Miracle-dz/UniFAR-FTUs huggingface dataset:
Auction Throttling and Causal Inference of Online Advertising Effects

Causally identifying the effect of digital advertising is challenging, because experimentation is expensive, and observational data lacks random variation. This paper identifies a pervasive source of naturally occurring, quasi-experimental variation in user-level ad-exposure in digital advertising campaigns. It shows how this variation can be utilized by ad-publishers to identify the causal effect of advertising campaigns. The variation pertains to auction throttling, a probabilistic method of budget pacing that is widely used to spread an ad-campaign's budget over its deployed duration, so that the campaign's budget is not exceeded or overly concentrated in any one period. The throttling mechanism is implemented by computing a participation probability based on the campaign's budget spending rate and then including the campaign in a random subset of available ad-auctions each period according to this probability. We show that access to logged-participation probabilities enables identifying the local average treatment effect (LATE) in the ad-campaign. We present a new estimator that leverages this identification strategy and outline a bootstrap procedure for quantifying its variability. We apply our method to real-world ad-campaign data from an e-commerce advertising platform, which uses such throttling for budget pacing. We show our estimate is statistically different from estimates derived using other standard observational methods such as OLS and two-stage least squares estimators. Our estimated conversion lift is 110%, a more plausible number than 600%, the conversion lifts estimated using naive observational methods. The full version of the paper : https://arxiv.org/abs/2112.15155
On the Role of Neural Collapse in Transfer Learning

We study the ability of foundation models to learn representations for classification that are transferable to new, unseen classes. Recent results in the literature show that representations learned by a single classifier over many classes are competitive on few-shot learning problems with representations learned by special-purpose algorithms designed for such problems. In this paper we provide an explanation for this behavior based on the recently observed phenomenon that the features learned by overparameterized classification networks show an interesting clustering property, called neural collapse. We demonstrate both theoretically and empirically that neural collapse generalizes to new samples from the training classes, and -- more importantly -- to new classes as well, allowing foundation models to provide feature maps that work well in transfer learning and, specifically, in the few-shot setting.
Leveraging in-domain supervision for unsupervised image-to-image translation tasks via multi-stream generators

Supervision for image-to-image translation (I2I) tasks is hard to come by, but bears significant effect on the resulting quality. In this paper, we observe that for many Unsupervised I2I (UI2I) scenarios, one domain is more familiar than the other, and offers in-domain prior knowledge, such as semantic segmentation. We argue that for complex scenes, figuring out the semantic structure of the domain is hard, especially with no supervision, but is an important part of a successful I2I operation. We hence introduce two techniques to incorporate this invaluable in-domain prior knowledge for the benefit of translation quality: through a novel Multi-Stream generator architecture, and through a semantic segmentation-based regularization loss term. In essence, we propose splitting the input data according to semantic masks, explicitly guiding the network to different behavior for the different regions of the image. In addition, we propose training a semantic segmentation network along with the translation task, and to leverage this output as a loss term that improves robustness. We validate our approach on urban data, demonstrating superior quality in the challenging UI2I tasks of converting day images to night ones. In addition, we also demonstrate how reinforcing the target dataset with our augmented images improves the training of downstream tasks such as the classical detection one.
Electrical manipulation of a hole ‘spin’–orbit qubit in nanowire quantum dot: The nontrivial magnetic field effects

Strong ‘spin’–orbit coupled one-dimensional hole gas is achievable in a Ge nanowire in the presence of a strong magnetic field. The strong magnetic field lifts the two-fold degeneracy in the hole subband dispersions, so that the effective low-energy subband dispersion exhibits strong spin–orbit coupling. Here, we study the electrical spin manipulation in a Ge nanowire quantum dot for both the lowest and second lowest hole subband dispersions. Using a finite square well to model the quantum dot confining potential, we calculate exactly the level splitting of the spin–orbit qubit and the Rabi frequency in the electric-dipole spin resonance. The spin–orbit coupling modulated longitudinal g-factor g so is not only non-vanishing but also magnetic field dependent. Moreover, the spin–orbit couplings of the lowest and second lowest subband dispersions have opposite magnetic dependences, so that the results for these two subband dispersions are totally different. It should be noticed that we focus only on the properties of the hole ‘spin’ instead of the real hole spin.
Visual and Object Geo-localization: A Comprehensive Survey

