased estimates of generalization error or result in poorly selected tuning parameters and thus degrade the external performance of downstream models. Results show that despite optimistic bias, the reduced variance of imputation before CV compared to imputation during each replicate of CV leads to a lower overall r

Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates than earlier cohorts, with gaps opening before late 2022. Finally, from millions of university syllabi, we find that graduates taking more AI-exposed curricula had higher first-job pay and shorter job searches after ChatGPT. Together, these results point to forces pre-dating generative AI and to the ongoing value of LLM-relevant education.

We consider several mechanisms that possibly affect the evolution of disk galaxies in clusters using analytical models based on a hierarchical clustering scenario. We especially focus on the evolution of disk galaxies in subclusters located around a main cluster. We show that ram-pressure stripping cannot be always ignored in subclusters, although their masses are much smaller than that of the main cluster. The star-formation rate of a galaxy may gradually decrease by the stripping of warm gas (`strangulation') in a main cluster. However, we find that ram-pressure stripping could start before the strangulation is completed, if a field galaxy directly falls into the main cluster. Since this conflicts with some recent observations, many galaxies might have been affected by some environmental effects when they were in subclusters before they fell into the main cluster (`pre-processing'). We show that strangulation and evaporation of the cold gas by the surrounding hot ICM in subclusters are the possible candidates. We also show that the observed morphological transformation of disk galaxies at z ~< 1 is not chiefly due to galaxy mergers.