Miracle-dz/UniFAR-FTUs huggingface dataset:
Your Dataset is a Multiset and You Should Compress it Like One

Neural Compressors (NCs) are codecs that leverage neural networks and entropy coding to achieve competitive compression performance for images, audio, and other data types. These compressors exploit parallel hardware, and are particularly well suited to compressing i.i. d. batches of data. The average number of bits needed to represent each example is at least the well-known cross-entropy. However, the cross-entropy bound assumes the order of the compressed examples in a batch is preserved, which in many applications is not necessary. The number of bits used to implicitly store the order information is the logarithm of the number of unique permutations of the dataset. In this work, we present a method that reduces the bitrate of any codec by exactly the number of bits needed to store the order, at the expense of shufﬂing the dataset in the process. Conceptually, our method applies bits-back coding to a latent variable model with observed symbol counts (i.e. multiset) and a latent permutation deﬁning the ordering, and does not require retraining any models. We present experiments with both lossy off-the-shelf codecs (WebP) as well as lossless NCs. On Binarized MNIST, lossless NCs achieved savings of up to 7 . 6% , while adding only 10% extra compute time.
Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems

The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (rooted policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.
A Holocene temperature (brGDGT) record from Garba Guracha, a high-altitude lake in Ethiopia

Abstract. Eastern Africa has experienced strong climatic changes since the last deglaciation (15 000 years ago). The driving mechanisms and teleconnections of these spatially complex climate variations are yet not fully understood. Although previous studies on lake systems have enhanced our knowledge of Holocene precipitation variation in eastern Africa, relatively few studies have reconstructed the terrestrial temperature history of eastern Africa from lake archives. Here, we present (i) a new branched glycerol dialkyl glycerol tetraether (brGDGT) temperature calibration that includes Bale Mountains surface sediments and (ii) a quantitative record of mean annual air temperature (MAT) over the past 12 ka cal BP using brGDGTs in a sediment core collected from Garba Guracha (3950 m a.s.l.) in the Bale Mountains. After adding Bale Mountains surface sediment (n=11) data (Baxter et al., 2019) to the existing East African lake dataset, additional variation in 6-methyl brGDGTs was observed, which necessitated modifying the MBT5ME′ calibration (MBT denotes methylation of branched tetraethers) by adding 6-methyl brGDGT IIIa′ (resulting in the MBT Bale Mountains index, r2=0.93, p<0.05). Comparing the MBT5ME′ and the new MBT Bale Mountains index, our high-altitude Garba Guracha temperature record shows that warming occurred shortly after the Holocene onset when the temperature increased by more than 3.0 ∘C in less than 600 years. The highest temperatures prevailed between 9 and 6 ka cal BP, followed by a temperature decrease until 1.4 ka cal BP. The reconstructed temperature history is linked to supraregional climatic changes associated with insolation forcing and the African Humid Period (AHP), as well as with local anomalies associated with catchment deglaciation and hydrology.
Experiments on Learning Action Probability Models from Replay Data in RTS Games

Recent work has shown that incorporating action probability models (models that given a game state can predict the probability with which an expert will play each move) into MCTS can lead to signiﬁcant performance improvements in a variety of adversarial games, including RTS games. This paper presents a collection of experiments aimed at understanding the relation between the amount of training data, the predictive performance of the action models, the effect of these models in the branching factor of the game and the resulting performance gains in MCTS. Experiments are carried out in the context of the µ RTS simulator, showing that more accurate predictive models do not necessarily result in better MCTS performance.
Supplementary: Active Learning of Convex Halfspaces on Graphs

Proof. Fix d, s ∈ N. Consider the unit vectors e1, . , es ∈ R and shifted copies of them: ej + i · es+1, for j ∈ {1, . , s} and i ∈ {1, . , d}. Construct the fully connected graph of these s · d points with edge weights corresponding to the Euclidean distance. The minimum shortest path cover will have size s and the diameter will be d. We can label each shortest path Pj of the form (ej , ej + es+1, . . . , ej + d · es+1) with at most one cut edge at an arbitrary position, independently of the other paths. The resulting labelling is halfspace separable, because the only shortest path between two vertices on different paths Pj ,Pj′ is the edge connecting them. Hence, to deduce all labels we will need at least log d(G) queries per path.
Acknowledgments and Disclosure of Funding

