Miracle-dz/UniFAR-FTUs huggingface dataset:
Stocking Under Random Demand and Product Variety: Exact Models and Heuristics

Efficient inventory management in the face of product variety is an important part of retail operations management. In this study, we analyze the optimal stocking policy for a retailer, in a setup with a single horizontally differentiated product with an arbitrary number of product variants, stochastic demand, and two‐level consumer choice. The demands for individual product variants are negatively correlated conditional on the total demand. We assume that each customer will purchase one unit of a preferred product variant, if it is in stock, and will seek to buy a second choice product, if the former is not in stock. We formulate an exact model, with Poisson customer arrivals. In order to maintain tractability and characterize an optimal policy analytically, we develop a benchmark model which does not explicitly account for the stochastic nature of customer arrival times. In this model, which is a heuristic approximation of the exact model, we find simple conditions under which the objective of maximizing expected profit is jointly concave in the stocking levels of the product variants; under these conditions we prove that the optimal stocking levels are simply scaled versions of the optimal newsvendor quantities. We then analytically establish a connection between the exact and benchmark models. We develop a dynamic Monte Carlo simulation experiment to gain further insights on the impact of different performance measures on the effectiveness of the optimal policy in the benchmark model and its performance in reference to the exact optimal policy.
More Effort or Better Technologies? On the Effect of Relative Performance Feedback

Relative performance feedback (RPF) allows agents to compare their performance to that of others. Current theory assumes that RPF affects performance by changing the optimal level of effort. We introduce a technology channel in which agents use RPF to improve their technologies. We compare the effort and technology channels by combining three elements: an extensive review, an original model and two field experiments. Under the technology channel, we highlight that RPF increases performance even at the bottom of the distribution and has a cumulative effect across periods. We draw implications for education and social norms.
Conditional Quantiles: An Operator-Theoretical Approach

This paper derives several novel properties of conditional quantiles viewed as nonlinear operators. The results are organized in parallel to the usual properties of the expectation operator. We first define a τ-conditional quantile random set, relative to any sigma-algebra, as a set of solutions of an optimization problem. Then, well-known properties of unconditional quantiles, as translation invariance, comonotonicity, and equivariance to monotone transformations, are generalized to the conditional case. Moreover, a simple proof for Jensen’s inequality for conditional quantiles is provided. We also investigate continuity of conditional quantiles as operators with respect to different topologies and obtain a novel Fatou’s lemma for quantiles. Conditions for continuity in Lp and weak continuity are also provided. We also investigate differentiability properties of quantiles. We demonstrate the validity of the Leibniz’s rule for conditional quantiles for the cases of monotone, as well as separable functions. Finally, although the law of iterated quantiles does not hold in general, we characterize the maximum set of random variables for which this law holds, and investigate its consequences for the infinite composition of conditional quantiles.
The Ex Post Accuracy of Subjective Beliefs: A New Measure and Decomposition

The choices, and hence outcomes, of students, consumers, or investors depend on their beliefs. Inaccurate beliefs lead to poor choices and undesirable outcomes. Recent attempts to elicit probabilistic expectations that capture the full distribution describing agents’ beliefs provide an opportunity to study those beliefs. We propose a novel but intuitive measure of the average ex post accuracy of a group of economic agents’ beliefs, and show how a decomposition of that measure captures different sources of inaccuracy: failure of rational expectations, subjective uncertainty, and aggregate shocks. We illustrate with application to the income expectations of college students and recent graduate in the BEREA panel survey.
Characterization of potential liquid fertilizers obtained by hydrothermal treatment of chicken feathers

Bio‐based fertilizers are currently gaining interest as a method for reducing the use of chemical fertilizers and as a means for safe disposal of biomass waste. Feathers, which are an abundantly available biomass and have a high N content, are a potential source of fertilizer nutrients. In this study, chicken feathers were subjected to hydrothermal treatment (HTT) with lime addition in the temperature range of 140–200°C to obtain a high N containing liquid product that can be used as a liquid fertilizer. The objective of this study was to examine the effect of HTT conditions on the feather solubilization rate and the properties of the liquid products. The results showed that HTT with a reaction temperature of 180°C, a reaction time of 30 min, feather‐water ratio of 1:5, and lime addition is sufficient to solubilize as high as 83% of the chicken feather protein. The produced liquid contained nitrogen of up to 3.4% and total amino acid content of 13.6%. These results indicate the potential utilization of soluble hydrolyzed feather obtained from the HTT process for liquid N fertilizer. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 375–382, 2018
Capacity Constraints in Hedge Funds: The Relation between Fund Performance and Cohort Size

