Miracle-dz/UniFAR-FTUs huggingface dataset:
Strongly Budget Balanced Auctions for Multi-Sided Markets

In two-sided markets, Myerson and Satterthwaite's impossibility theorem states that one can not maximize the gain-from-trade while also satisfying truthfulness, individual-rationality and no deficit. Attempts have been made to circumvent Myerson and Satterthwaite's result by attaining approximately-maximum gain-from-trade: the double-sided auctions of McAfee (1992) is truthful and has no deficit, and the one by Segal-Halevi et al. (2016) additionally has no surplus — it is strongly-budget-balanced. They consider two categories of agents — buyers and sellers, where each trade set is composed of a single buyer and a single seller.The practical complexity of applications such as supply chain require one to look beyond two-sided markets. Common requirements are for: buyers trading with multiple sellers of different or identical items, buyers trading with sellers through transporters and mediators, and sellers trading with multiple buyers. We attempt to address these settings.We generalize Segal-Halevi et al. (2016)'s strongly-budget-balanced double-sided auction setting to a multilateral market where each trade set is composed of any number of agent categories. Our generalization refines the notion of competition in multi-sided auctions by introducing the concepts of external competition and trade reduction. We also show an obviously-truthful implementation of our auction using multiple ascending prices.Full version, including omitted proofs and simulation experiments, is available at https://arxiv.org/abs/1911.08094.
The gift of the gab: Are rental scammers skilled at the art of persuasion?

Rental scams are a type of advance fee fraud, in which the scammer tries to get a victim to pay a deposit to rent an apartment of which the scammer pretends to be the landlord. We specifically focused on fraudulent long-term rentals advertised in the UK on Craigslist. After a victim responds to the scammer's advertisement, the scammer attempts to persuade them to transfer money without having seen the property. We were interested in which persuasion techniques scammers use, and in assessing their skill at the art of persuasion. During a period of three weeks, we scraped 2112 letting advertisements, identified the fraudulent advertisements and had 44 conversations of around 4 or 5 emails each with the scammers. Our analysis indicates that Cialdini`s marketing-based social persuasion strategies, such as liking, appeal to authority, and the need for commitment and consistency are extensively implemented by rental scammers. Of Stajano and Wilson's scam-based persuasion strategies, an appeal to sympathy (i.e., kindness) and need for greed were commonly used. We identified two further social persuasion strategies: establishing credibility and removing objections. At a superficial level, rental scammers seem skilled at their job, because they mimic genuine landlords and use a range of effective persuasion techniques. However, when examining their emails more closely, we see they often use pre-scripted emails, their mimicry is often incompetent, and they have a lack of language skills and cultural knowledge that may tip people off. They appear to be the criminal equivalent of a boilerhouse sales operation, a modus operandi that has not previously been studied by cybercrime researchers.
The Star-forming Interstellar Medium of Lyman Break Galaxy Analogs

We present Very Large Telescope SINFONI near-infrared (NIR) integral field spectroscopy of six z ∼ 0.2 Lyman break galaxy “analogs” (LBAs) from which we detect H i, He i, and [Fe ii] recombination lines and multiple H2 rovibrational lines in emission. The Paα kinematics reveal high velocity dispersions and low rotational velocities relative to random motions ( ). Matched-aperture comparisons of Hβ, Hα, and Paα reveal that the nebular color excesses are lower relative to the continuum color excesses than is the case for typical local star-forming systems. We compare observed He i/H i recombination line ratios to photoionization models to gauge the effective temperatures (Teff) of massive ionizing stars, finding that the properties of at least one LBA are consistent with extra heating from an active galactic nucleus (AGN) and/or an overabundance of massive stars. We use H2 1−0 S(·) rovibrational spectra to determine a rotational excitation temperature Tex ∼ 2000 K for warm molecular gas, which we attribute to UV heating in dense photon-dominated regions. Spatially resolved NIR line ratios favor excitation by massive young stars, rather than supernova or AGN feedback. Our results suggest that the local analogs of Lyman break galaxies are primarily subject to strong feedback from recent star formation, with evidence for AGNs and outflows in some cases.
Improving the indistinguishability of single photons from an ion-cavity system