This research was supported by an EPSRC Programme Grant (EP/V000748/1) and a gift from Amazon Web Services (AWS). The authors would like to acknowledge the use of the University of Oxford Advanced Research Computing (ARC) facility in carrying out this work, http://dx. doi.org/10.5281/zenodo.22558, and the use of Hartree Centre resources. The Oxford Robotics Institute is supported by SCAN Computers in the form of hardware and services. Martin Engelcke was funded by an EPSRC DTA Studentship and a Google Studentship during his doctoral studies. Finally, the authors thank Yizhe Wu and Adam R. Kosiorek for useful comments and the NeurIPS reviewers for their time and feedback.
HypGen: A Generative Approach for Natural Language Understanding Enrichment Using Implicit User Feedback

In a conversational AI system, Natural Lan-001 guage Understanding (NLU) is a key compo-002 nent that produces the semantic interpretations 003 for a user request. As one of the most upstream 004 components, NLU errors are costly and have 005 a wide blast radius to the end-to-end system 006 behavior. We propose a model-based gener-007 ative approach for automatic NLU n-best en-008 richment, by leveraging implicit user feedback 009 learned from the existing user dissatisfaction 010 and rephrase detection techniques. It aims to 011 generate reasonable and promising NLU inter-012 pretation candidates that can potentially correct 013 NLU errors. We propose three different mod-014 eling designs and compare their performance 015 with extensive experiments on live traffic from 016 one of today’s state-of-the-art large-scale con-017 versational AI systems. 018
An explore-then-commit algorithm for submodular maximization under full-bandit feedback

We investigate the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit feedback, where no extra information other than the reward of selected action at each time step t is ob-served. We propose a simple algorithm, Explore-Then-Commit Greedy (ETCG) and prove that it achieves a (1 − 1 /e ) -regret upper bound of O ( n 13 k 43 T 23 log( T ) 12 ) for a horizon T , number of base elements n , and cardinality constraint k . We also show in experiments with synthetic and real-world data that the ETCG empirically outperforms other full-bandit methods.
What Makes Your Data Unavailable To Deep Learning?

Availability attacks 1 , which poison the training data with imperceptible perturbations, can make the data not exploitable by machine learning algorithms so as to prevent unauthorized use of data. In this work, we investigate why these perturbations work in principle. We are the first to unveil an important population property of the perturbations of these attacks: they are almost linearly separable when assigned with the target labels of the corresponding samples, which hence can work as shortcuts for the learning objective. We further verify that linear separability is indeed the workhorse for availability attacks. We synthesize linearly-separable perturbations as attacks and show that they are as powerful as the deliberately crafted attacks. Moreover, such synthetic perturbations are much easier to generate. For example, previous attacks need dozens of hours to generate perturbations for ImageNet while our algorithm only needs several seconds. Our finding also suggests that the short-cut learning is more widely present than previously believed as deep models would rely on shortcuts even if they are of an imperceptible scale and mixed together with the normal features.
Empirics on the Expressiveness of Randomized Signature

Time series analysis is a widespread task in Natural Sciences, Social Sciences and Engineering. A fundamental problem is ﬁnding an expressive yet efﬁcient-to-compute representation of the input time series to use as a starting point to perform arbitrary downstream tasks. In this paper, we build upon recent work using signature of a path as a feature map, and investigate a computationally efﬁcient technique to approximate these features based on linear random projections. We present several theoretical results to justify our approach, we analyze and showcase its empirical performance on the task of learning a mapping between the input controls of a Stochastic Differential Equation (SDE) and its corresponding solution. Our results show that the representational power of the proposed random features allows to efﬁciently learn the aforementioned mapping.
CELL STRUCTURE AND FUNCTION 8, 223-232 (1983) C by Japan Society for Cell Biology Enrichment of the Poly (A) Sequence and Lack of Enhancement of Total RNA Synthesis in Cultured Xenopus Hepatocytes by Estradio1-17ƒÀ

