Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Continuation Values in an FCC Spectrum Auction

The US government auctions licenses for mobile phone spectr um using a simultaneous, ascending bids auction design. While this auction design ha s many novel features, previous theoretical and experimental work also suggests that it cou ld generate equilibria that are inefficient, collusive or display other undesirable properti es. We empirically examine bidding in the C Block of the U.S. spectrum auctions. We propose a simu lation-based estimator that recovers estimates of bidders’ continuation value functio s at the end of the auction. The value function estimates allow us to test for properties of t he equilibrium allocation. Our estimator contributes to the econometrics of semiparametr ic discrete choice models by allowing agents to choose from very large, but discrete sets of alternatives which is important in other applications including housing or matching. We als o e timate complementarities between the licenses in a package, an important issue when de ciding the optimal geographic area size of mobile phone licenses. Thanks to the NET Institute for financial support. Thanks to h elpful comments from Lawrence Ausubel, Nicholas Economides, Philippe Fevrier, James Heckman, Pau l Milgrom, Gregory Rosston, Andrew Shepherd, Andrew Sweeting and Edward Vytlacil. Thanks to Todd Schuble for help with GIS software, to Peter Cramton for sharing his data on license characteristics, and to Chad Syverson for sharing data on airline travel. Excellent research assistance has been provided by Luis Andres, Steph ani Houghton, Dionysios Kaltis and Wai-Ping Chim. Our email addresses are bajari@econ.duke. edu and fox@uchi cago.edu.
Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultra-dense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback

Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. A robot or other agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This approach contrasts with common agent teaching methods based on demonstrations, critiques, or other guidance that need to be attentively and intentionally provided. In this paper, we first define the general problem of learning from implicit human feedback and then propose to address this problem through a novel data-driven framework, EMPATHIC. This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as rewards, optimality, and advantage; and (2) using such a mapping to learn a task. We instantiate the first stage and three second-stage evaluations of the learned mapping. To do so, we collect a dataset of human facial reactions while participants observe an agent execute a sub-optimal policy for a prescribed training task. We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories.
The radio structure of the narrow-line Seyfert 1 Mrk 783 with VLBA and e-MERLIN

In this paper, we present the analysis of new radio and optical observations of the narrow-line Seyfert 1 galaxy Mrk 783. $1.6\, \mathrm{GHz}$ observations performed with the e-MERLIN interferometer confirm the presence of the diffuse emission previously observed. The Very Long Baseline Array (VLBA) also detects the nuclear source both at $1.6\, \mathrm{GHz}$ (L band) and $5\, \mathrm{GHz}$ (C band). While the L-band image shows only an unresolved core, the C-band image shows the presence of a partially resolved structure at a position angle of 60○. The brightness temperature of the emission in both bands (${\gt}10^6\, \mathrm{K}$) suggests that it is a pc-scale jet produced by the active galactic nucleus. The relatively steep VLBA spectral index (αVLBA = 0.63 ± 0.03) is consistent with the presence of optically thin emission on milliarcsecond scales. Finally, we investigated two possible scenarios that can result in the misalignment between the kpc and pc-scale radio structure detected in the galaxy. We also analysed the optical morphology of the galaxy, which suggests that Mrk 783 underwent a merging in relatively recent times.
A Psychology of Visualization or (External) Representation?

What is a visualization? There is limited utility in trifling with definitions, except insofar as one serves as a tool for communicating and conceptualizing our subject matter; a statement of identity for a community. To establish Visualization Psychology as a viable inter-disciplinary research programme, we must first define the object(s) of our collective inquiry. I propose that while we might refer to the study of "visualization" for the term's colloquial accessibility and pragmatic alignment with other fields, we should consider for exploration a class of artifacts and corresponding processes more expansive and profound: external representations. What follows is an argument for the study of external representation as the foundation for a new interdisciplinary endeavor, and approach to mapping the corresponding problem space.
Derived category of permutation modules and cohomological singularity

We consider the bounded derived category of finitely generated representations of a finite group, with coefficients in a noetherian commutative ring. We construct an invariant that detects which part of this derived category is controlled by permutation modules. This invariant uses group cohomology and takes values in the singularity category of the coefficient ring. We then discuss variants for big triangulated categories and propose a definition of the big derived category of permutation modules.
Engineering In-place (Shared-memory) Sorting Algorithms

We present new sequential and parallel sorting algorithms that now represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. Somewhat surprisingly, part of the speed advantage is due to the additional feature of the algorithms to work in-place, i.e., they do not need a significant amount of space beyond the input array. Previously, the in-place feature often implied performance penalties. Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our new comparison-based algorithm In-place Parallel Super Scalar Samplesort (IPS4o), combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best previous in-place parallel comparison-based sorting algorithms by almost a factor of three. That algorithm also outperforms the best comparison-based competitors regardless of whether we consider in-place or not in-place, parallel or sequential settings. Another surprising result is that IPS4o even outperforms the best (in-place or not in-place) integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new In-place Parallel Super Scalar Radix Sort (IPS2Ra) turns out to be the best algorithm. Claims to have the – in some sense – “best” sorting algorithm can be found in many papers which cannot all be true. Therefore, we base our conclusions on an extensive experimental study involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude. This confirms the claims made about the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications. This is particularly true for integer sorting algorithms giving one reason to prefer comparison-based algorithms for robust general-purpose sorting.
Certification of highly entangled measurements and nonlocality via scalable entanglement-swapping on quantum computers