We investigate two schemes for generating indistinguishable single photons, a key feature of quantum networks, from a trapped ion coupled to an optical cavity. Through selection of the initial state in a cavity-assisted Raman transition, we suppress the detrimental effects of spontaneous emission on the photon's coherence length, measuring a visibility of 81(2)% without subtraction of background counts in a Hong-Ou-Mandel interference measurement, the highest reported for an ion-cavity system. In comparison, a visbility of 50(2)% was measured using a more conventional single photon scheme. We demonstrate through numerical analysis of the single photon generation process that the new scheme produces photons of a given indistinguishability with a greater efficiency than the conventional one. Single photon schemes such as the one demonstrated here have applications in distributed quantum computing and communications, which rely on high fidelity entanglement swapping and state transfer through indistinguishable single photons.
The k-Power Domination Number in Some Self-Similar Graphs

The $k$-power domination problem is a problem in graph theory, which has applications in many areas. However, it is hard to calculate the exact $k$-power domination number since determining k-power domination number of a generic graph is a NP-complete problem. We determine the exact $k$-power domination number in two graphs which have the same number of vertices and edges: pseudofractal scale-free web and Sierpi\'nski gasket. The $k$-power domination number becomes 1 for $k\ge2$ in the Sierpi\'nski gasket, while the $k$-power domination number increases at an exponential rate with regard to the number of vertices in the pseudofractal scale-free web. The scale-free property may account for the difference in the behavior of two graphs.
GMAN: A Graph Multi-Attention Network for Traffic Prediction

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.
Neural Network Pruning With Residual-Connections and Limited-Data

Filter level pruning is an effective method to accelerate the inference speed of deep CNN models. Although numerous pruning algorithms have been proposed, there are still two open issues. The first problem is how to prune residual connections. We propose to prune both channels inside and outside the residual connections via a KL-divergence based criterion. The second issue is pruning with limited data. We observe an interesting phenomenon: directly pruning on a small dataset is usually worse than fine-tuning a small model which is pruned or trained from scratch on the large dataset. Knowledge distillation is an effective approach to compensate for the weakness of limited data. However, the logits of a teacher model may be noisy. In order to avoid the influence of label noise, we propose a label refinement approach to solve this problem. Experiments have demonstrated the effectiveness of our method (CURL, Compression Using Residual-connections and Limited-data). CURL significantly outperforms previous state-of-the-art methods on ImageNet. More importantly, when pruning on small datasets, CURL achieves comparable or much better performance than fine-tuning a pretrained small model.
Cosmic Censorship and Holography

Recent developments regarding the Weak Gravity Conjecture suggest the existence of a quantum-gravitational version of Cosmic Censorship. We explore the form that this "quantum Censorship" might take in a concrete example, provided by the five-dimensional asymptotically AdS Kerr spacetime. Classical Censorship permits AdS$_5$-Kerr black holes with arbitrarily large angular momenta per unit mass, despite the fact that these black holes are holographically dual to four-dimensional field theories describing strongly coupled matter with \emph{bounded} vorticity. We argue that "quantum Censorship" should forbid this, and we explain the specific way in which this works when these black holes are embedded in string theory. We are guided by the proposed holographic duality of AdS$_5$ black holes with field theories which under some circumstances resemble the one describing the Quark-Gluon Plasma.
Thick-Net: Parallel Network Structure for Sequential Modeling

Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper structures. However, the former becomes more prone to overfitting, while the latter is difficult to optimize. In this paper, we propose a simple new model named Thick-Net, by expanding the network from another dimension: thickness. Multiple parallel values are obtained via more sets of parameters in each hidden state, and the maximum value is selected as the final output among parallel intermediate outputs. Notably, Thick-Net can efficiently avoid overfitting, and is easier to optimize than the vanilla structures due to the large dropout affiliated with it. Our model is evaluated on four sequential tasks including adding problem, permuted sequential MNIST, text classification and language modeling. The results of these tasks demonstrate that our model can not only improve accuracy with faster convergence but also facilitate a better generalization ability.
The Masses of Isolated Neutron Stars Inferred from the Gravitational Redshift Measurements