The effect of estradiol-17ƒÀ on RNA synthesis and the amounts of total RNA and polyadenylic acid were determined in primary cultures of Xenopus laevis liver parenchymal cells. Results showed that estradiol did not alter the RNA content significantly; control cells contained 11.9 ±0.34 i,cg and estradiol-treated cells 12.4 •}0.17 ƒÊg per 106 cells on day 2 of estradiol treatment, and 22.0 ±0.61 pcg and 24.0 ±1.09 ƒÊg on day 5. Hybridization with [3H]poly(U) revealed that estradiol increased the poly(A) content about 1.2-fold more than in the controls on day 2 and 1.6-fold on day 5 of estradiol treatment. The actual rate of RNA synthesis was estimated from analyses of the kinetics of [3H]adenosine incorporation into the ATP pool and into RNA. The initial rate of incorporation of ATP into RNA on day 5 of estradiol treatment was 29.38 pmol/min/106 cells and the rate of the controls of 29.35. Subsequent accumulation kinetics of [3H]adenosine into RNA showed no difference between estradiol and the control cells. Thus, estradiol did not alter the rate of total RNA synthesis and the total RNA content significantly, but it did increase the poly(A) content. Vitellogenin synthesis in adult Xenopus laevis is an excellent model with which to study the hormonal regulation of gene expression (18). Administration of estrogen to male Xenopus induces vitellogenin synthesis in liver cells which normally do not produce vitellogenin in vivo (19). Under such conditions, Lewis et al. (7) reported that the amount of total liver RNA increased to 1.5-fold that in the controls between 6 and 12 days after estrogen injection. Wittliff et al. (23) reported that estrogen stimulated total RNA synthesis five times the value found for untreated animals 9 to 12 h after its injection. Tata and Baker (17) reported that a 4to 8-fold increase in the rate of hepatic nuclear RNA synthesis took place within 11 h of estrogen injection to male Xenopus. These studies, however, did not analyze the effect of estrogen on the specific radioactivity of RNA precursor pool after radioactive nucleoside administration. Furthermore, it is necessary to ascertain whether these responses are caused directly by estrogen because results might have been affected by the complex hormonal environment present in vivo. Abbreviations used: PCA, perchloric acid; TCA, trichloroacetic acid; SDS, sodium dodecyl sulfate.
Strong light scattering of highly oxygenated organic aerosols impacts significantly on visibility degradation

Abstract. Secondary organic aerosols (SOAs) account for a large fraction of atmospheric aerosol mass and play significant roles in visibility impairment by scattering solar radiation. However, comprehensive evaluations of SOA scattering abilities under ambient relative humidity (RH) conditions on the basis of field measurements are still lacking due to the difficulty of simultaneously direct quantifications of SOA scattering efficiency in dry state and SOA water uptake abilities. In this study, field measurements of aerosol chemical and physical properties were conducted in winter in Guangzhou (lasting about 3 months) using a humidified nephelometer system and aerosol chemical speciation monitor. A modified multilinear regression model was proposed to retrieve dry-state mass scattering efficiencies (MSEs, defined as scattering coefficient per unit aerosol mass) of aerosol components. The more oxidized oxygenated organic aerosol (MOOA) with an O/C ratio of 1.17 was identified as the most efficient light scattering aerosol component. On average, 34 % mass contribution of MOOA to total submicron organic aerosol mass contributed 51 % of dry-state organic aerosol scattering. The overall organic aerosol hygroscopicity parameter κOA was quantified directly through hygroscopicity closure, and hygroscopicity parameters of SOA components were further retrieved using a multilinear regression model by assuming hydrophobic properties of primary organic aerosols. The highest water uptake ability of MOOA among organic aerosol factors was revealed with κMOOA reaching 0.23, thus further enhancing the fractional contribution of MOOA in ambient organic aerosol scattering. In particular, the scattering abilities of MOOA were found to be even higher than those of ammonium nitrate under RH of <70 %, which was identified as the most efficient inorganic scattering aerosol component, demonstrating that MOOA had the strongest scattering abilities in ambient air (average RH of 57 %) during winter in Guangzhou. During the observation period, secondary aerosols contributed dominantly to visibility degradation (∼70 %), with substantial contributions from MOOA (16 % on average), demonstrating significant impacts of MOOA on visibility degradation. The findings of this study demonstrate that more attention needs to be paid to SOA property changes in future visibility improvement investigations. Also, more comprehensive studies on MOOA physical properties and chemical formation are needed to better parameterize its radiative effects in models and implement targeted control strategies on MOOA precursors for visibility improvement.
Long-term monitoring of cloud water chemistry at Whiteface Mountain: the emergence of a new chemical regime