Abstract We provide evidence consistent with scale diseconomies for hedge funds being related to the aggregate assets pursing particular investment strategies. This study extends Forsberg, Gallagher and Warren who identified skilled managers with persistent performance by forming peer cohorts of hedge funds using return correlations. Our analysis shows fund performance had a significant negative relation with cohort size, while the relation with fund size is inconsistent across specifications but evident where funds faced limited competition. We also document a weaker relation between performance and inflows where funds faced less competition, suggesting that cohort structure might influence propensity to accept assets.
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer

Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks. Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore the application of neuroevolution, a form of neural architecture search inspired by biological evolution, in the design of 2D human pose networks for the first time. Additionally, we propose a new weight transfer scheme that enables us to accelerate neuroevolution in a flexible manner. Our method produces network designs that are more efficient and more accurate than state-of-the-art hand-designed networks. In fact, the generated networks process images at higher resolutions using less computation than previous hand-designed networks at lower resolutions, allowing us to push the boundaries of 2D human pose estimation. Our base network designed via neuroevolution, which we refer to as EvoPose2D-S, achieves comparable accuracy to SimpleBaseline while being 50% faster and $12.7\times $ smaller in terms of file size. Our largest network, EvoPose2D-L, achieves new state-of-the-art accuracy on the Microsoft COCO Keypoints benchmark, is $4.3\times $ smaller than its nearest competitor, and has similar inference speed. The code is publicly available at https://github.com/wmcnally/evopose2d.
Improved rates for prediction and identification of partially observed linear dynamical systems

Identiﬁcation of a linear time-invariant dynamical system from partial observations is a fundamental problem in control theory. Particularly challenging are systems exhibiting long-term memory. A natural question is how to learn such systems with non-asymptotic statistical rates depending on the inherent dimensionality (order) 𝑑 of the system, rather than on the possibly much larger memory length. We propose an algorithm that given a single trajectory of length 𝑇 with gaussian observation noise, learns the system with a near-optimal rate of ̃︀ 𝑂 (︂√︁ 𝑑𝑇 )︂ in ℋ 2 error, with only logarithmic, rather than polynomial dependence on memory length. We also give bounds under process noise and improved bounds for learning a realization of the system. Our algorithm is based on multiscale low-rank approximation: SVD applied to Hankel matrices of geometrically increasing sizes. Our analysis relies on careful application of concentration bounds on the Fourier domain—we give sharper concentration bounds for sample covariance of correlated inputs and for ℋ ∞ norm estimation, which may be of independent
Empathetic Dialog Generation with Fine-Grained Intents

Empathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on pre-defined emotion labels to guide the response generation, or use deterministic rules to decide the emotion of the response. With the advent of advanced language models, it is possible to learn subtle interactions directly from the dataset, providing that the emotion categories offer sufficient nuances and other non-emotional but emotional regulating intents are included. In this paper, we describe how to incorporate a taxonomy of 32 emotion categories and 8 additional emotion regulating intents to succeed the task of empathetic response generation. To facilitate the training, we also curated a large-scale emotional dialog dataset from movie subtitles. Through a carefully designed crowdsourcing experiment, we evaluated and demonstrated how our model produces more empathetic dialogs compared with its baselines.
Killing Tensor and Carter Constant for Painlevé–Gullstrand Form of Lense–Thirring Spacetime

Recently, the authors have formulated and explored a novel Painlevé–Gullstrand variant of the Lense–Thirring spacetime, which has some particularly elegant features, including unit-lapse, intrinsically flat spatial 3-slices, and some particularly simple geodesics—the “rain” geodesics. At the linear level in the rotation parameter, this spacetime is indistinguishable from the usual slow-rotation expansion of Kerr. Herein, we shall show that this spacetime possesses a nontrivial Killing tensor, implying separability of the Hamilton–Jacobi equation. Furthermore, we shall show that the Klein–Gordon equation is also separable on this spacetime. However, while the Killing tensor has a 2-form square root, we shall see that this 2-form square root of the Killing tensor is not a Killing–Yano tensor. Finally, the Killing-tensor-induced Carter constant is easily extracted, and now, with a fourth constant of motion, the geodesics become (in principle) explicitly integrable.
Elementary formulae for social distancing scenarios: Application to COVID-19 mitigation via feedback control