The concept of geo-localization refers to the process of determining where on earth some `entity' is located, typically using Global Positioning System (GPS) coordinates. The entity of interest may be an image, sequence of images, a video, satellite image, or even objects visible within the image. As massive datasets of GPS tagged media have rapidly become available due to smartphones and the internet, and deep learning has risen to enhance the performance capabilities of machine learning models, the fields of visual and object geo-localization have emerged due to its significant impact on a wide range of applications such as augmented reality, robotics, self-driving vehicles, road maintenance, and 3D reconstruction. This paper provides a comprehensive survey of geo-localization involving images, which involves either determining from where an image has been captured (Image geo-localization) or geo-locating objects within an image (Object geo-localization). We will provide an in-depth study, including a summary of popular algorithms, a description of proposed datasets, and an analysis of performance results to illustrate the current state of each field.
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up

A trivializing map is a field transformation whose Jacobian determinant exactly cancels the interaction terms in the action, providing a representation of the theory in terms of a deterministic transformation of a distribution from which sampling is trivial. Recently, a proof-of-principle study by Albergo, Kanwar and Shanahan [arXiv:1904.12072] demonstrated that approximations of trivializing maps can be `machine-learned' by a class of invertible, differentiable neural models called \textit{normalizing flows}. By ensuring that the Jacobian determinant can be computed efficiently, asymptotically exact sampling from the theory of interest can be performed by drawing samples from a simple distribution and passing them through the network. From a theoretical perspective, this approach has the potential to become more efficient than traditional Markov Chain Monte Carlo sampling techniques, where autocorrelations severely diminish the sampling efficiency as one approaches the continuum limit. A major caveat is that it is not yet understood how the size of models and the cost of training them is expected to scale. As a first step, we have conducted an exploratory scaling study using two-dimensional $\phi^4$ with up to $20^2$ lattice sites. Although the scope of our study is limited to a particular model architecture and training algorithm, initial results paint an interesting picture in which training costs grow very quickly indeed. We describe a candidate explanation for the poor scaling, and outline our intentions to clarify the situation in future work.
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques

Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep neural networks demand substantial compute resources during training and inference. The machine learning community has mainly focused on model-level optimizations such as architectural compression of deep learning models, while the system community has focused on implementation-level optimization. In between, various arithmetic-level optimization techniques have been proposed in the arithmetic community. This article provides a survey on resource-efficient deep learning techniques in terms of model-, arithmetic-, and implementation-level techniques and identifies the research gaps for resource-efficient deep learning techniques across the three different level techniques. Our survey clarifies the influence from higher to lower-level techniques based on our resource-efficiency metric definition and discusses the future trend for resource-efficient deep learning research.
Quantum secure direct communication with private dense coding using general preshared quantum state

We study quantum secure direct communication by using a general preshared quantum state and a generalization of dense coding. In this scenario, Alice is allowed to apply a unitary on the preshared state to encode her message, and the set of allowed unitaries forms a group. To decode the message, Bob is allowed to apply a measurement across his own system and the system he receives. In the worst scenario, we guarantee that Eve obtains no information for the message even when Eve access the joint system between the system that she intercepts and her original system of the preshared state. For a practical application, we propose a concrete protocol and derive an upper bound of information leakage in the finite-length setting. We also discuss how to apply our scenario to the case with discrete Weyl-Heisenberg representation when the preshared state is unknown.
Fast Graph Subset Selection Based on G-optimal Design

Graph sampling theory extends the traditional sampling theory to graphs with topological structures. As a key part of the graph sampling theory, subset selection chooses nodes on graphs as samples to reconstruct the original signal. Due to the eigen-decomposition operation for Laplacian matrices of graphs, however, existing subset selection methods usually require high-complexity calculations. In this paper, with an aim of enhancing the computational efficiency of subset selection on graphs, we propose a novel objective function based on the optimal experimental design. Theoretical analysis shows that this function enjoys an $\alpha$-supermodular property with a provable lower bound on $\alpha$. The objective function, together with an approximate of the low-pass filter on graphs, suggests a fast subset selection method that does not require any eigen-decomposition operation. Experimental results show that the proposed method exhibits high computational efficiency, while having competitive results compared to the state-of-the-art ones, especially when the sampling rate is low.
GenShare: Sharing Accurate Differentially-Private Statistics for Genomic Datasets with Dependent Tuples