Increasingly sophisticated quantum computers motivate the exploration of their abilities in certifying genuine quantum phenomena. Here, we demonstrate the power of state-of-the-art IBM quantum computers in correlation experiments inspired by quantum networks. Our experiments feature up to 12 qubits and require the implementation of paradigmatic Bell-State Measurements for scalable entanglement-swapping. First, we demonstrate quantum communication advantages in up to nine-qubit systems while only assuming that the quantum computer operates on qubits. Harvesting these communication advantages, we are able to certify 82 basis elements as entangled in a 512-outcome measurement. Then, we relax the qubit assumption and consider quantum nonlocality in a scenario with multiple independent entangled states arranged in a star configuration. We report quantum violations of source-independent Bell inequalities for up to ten qubits. Our results demonstrate the ability of quantum computers to outperform classical limitations and certify scalable entangled measurements.
Neural Topic Modeling with Cycle-Consistent Adversarial Training

Advances on deep generative models have attracted significant research interest in neural topic modeling. The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics. It is effective in discovering coherent topics but unable to infer topic distributions for given documents or utilize available document labels. To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT. ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics. Adversarial training and cycle-consistent constraints are used to encourage the generator and the encoder to produce realistic samples that coordinate with each other. sToMCAT extends ToMCAT by incorporating document labels into the topic modeling process to help discover more coherent topics. The effectiveness of the proposed models is evaluated on unsupervised/supervised topic modeling and text classification. The experimental results show that our models can produce both coherent and informative topics, outperforming a number of competitive baselines.
Uncertainty Sets for Image Classifiers using Conformal Prediction

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Furthermore, our method generates much smaller predictive sets than alternative methods, since we introduce a regularizer to stabilize the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with a ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving exact coverage with sets that are often factors of 5 to 10 smaller.
Computation of the Taut, the Veering and the Teichmüller Polynomials

Landry, Minsky and Taylor [LMT] introduced two polynomial invariants of veering triangulations - the taut polynomial and the veering polynomial. We give algorithms to compute these invariants. In their definition [LMT] use only the upper track of the veering triangulation, while we consider both the upper and the lower track. We prove that the lower and the upper taut polynomials are equal. However, we show that there are veering triangulations whose lower and upper veering polynomials are different. [LMT] proved that when a veering triangulation is layered, the taut polynomial is equal to the Teichmuller polynomial of the corresponding fibred face of the Thurston norm ball. Thus the algorithm presented in this paper computes the Teichmuller polynomial of a fully-punctured fibred face. We generalise this algorithm to the case of fibred faces which are not fully-punctured.
Breaking the Memory Wall for AI Chip with a New Dimension

Recent advancements in deep learning have led to the widespread adoption of artificial intelligence (AI) in applications such as computer vision and natural language processing. As neural networks become deeper and larger, AI modeling demands outstrip the capabilities of conventional chip architectures. Memory bandwidth falls behind processing power. Energy consumption comes to dominate the total cost of ownership. Currently, memory capacity is insufficient to support the most advanced NLP models. In this work, we present a 3D AI chip, called Sunrise, with near-memory computing architecture to address these three challenges. This distributed, near-memory computing architecture allows us to tear down the performance-limiting memory wall with an abundance of data bandwidth. We achieve the same level of energy efficiency on 40nm technology as competing chips on 7nm technology. By moving to similar technologies as other AI chips, we project to achieve more than ten times the energy efficiency, seven times the performance of the current state-of-the-art chips, and twenty times of memory capacity as compared with the best chip in each benchmark.
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

We propose \textit{average Localisation-Recall-Precision} (aLRP), a unified, bounded, balanced and ranking-based loss function for both classification and localisation tasks in object detection. aLRP extends the Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018) inspired from how Average Precision (AP) Loss extends precision to a ranking-based loss function for classification (Chen et al., 2020). aLRP has the following distinct advantages: (i) aLRP is the first ranking-based loss function for both classification and localisation tasks. (ii) Thanks to using ranking for both tasks, aLRP naturally enforces high-quality localisation for high-precision classification. (iii) aLRP provides provable balance between positives and negatives. (iv) Compared to on average $\sim$6 hyperparameters in the loss functions of state-of-the-art detectors, aLRP Loss has only one hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP Loss improves its ranking-based predecessor, AP Loss, up to around $5$ AP points, achieves $48.9$ AP without test time augmentation and outperforms all one-stage detectors. Code available at: this https URL .
Isomorphic Data Type Transformations