For some neutron stars (NSs) in the binary systems, the masses have been accurately measured. While for the isolated neutron stars (INSs), no mass measurement has been reported yet. The situation will change soon thanks to the successful performance of the Neutron Star Interior Composition Explorer (NICER), with which the radius and mass of the isolated PSR J0030+0451 can be simultaneously measured. For most INSs, no mass measurements are possible for NICER because of observational limitations. Benefiting from recent significant progress made on constraining the equation of state of NSs, in this work we propose a way to estimate the masses of the INSs with the measured gravitational redshifts. We apply our method to RX J1856.5-3754, RX J0720.4-3125, and RBS 1223, three members of “The Magnificent Seven” (M7), and estimate their masses to be , , and , respectively. These masses are consistent with that of binary NS systems, suggesting no evidence for experiencing significant accretion of these isolated objects.
Fair Learning-to-Rank from Implicit Feedback

Addressing unfairness in rankings has become an increasingly important problem due to the growing influence of rankings in critical decision making, yet existing learning-to-rank algorithms suffer from multiple drawbacks when learning fair ranking policies from implicit feedback. Some algorithms suffer from extrinsic reasons of unfairness due to inherent selection biases in implicit feedback leading to rich-get-richer dynamics. While those that address the biased nature of implicit feedback suffer from intrinsic reasons of unfairness due to the lack of explicit control over the allocation of exposure based on merit (i.e, relevance). In both cases, the learned ranking policy can be unfair and lead to suboptimal results. To this end, we propose a novel learning-to-rank framework, FULTR, that is the first to address both intrinsic and extrinsic reasons of unfairness when learning ranking policies from logged implicit feedback. Considering the needs of various applications, we define a class of amortized fairness of exposure constraints with respect to items based on their merit, and propose corresponding counterfactual estimators of disparity (aka unfairness) and utility that are also robust to click noise. Furthermore, we provide an efficient algorithm that optimizes both utility and fairness via a policy-gradient approach. To show that our proposed algorithm learns accurate and fair ranking policies from biased and noisy feedback, we provide empirical results beyond the theoretical justification of the framework.
Characterization of a 15-μm cutoff HgCdTe detector array for astronomy

Abstract. The University of Rochester infrared detector group is working together with Teledyne Imaging Sensors to develop mercury cadmium telluride (HgCdTe) 15 μm cutoff wavelength detector arrays for future space missions. To reach the 15-μm cutoff goal, we took an intermediate step by developing four ∼13-μm cutoff wavelength arrays to identify any unforeseen effects related to increasing the cutoff wavelength from the extensively characterized 10-μm cutoff wavelength detector arrays developed for the NEOCam mission. The characterization of the ∼13-μm cutoff wavelength HgCdTe arrays at the University of Rochester allowed us to determine the key dark current mechanisms that limit the performance of these HgCdTe detector arrays at different temperatures and biases when the cutoff wavelength is increased. We present initial dark current and well depth measurements of a 15-μm cutoff array that shows dark current values two orders of magnitude smaller at large reverse bias than would be expected from our previous best structures.
Optimal V2G and Route Scheduling of Mobile Energy Storage Devices Using a Linear Transit Model to Reduce Electricity and Transportation Energy Losses

Mobile energy storage devices (MESDs) operate as medium- or large-sized batteries that can be loaded onto electric trucks and connected to charging stations to provide various ancillary services for distribution grids. This article proposes a new strategy for MESD operation, in which their power outputs and paths are co-optimally scheduled to minimize the total energy loss in both power and transportation networks. The distances moved by MESDs and time at different locations are modeled using a set of linear equations, considering the time-varying traffic flow. The linear transit model is integrated with linearized constraints to support the reliable operation of the distribution grid. In particular, an optimal scheduling problem is formulated considering the maximum limits on incremental variations in bus voltages and line power flows for active and reactive power outputs of MESDs. A mixed-integer linear programming solver can be readily applied to the optimization problem, ensuring the global optimality of the solution. Simulation case studies are carried out under various power and transportation network conditions. The results of these case studies confirm that the proposed strategy using MESDs is effective in reducing total energy losses, compared to conventional methods using stationary batteries and plug-in electric vehicles.
Single-Stage 6D Object Pose Estimation