Social distancing has been enacted in order to mitigate the spread of COVID-19. Like many authors, we adopt the classic epidemic SIR model, where the infection rate is the control variable. Its differential flatness property yields elementary closed-form formulae for open-loop social distancing scenarios, where, for instance, the increase of the number of uninfected people may be taken into account. Those formulae might therefore be useful to decision makers. A feedback loop stemming from model-free control leads to a remarkable robustness with respect to severe uncertainties of various kinds. Although an identification procedure is presented, a good knowledge of the recovery rate is not necessary for our control strategy. Several convincing computer experiments are displayed.
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors

Understanding users' activities from head-mounted cameras is a fundamental task for Augmented and Virtual Reality (AR/VR) applications. A typical approach is to train a classifier in a supervised manner using data labeled by humans. This approach has limitations due to the expensive annotation cost and the closed coverage of activity labels. A potential way to address these limitations is to use self-supervised learning (SSL). Instead of relying on human annotations, SSL leverages intrinsic properties of data to learn representations. We are particularly interested in learning egocentric video representations benefiting from the head-motion generated by users' daily activities, which can be easily obtained from IMU sensors embedded in AR/VR devices. Towards this goal, we propose a simple but effective approach to learn video representation by learning to tell the corresponding pairs of video clip and head-motion. We demonstrate the effectiveness of our learned representation for recognizing egocentric activities of people and dogs.
Late reverberation suppression using U-nets

In real-world settings, speech signals are almost always affected by reverberation produced by the working environment; these corrupted signals need to be \emph{dereverberated} prior to performing, e.g., speech recognition, speech-to-text conversion, compression, or general audio enhancement. In this paper, we propose a supervised dereverberation technique using \emph{U-nets with skip connections}, which are fully-convolutional encoder-decoder networks with layers arranged in the form of an"U"and connections that"skip"some layers. Building on this architecture, we address speech dereverberation through the lens of Late Reverberation Suppression (LS). Via experiments on synthetic and real-world data with different noise levels and reverberation settings, we show that our proposed method termed"LS U-net"improves quality, intelligibility and other performance metrics compared to the original U-net method and it is on par with the state-of-the-art GAN-based approaches.
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication

Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews has been mainly focused on a single numerical cause, e.g., ratings or sentiment scores. We argue that such notions of causes entail three key limitations: they solely consider the effects of single numerical causes and ignore different effects of multiple aspects -- e.g., Food, Service -- embedded in the textual reviews; they assume the absence of hidden confounders in observational studies, e.g., consumers' personal preferences; and they overlook the indirect effects of numerical causes that can potentially cancel out the effect of textual reviews on business revenue. We thereby propose an alternative perspective to this single-cause-based effect estimation of online reviews: in the presence of hidden confounders, we consider multi-aspect textual reviews, particularly, their total effects on business revenue and direct effects with the numerical cause -- ratings -- being the mediator. We draw on recent advances in machine learning and causal inference to together estimate the hidden confounders and causal effects. We present empirical evaluations using real-world examples to discuss the importance and implications of differentiating the multi-aspect effects in strategizing business operations.
A study of first-passage time minimization via Q-learning in heated gridworlds

Optimization of first-passage times is required in applications ranging from nanobots navigation to market trading. In such settings, one often encounters unevenly distributed noise levels across the environment. We extensively study how a learning agent fares in 1- and 2- dimensional heated gridworlds with an uneven temperature distribution. The results show certain bias effects in agents trained via simple tabular Q-learning, SARSA, Expected SARSA and Double Q-learning. Namely, the state-dependency of noise triggers convergence to suboptimal solutions and the respective policies follow them for practically long learning times. The high learning rate prevents exploration of regions with higher temperature, while the low enough rate increases the presence of agents in such regions. These biases of temporal-difference-based reinforcement learning methods may have implications for their application in real-world physical scenarios and agent design.
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression

Block based motion estimation is integral to inter prediction processes performed in hybrid video codecs. Prevalent block matching based methods that are used to compute block motion vectors (MVs) rely on computationally intensive search procedures. They also suffer from the aperture problem, which tends to worsen as the block size is reduced. Moreover, the block matching criteria used in typical codecs do not account for the resulting levels of perceptual quality of the motion compensated pictures that are created upon decoding. Towards achieving the elusive goal of perceptually optimized motion estimation, we propose a search-free block motion estimation framework using a multi-stage convolutional neural network, which is able to conduct motion estimation on multiple block sizes simultaneously, using a triplet of frames as input. This composite block translation network (CBT-Net) is trained in a self-supervised manner on a large database that we created from publicly available uncompressed video content. We deploy the multi-scale structural similarity (MS-SSIM) loss function to optimize the perceptual quality of the motion compensated predicted frames. Our experimental results highlight the computational efficiency of our proposed model relative to conventional block matching based motion estimation algorithms, for comparable prediction errors. Further, when used to perform inter prediction in AV1, the MV predictions of the perceptually optimized model result in average Bjøntegaard-delta rate (BD-rate) improvements of −1.73% and −1.31% with respect to the MS-SSIM and Video Multi-Method Assessment Fusion (VMAF) quality metrics, respectively, as compared to the block matching based motion estimation system employed in the SVT-AV1 encoder.
Online Application Guidance for Heterogeneous Memory Systems