Abstract. Atmospheric aqueous chemistry can have profound effects on our environment. The importance of chemistry within the atmospheric aqueous phase started gaining widespread attention in the 1970s as there was growing concern over the negative impacts on ecosystem health from acid deposition. Research at mountaintop observatories including Whiteface Mountain (WFM) showed that gas phase sulfur dioxide emissions react in cloud droplets to form sulfuric acid, which also impacted air quality by increasing aerosol mass loadings. The current study updates the long-term trends in cloud water composition at WFM for the period 1994–2021, with special consideration given to samples that have traditionally been excluded from analysis due to inorganic charge imbalance. We emphasize three major findings: (1) a growing abundance of total organic carbon (TOC), with annual median concentrations more than doubling since measurements began in 2009, (2) a growing imbalance between the measured inorganic cations and anions, consistent with independent rain water observations, implying that a substantial fraction of anions are no longer being measured with the historical suite of measurements, and (3) a growing number of samples exhibiting greater ammonium concentrations than sulfate plus nitrate concentrations, which now routinely describes over one-third of samples. Organic acids are identified as the most likely candidates for the missing anions, since the measured inorganic ion imbalance correlates strongly with measured TOC concentrations. An “inferred cloud droplet pH” is introduced to estimate the pH of the vast majority of cloud droplets as they reside in the atmosphere using a simple method to account for the expected mixing state of calcium and magnesium containing particles. While the inferred cloud droplet pH closely matches the measured bulk cloud water pH during the early years of the cloud water monitoring program, a growing discrepancy is found over the latter half of the record. We interpret these observations as indicating a growing fraction of cloud droplet acidity that is no longer accounted for by the measured sulfate, nitrate and ammonium concentrations. Altogether, these observations indicate that the chemical system at WFM has shifted away from a system dominated by sulfate to a system controlled by base cations, reactive nitrogen species and organic compounds. Further research is required to understand the effects on air quality, climate and ecosystem health.
Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method

Abstract. The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.
What Role Does BERT Play in the Neural Machine Translation Encoder?

Pre-trained language models have been widely 001 applied in various natural language processing 002 tasks. But when it comes to neural machine 003 translation, things are a little different. The dif-004 ferences between the embedding spaces created 005 by BERT and NMT encoder may be one of the 006 main reasons for the difficulty of integrating 007 pre-trained LMs into NMT models. Previous 008 studies illustrate the best way of integration is 009 introducing the output of BERT into the en-010 coder with some extra modules. Nevertheless, 011 it is still unrevealed whether these additional 012 modules will affect the embedding spaces cre-013 ated by the NMT encoder or not and what kind 014 of information the NMT encoder takes advan-015 tage of from the output of BERT. In this pa-016 per, we start by comparing the changes of em-017 bedding spaces after introducing BERT into 018 the NMT encoder trained on different machine 019 translation tasks. Although the changing trends 020 of these embedding spaces vary, introducing 021 BERT into the NMT encoder will not affect the 022 space of the last layer significantly. Subsequent 023 evaluation on several semantic and syntactic 024 tasks proves the NMT encoder is facilitated by 025 the rich syntactic information contained in the 026 output of BERT to boost the translation quality. 027
Comparative Effects of Aprotinin and Human Recombinant R24K KD1 on Temporal Renal Function in Long-Evans Rats

Bovine aprotinin, a reversible inhibitor of plasmin and kallikrein, has been clinically approved for over two decades to prevent peri-operative blood loss during cardiac surgery. However, due to post-operative renal dysfunction in thousands of these patients, aprotinin was voluntarily withdrawn from the market. Our earlier studies indicated that a Arg24Lys mutant of the first Kunitz-type domain of human tissue factor pathway inhibitor-2 (R24K KD1) exhibited plasmin inhibitory activity equivalent to aprotinin in-vitro . In this study, we compared the effects on renal function following infusion of aprotinin and recombinant R24K KD1 in chronically-instrumented, conscious rats. Aprotinin-infused rats exhibited statistically significant decreases in glomerular filtration rate and effective renal plasma flow relative to rats infused with PBS or R24K KD1 dissolved in PBS. In addition, aprotinin-treated rats exhibited marked increases in serum creatinine, blood urea nitrogen, urinary protein and effective renal vascular resistance, whereas these renal parameters remained essentially unchanged in vehicle and R24K KD1-treated rats over a one-week period. Moreover, using a highly sensitive apoptosis detection assay, a significant increase in the rate of early and late apoptotic events in renal tubule cells occurred in aprotinin-treated rats relative to R24K KD1-treated rats. In addition, histological examination of the rat kidney revealed markedly higher levels of protein reabsorption droplets in the aprotinin-infused rats. Collectively, our data provide suggestive evidence that R24K KD1 does not induce the renal dysfunction associated with aprotinin, and may be an effective clinical alternative to aprotinin as an antifibrinolytic agent in cardiac surgery.