Motivation: Cutting the cost of DNA sequencing technology led to a quantum leap in the availability of genomic data. While sharing genomic data across researchers is an essential driver of advances in health and biomedical research, the sharing process is often infeasible due to data privacy concerns. Differential privacy is one of the rigorous mechanisms utilized to facilitate the sharing of aggregate statistics from genomic datasets without disclosing any private individual-level data. However, differential privacy can still divulge sensitive information about the dataset participants due to the correlation between dataset tuples. Results: Here, we propose GenShare model built upon Laplace-perturbation-mechanism-based DP to introduce a privacy-preserving query-answering sharing model for statistical genomic datasets that include dependency due to the inherent correlations between genomes of individuals (i.e., family ties). We demonstrate our privacy improvement over the state-of-the-art approaches for a range of practical queries including cohort discovery, minor allele frequency, and chi^2 association tests. With a fine-grained analysis of sensitivity in the Laplace perturbation mechanism and considering joint distributions, GenShare results near-achieve the formal privacy guarantees permitted by the theory of differential privacy as the queries that computed over independent tuples (only up to 6% differences). GenShare ensures that query results are as accurate as theoretically guaranteed by differential privacy. For empowering the advances in different scientific and medical research areas, GenShare presents a path toward an interactive genomic data sharing system when the datasets include participants with familial relationships.
Mesoscopic energy ranking constraints in the IllustrisTNG simulations

We revisited the problem of mixing in a gravitational N-body system from the point of view of the ordering of coarse-grained cells in the one-particle energy space, here denoted energy ranking preservation (ERP). This effect has been noted for some time in simulations, although individual particle energies and their phase-space variables mix considerably. The present investigation aimed to map ERP in terms of parameters involving the collective range in which it is effective, as well as in terms of global and historical characterisations of gravitational systems evolving towards equilibrium. We examined a subset of the IllustrisTNG cosmological magnetohydrodynamical simulations (TNG50-4 and TNG100-3), considering both their full and dark-only versions. For each simulation, we selected the 20 most massive haloes at redshift z = 0, tracing their ERP fractions back at selected redshift markers (z ={1.0, 5.0, 10.0}), and for a coarse-graining set ranging from 5 to 30 energy bins. At the redshift marker z =1, we found high ERP fractions (above $\sim 80 {{\%}}$) in both simulations, regardless of the coarse-graining level. The decline in ERP fractions with redshift was roughly a function of mass and fractional mass increase in the analysed TNG50-4 haloes, but not in the TNG100-3 ones, indicating a possible relative susceptibility of the ERP effect to mass accretion for haloes less massive than ∼1014 rmM⊙. We confirmed earlier indications in the literature concerning a possible ‘mesoscopic’ constraint operative in a time span of at least several Gyr.
Higher spin dynamics in gravity and $w_{1 + \infty}$ celestial symmetries

In this paper we extract from a large- r expansion of the vacuum Einstein’s equations a dynamical system governing the time evolution of an inﬁnity of higher-spin charges. Upon integration, we evaluate the canonical action of these charges on the gravity phase space. The truncation of this action to quadratic order and the associated charge conservation laws yield an inﬁnite tower of soft theorems. We show that the canonical action of the higher spin charges on gravitons in a conformal primary basis, as well as conformally soft gravitons reproduces the higher spin celestial symmetries derived from the operator product expansion. Finally, we give direct evidence that these charges form a canonical representation of a w 1+ ∞ loop algebra on the gravitational phase space.
Reheating in Runaway Inflation Models via the Evaporation of Mini Primordial Black Holes

We investigate the cosmology of mini Primordial Black Holes (PBHs) produced by large density perturbations that collapse during a stiff fluid domination phase. Such a phase can be realized by a runaway-inflaton model that crosses an inflection point or a sharp feature at the last stage of inflation. Mini PBHs evaporate promptly and reheat the early universe. In addition, we examine two notable implications of this scenario: the possible presence of PBH evaporation remnants in galaxies and a non-zero residual potential energy density for the runaway inflaton that might play the role of the dark energy. We specify the parameter space that this scenario can be realized and we find that a transit PBH domination phase is necessary due to gravitational wave (GW) constraints. A distinct prediction of the scenario is a compound GW signal that might be probed by current and future experiments. We also demonstrate our results employing an explicit inflation model.
High-Precision Calculations of the Higgs Boson Mass

In this paper, we review the status of the computations of the perturbative quantum corrections to the Higgs boson mass in the Standard Model and in its supersymmetric extensions. In particular, supersymmetric theories require a very accurate computation of the Higgs boson mass, which includes corrections even up to the three-loop level, since their predictions are limited by theoretical uncertainties. A discussion about these uncertainties in the context of the Minimal and Next To Minimal Supersymmetric Standard Model is included.
Inferring perceptual decision making parameters from behavior in production and reproduction tasks