As scaling of conventional memory devices has stalled, many high-end computing systems have begun to incorporate alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as higher bandwidth with lower capacity or vice versa, they are typically packaged alongside conventional SDRAM in a heterogeneous memory architecture. To utilize the different types of memory efficiently, new data management strategies are needed to match application usage to the best available memory technology. However, current proposals for managing heterogeneous memories are limited, because they either (1) do not consider high-level application behavior when assigning data to different types of memory or (2) require separate program execution (with a representative input) to collect information about how the application uses memory resources. This work presents a new data management toolset to address the limitations of existing approaches for managing complex memories. It extends the application runtime layer with automated monitoring and management routines that assign application data to the best tier of memory based on previous usage, without any need for source code modification or a separate profiling run. It evaluates this approach on a state-of-the-art server platform with both conventional DDR4 SDRAM and non-volatile Intel Optane DC memory, using both memory-intensive high-performance computing (HPC) applications as well as standard benchmarks. Overall, the results show that this approach improves program performance significantly compared to a standard unguided approach across a variety of workloads and system configurations. The HPC applications exhibit the largest benefits, with speedups ranging from 1.4× to 7× in the best cases. Additionally, we show that this approach achieves similar performance as a comparable offline profiling-based approach after a short startup period, without requiring separate program execution or offline analysis steps.
ASR Rescoring and Confidence Estimation with Electra

In automatic speech recognition (ASR) rescoring, the hypothesis with the fewest errors should be selected from the $n$-best list using a language model (LM). However, LMs are usually trained to maximize the likelihood of correct word sequences, not to detect ASR errors. We propose an ASR rescoring method for directly detecting errors with ELECTRA, which is originally a pre-training method for NLP tasks. ELECTRA is pre-trained to predict whether each word is replaced by BERT or not, which can simulate ASR error detection on large text corpora. To make this pre-training closer to ASR error detection, we further propose an extended version of ELECTRA called phone-attentive ELECTRA (P-ELECTRA). In the pre-training of P-ELECTRA, each word is replaced by a phone-to-word conversion model, which leverages phone information to generate acoustically similar words. Since our rescoring method is optimized for detecting errors, it can also be used for word-level confidence estimation. Experimental evaluations on the Librispeech and TED-LIUM2 corpora show that our rescoring method with ELECTRA is competitive with conventional rescoring methods with faster inference. ELECTRA also performs better in confidence estimation than BERT because it can learn to detect inappropriate words not only in fine-tuning but also in pre-training.
A Critique of Strictly Batch Imitation Learning

Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model (Jarrett et al., 2020), with the hope of out-performing standard behavioral cloning. We suggest that notational issues obscure how the psuedo-state visitation distribution the authors propose to optimize might be disconnected from the policy’s true state visitation distribution. We further construct natural examples where the parameter coupling advocated by Jarrett et al. leads to inconsistent estimates of the expert’s policy, unlike behavioral cloning.
The transformation of edge-regular and pseudo strongly regular graphs under graph operations

The graph G is said to be strongly regular with parameters (n, k, λ, μ) if the following conditions hold: (1) each vertex has k neighbours; (2) any two adjacent vertices of G have λ common neighbours; (3) any two non-adjacent vertices of G have μ common neighbours. In this paper we study two weaker notions of strongly regular graphs. A graph satisfying the conditions (1) and (2) is called an edge-regular graph with parameters (n, k, λ). We call a graph satisfying the conditions (1) and (3) a pseudo strongly regular graph with parameters ∗Email : jeepamoljp@gmail.com †E-mail : aparnaren@gmail.com, aparnals@cusat.ac. in ‡E-mail : greg.markowsky@monash.edu 1 ar X iv :2 11 0. 02 10 1v 1 [ m at h. C O ] 5 O ct 2 02 1 (n, k, μ). In this paper we study the impact of various graph operations on edge regular graphs and pseudo strongly regular graphs.