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, however, is suboptimal: First, it is not end-to-end trainable. Second, training the deep network relies on a surrogate loss that does not directly reflect the final 6D pose estimation task. In this work, we introduce a deep architecture that directly regresses 6D poses from correspondences. It takes as input a group of candidate correspondences for each 3D keypoint and accounts for the fact that the order of the correspondences within each group is irrelevant, while the order of the groups, that is, of the 3D keypoints, is fixed. Our architecture is generic and can thus be exploited in conjunction with existing correspondence-extraction networks so as to yield single-stage 6D pose estimation frameworks. Our experiments demonstrate that these single-stage frameworks consistently outperform their two-stage counterparts in terms of both accuracy and speed.
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.
Application Level Authentication for Ethereum Private Blockchain Atomic Crosschain Transactions

Atomic Crosschain Transaction technology allows composable programming across private Ethereum blockchains. It allows for inter-contract and inter-blockchain function calls that are both synchronous and atomic: if one part fails, the whole call graph of function calls is rolled back. Traditional Ethereum contract functions can limit which accounts can call them by specialised application program logic. This is important as it allows application developers to specify which callers can execute functions that update contract state. In this paper we introduce the strategy required to restrict which contracts on one blockchain can call a function in a contract that is deployed on another blockchain. We show that validating the Originating Blockchain Id (the blockchain the crosschain function call started on), From Blockchain Id, and From Account provides contracts with certainty that a function call came from a specific contract on a specific blockchain.
7 The Symbiontic Nature of Metabolic Evolution

We discuss evolutionary aspects of metabolism, right from the beginning of life to the present day at various levels of organization, thereby including quantitative aspects on the basis of the Dynamic Energy Budget (DEB) theory. We propose a scheme for the evolution of the central metabolism with archaeal as well as eubacterial roots. After an extended initial phase of prokaryotic diversification, cycles of exchange of metabolites between partners in a symbiosis, integration of partners into new individuals and new specializations led to forms of symbiosis of various intensity ranging from loosely living together in species aggregates to several forms of endosymbiosis. While the prokaryotic metabolism evolved into a considerable chemical diversity, the eukaryotic metabolic design remained qualitatively the same but shows a large organizational diversity. Homeostasis of biomass evolved, introducing stoichiometric constraints on production and excretion of products that can be re-utilized; carbohydrates and inorganic nitrogen being the most important ones. This stimulates the formation of symbioses, since most are based on syntrophy, which is probably the basis of the huge biodiversity. A remarkable property of DEB theory for metabolic organization is that organisms of two species that exchange products, and thereby follow the DEB rules, can together follow a symbiogenic route such that the symbiosis behaves as a new organism that itself follows the DEB rules. This property of the reserve dynamics in the DEB theory also explains a possible evolutionary route to homeostasis. The reserve dynamics in DEB theory also plays a key role in linking the kinetics of metabolic pathways to needs of metabolites at the cellular level. Moreover, reserve kinetics, in combination with other DEB elements, explains how metabolic performance depends on body size and why such relationships work out differently within and between species. Apart from the key role of reserves, the dynamic interaction between surface areas and volumes is a basic feature of the DEB theory at all levels of organization (molecules, individuals, ecosystems). The explicit mass and energy balances of the DEB theory facilitates ecosystem modelling as it depends on nutrient exchange. The theoretical interest in this topic concerns the huge range in space-time scales that is involved in understanding the significance of the actions of life within the context of metabolic organization.
Title The 2015 Borneo fires : What have we learned from the 1997 and 2006 El Ninos ? Permalink

Fire activity in Indonesia is strongly linkedwith ElNiño events, whose sea surface temperature (SST) patterns canweaken theWalker circulation to induce drought conditions in the region.Here we show via case analyses and idealized climatemodel simulations that it is the central location of the SST anomalies associatedwith ElNiño, rather than its strength, that ismostly linkedwith thefire occurrence. During our study period of 1997–2015, Eastern Pacific (EP)ElNiño events produced the largestfire events in southern Borneo (i.e., in 1997, 2006, and 2015), while Central Pacific (CP)El Niño events consistently producedminor fire events. The EPElNiño is found to bemore capable than theCPElNiño of weakening theWalker circulation that acts to prolong Borneo’s drought condition fromSeptember toOctober. The extended dry conditions inOctober potentially increase the occurrence offires during EPElNiño years. The 2015fire event owes its occurrence to the location of the 2015 ElNiño but not necessarily its ‘Godzilla’ strength in affecting thefire episodes over southern Borneo. Projecting the location of ElNiño eventsmight bemore important than projecting their strength forfiremanagement in southern Borneo.