Bayesian models of behavior have provided computational level explanations in a range of psychophysical tasks. One fundamental experimental paradigm is the production or reproduction task, in which subjects are instructed to generate an action that either reproduces a previously sensed stimulus magnitude or achieves a target response. This type of task therefore distinguishes itself from other psychophysical tasks in that the responses are on a continuum and effort plays an important role with increasing response magnitude. Based on Bayesian decision theory we present an inference method to recover perceptual uncertainty, response variability, and the cost function underlying human responses. Crucially, the cost function is parameterized such that effort is explicitly included. We present a hybrid inference method employing MCMC sampling utilizing appropriate proposal distributions and an inner loop utilizing amortized inference with a neural network that approximates the mode of the optimal response distribution. We show how this model can be utilized to avoid unidentifiability of experimental designs and that parameters can be recovered through validation on synthetic and application to experimental data. Our approach will enable behavioral scientists to perform Bayesian inference of decision making parameters in production and reproduction tasks.
Data-Free Knowledge Transfer: A Survey

In the last decade, many deep learning models have been well trained and made a great success in various fields of machine intelligence, especially for computer vision and natural language processing. To better leverage the potential of these well-trained models in intra-domain or cross-domain transfer learning situations, knowledge distillation (KD) and domain adaptation (DA) are proposed and become research highlights. They both aim to transfer useful information from a well-trained model with original training data. However, the original data is not always available in many cases due to privacy, copyright or confidentiality. Recently, the data-free knowledge transfer paradigm has attracted appealing attention as it deals with distilling valuable knowledge from well-trained models without requiring to access to the training data. In particular, it mainly consists of the data-free knowledge distillation (DFKD) and source data-free domain adaptation (SFDA). On the one hand, DFKD aims to transfer the intra-domain knowledge of original data from a cumbersome teacher network to a compact student network for model compression and efficient inference. On the other hand, the goal of SFDA is to reuse the cross-domain knowledge stored in a well-trained source model and adapt it to a target domain. In this paper, we provide a comprehensive survey on data-free knowledge transfer from the perspectives of knowledge distillation and unsupervised domain adaptation, to help readers have a better understanding of the current research status and ideas. Applications and challenges of the two areas are briefly reviewed, respectively. Furthermore, we provide some insights to the subject of future research.
Waste Handling Practices at Abattiors: Experience from Ile-ife, Nigeria

Using all the 25 abattoirs identified in Ile-Ife, Nigeria, the study investigated waste-handling practices in this urban sub-system. The study established that 80% of the abattoirs were located along river banks where adequate water could be available. Waste generated from abattoirs include blood, baries, carcass trimming and snout. It was identified that the storage and disposal practices of these waste were not environment-friendly as they are sources of land, air and water pollution. The study concluded that since waste from abattoir is special, efforts should be put in place to making sure that the waste is not allowed to enter into the municipal solid waste management stream.
Pedestrians and Their Survivability at Different Impact Speeds

The United Kingdom's (UK’s) On The Spot (OTS) accident data collection project started in 2000 and continues to investigate 500 crashes per year. Investigations are undertaken minutes after the collision has occurred to gather all the perishable information. At the time of writing over 3,000 crashes involving all road users and all injury severities have been examined. The OTS database provides a unique insight into the prevailing factors that have been seen to cause crashes and the associated human injuries and vehicle and infrastructure damage that have been witnessed by the crash investigation teams. The research objective of this paper is to outline the pre and post-crash circumstances of 108 pedestrian crashes. The nature of the events that led to the collision, including the respective travelling speeds, time and distance from the moment the impact was inevitable are described. The information provided can be used to begin to outline the potential effectiveness of future crash mitigation systems. Further, the impact speeds are correlated to the injuries the pedestrians suffered with respect to the impact partner. Lower limb and head injuries are highlighted to be the most frequently injured body regions. The risk of injury for pedestrians with respect to the cars’ speed at the point of impact is outlined and comparison made with the literature. The small sample size is a limitation to the work, which has not at this stage been proven to be representative of the UK pedestrian accident population. Further, the nature of real world crash investigation means that some of the calculated speed values have reasonably large ranges. However, the work does offer an up to date review of the risk and type of injury versus impact speed for modern vehicles. In addition, the study starts to describe the in-depth pre-crash circumstances witnessed in real life crashes.