In stepwise derivations of programs from specifications, data type refinements are common. Many data type refinements involve isomorphic mappings between the more abstract and more concrete data representations. Examples include refinement of finite sets to duplicate-free ordered lists or to bit vectors, adding record components that are functions of the other fields to avoid expensive recomputation, etc. This paper describes the APT (Automated Program Transformations) tools to carry out isomorphic data type refinements in the ACL2 theorem prover and gives examples of their use. Because of the inherent symmetry of isomorphisms, these tools are also useful to verify existing programs, by turning more concrete data representations into more abstract ones to ease verification. Typically, a data type will have relatively few interface functions that access the internals of the type. Once versions of these interface functions have been derived that work on the isomorphic type, higher-level functions can be derived simply by substituting the old functions for the new ones. We have implemented the APT transformations isodata to generate the former, and propagate-iso for generating the latter functions as well as theorems about the generated functions from the theorems about the original functions. Propagate-iso also handles cases where the type is a component of a more complex one such as a list of the type or a record that has a field of the type: the isomorphism on the component type is automatically lifted to an isomorphism on the more complex type. As with all APT transformations, isodata and propagate-iso generate proofs of the relationship of the transformed functions to the originals.
Normalized Laplacians for Gain Graphs

We propose the notion of normalized Laplacian matrix $\mathcal{L}(\Phi)$ for a gain graphs and study its properties in detail, providing insights and counterexamples along the way. We establish bounds for the eigenvalues of $\mathcal{L}(\Phi)$ and characterize the classes of graphs for which equality holds. The relationships between the balancedness, bipartiteness, and their connection to the spectrum of $\mathcal{L}(\Phi)$ are also studied. Besides, we extend the edge version of eigenvalue interlacing for the gain graphs. Thereupon, we determine the coefficients for the characteristic polynomial of $\mathcal{L}(\Phi)$.
Bridging the Gap on Early Identification and Treatment of Mental, Neurological and Substance Use Disorders in Sub-Saharan Africa

Although communicable diseases remain a priority in Sub-Saharan Africa (SSA), the demographic, epidemiologic, and nutritional transitions occurring in the region is expected to change the narrative to non-communicable diseases. Mental, neurological, and substance (MNS) use disorders contribute substantially to the Global Burden of Disease (GBD) worldwide, with MNS accounting for about 350 million Disability-Adjusted Life Years (DALYs) lost per annum in Sub-Saharan Africa. This picture is made bleaker by the huge treatment gap in the sub-region with over 90% of persons with MNS disorders lacking access to optimal health care services. The Mental Health Gap Action Program (mhGAP) was designed to bridge this gap in treatment. However, after over a decade it was first launched, access to optimal care for MNS disorders is still lacking in most SSA countries despite some positive efforts. The authors opine that bridging the treatment gap for MNS disorders will require the following: forging a common terminology for MNS disorders in the SSA countries for a comprehensive policy, integration of mental health services into existing health care services (e.g. primary health care, HIV/AIDS and maternal and child health care services), the use of mobile phone or other application-based technology and leveraging on the informal health care systems (e.g. traditional and faith-based healers) to deliver some level of care to persons with MNS disorders.
Unfolding the X-ray spectral curvature of Mkn 421 for further clues

The X-ray observations of Mkn 421 show significant spectral curvature that can be reproduced by a log-parabola function. The spectra can also be fitted by an analytical model considering synchrotron emission from an electron distribution that is accelerated at a shock front with an energy-dependent diffusion(EDD model). The spectral fit of NuSTAR and Swift-XRT observations using EDD model during different flux states reveal the model parameters are strongly correlated. We perform a detailed investigation of this correlation to decipher the information hidden underneath. The model predicts the synchrotron peak energy to be correlated with the peak spectral curvature which is consistent with the case of Mkn 421. Expressing the energy dependence of the diffusion in terms of the magnetohydrodynamic turbulence energy index, it appears the turbulence shifts from Kolmogorov/Kraichnan type to Bohm limit during high flux states. Further, the correlation between the best-fit parameters of EDD model lets us derive an expression for the product of source magnetic field(B) and jet Doppler factor($\delta$) in terms of synchrotron and Compton peak energies. The synchrotron peak energy is obtained using the simultaneous Swift-XRT and NuSTAR observations; whereas, the Compton peak energy is estimated by performing a linear regression analysis of the archival spectral peaks. The deduced $\delta$B varies over a wide range; however, it satisfies reasonably well with the values estimated solely from the spectral peak energies independent of the EDD model. This highlights the plausible connection between the microscopic description of the electron diffusion with the macroscopic quantities deciding the broadband spectrum of Mkn 421.
Evolutionary Facial Expression Recognition

Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, whilst simultaneously retaining classification performance, and in some cases achieving superior performance. We are robustly able to reduce the number of parameters on average by 95% (e.g. from 2M to 100k parameters) with no loss in classification accuracy. The algorithm learns to choose small patches from the image, relative to the nose, which carry the most important information about emotion, and which coincide with typical human choices of important features. Our work implements a novel form attention and shows that evolutionary algorithms are a valuable addition to machine learning in the deep learning era, both for reducing the number of parameters for facial expression recognition and for providing interpretable features that can help reduce bias.