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## Oral Session 10

Moderators: Andreas Damianou · Piotr Mirowski · Thomas Kipf

Thu 6 May 3 a.m. PDT — 5:40 a.m. PDT

Abstract:

Chat is not available.

Thu 6 May 3:00 - 3:15 PDT

(Oral)
##### What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study

Marcin Andrychowicz · Anton Raichuk · Piotr Stanczyk · Manu Orsini · Sertan Girgin · Raphaël Marinier · Léonard Hussenot-Desenonges · Matthieu Geist · Olivier Pietquin · Marcin Michalski · Sylvain Gelly · Olivier Bachem

In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such "choices" in a unified on-policy deep actor-critic framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for the training of on-policy deep actor-critic RL agents.

Thu 6 May 3:15 - 3:25 PDT

(Spotlight)
##### Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

Deunsol Yoon · Sunghoon Hong · Byung-Jun Lee · Kee-Eung Kim

Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exempliﬁed by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked ﬁrst in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efﬁciency in every test scenarios. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids.

Thu 6 May 3:25 - 3:35 PDT

(Spotlight)
##### UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers

Siyi Hu · Fengda Zhu · Xiaojun Chang · Xiaodan Liang

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over tasks with diverse levels of difficulty (e.g. 3 vs 3 or 5 vs 6 multi-agent games). In this paper, we make the first attempt to explore a universal multi-agent reinforcement learning pipeline, designing one single architecture to fit tasks with the requirement of different observation and action configurations. Unlike previous RNN-based models, we utilize a transformer-based model to generate a flexible policy by decoupling the policy distribution from the intertwined input observation with an importance weight measured by the merits of the self-attention mechanism. Compared to a standard transformer block, the proposed model, named as Universal Policy Decoupling Transformer (UPDeT), further relaxes the action restriction and makes the multi-agent task's decision process more explainable. UPDeT is general enough to be plugged into any multi-agent reinforcement learning pipeline and equip them with strong generalization abilities that enables the handling of multiple tasks at a time. Extensive experiments on large-scale SMAC multi-agent competitive games demonstrate that the proposed UPDeT-based multi-agent reinforcement learning achieves significant results relative to state-of-the-art approaches, demonstrating advantageous transfer capability in terms of both performance and training speed (10 times faster).

Thu 6 May 3:35 - 3:45 PDT

(Spotlight)
##### Quantifying Differences in Reward Functions

Adam Gleave · Michael Dennis · Shane Legg · Stuart Russell · Jan Leike

For many tasks, the reward function is inaccessible to introspection or too complex to be specified procedurally, and must instead be learned from user data. Prior work has evaluated learned reward functions by evaluating policies optimized for the learned reward. However, this method cannot distinguish between the learned reward function failing to reflect user preferences and the policy optimization process failing to optimize the learned reward. Moreover, this method can only tell us about behavior in the evaluation environment, but the reward may incentivize very different behavior in even a slightly different deployment environment. To address these problems, we introduce the Equivalent-Policy Invariant Comparison (EPIC) distance to quantify the difference between two reward functions directly, without a policy optimization step. We prove EPIC is invariant on an equivalence class of reward functions that always induce the same optimal policy. Furthermore, we find EPIC can be efficiently approximated and is more robust than baselines to the choice of coverage distribution. Finally, we show that EPIC distance bounds the regret of optimal policies even under different transition dynamics, and we confirm empirically that it predicts policy training success. Our source code is available at https://github.com/HumanCompatibleAI/evaluating-rewards.

Thu 6 May 3:45 - 3:55 PDT

(Spotlight)
##### Iterative Empirical Game Solving via Single Policy Best Response

Max Smith · Thomas Anthony · Michael Wellman

Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (DRL). At each iteration, DRL is invoked to train a best response to a mixture of opponent policies. The repeated application of DRL poses an expensive computational burden as we look to apply this algorithm to more complex domains. We introduce two variations of PSRO designed to reduce the amount of simulation required during DRL training. Both algorithms modify how PSRO adds new policies to the empirical game, based on learned responses to a single opponent policy. The first, Mixed-Oracles, transfers knowledge from previous iterations of DRL, requiring training only against the opponent's newest policy. The second, Mixed-Opponents, constructs a pure-strategy opponent by mixing existing strategy's action-value estimates, instead of their policies. Learning against a single policy mitigates conflicting experiences on behalf of a learner facing an unobserved distribution of opponents. We empirically demonstrate that these algorithms substantially reduce the amount of simulation during training required by PSRO, while producing equivalent or better solutions to the game.

Thu 6 May 3:55 - 4:05 PDT

(Spotlight)
##### Discovering a set of policies for the worst case reward

Tom Zahavy · Andre Barreto · Daniel J Mankowitz · Shaobo Hou · Brendan ODonoghue · Iurii Kemaev · Satinder Singh

We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward function defined as a linear combination of known features. We consider a specific class of policy compositions which we call set improving policies (SIPs): given a set of policies and a set of tasks, a SIP is any composition of the former whose performance is at least as good as that of its constituents across all the tasks. We focus on the most conservative instantiation of SIPs, set-max policies (SMPs), so our analysis extends to any SIP. This includes known policy-composition operators like generalized policy improvement. Our main contribution is an algorithm that builds a set of policies in order to maximize the worst-case performance of the resulting SMP on the set of tasks. The algorithm works by successively adding new policies to the set. We show that the worst-case performance of the resulting SMP strictly improves at each iteration, and the algorithm only stops when there does not exist a policy that leads to improved performance. We empirically evaluate our algorithm on a grid world and also on a set of domains from the DeepMind control suite. We confirm our theoretical results regarding the monotonically improving performance of our algorithm. Interestingly, we also show empirically that the sets of policies computed by the algorithm are diverse, leading to different trajectories in the grid world and very distinct locomotion skills in the control suite.

Thu 6 May 4:05 - 4:20 PDT

(Q&A)
##### Q&A

Thu 6 May 4:20 - 4:35 PDT

(Oral)
##### Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

Yuan Yin · Vincent Le Guen · Jérémie DONA · Emmanuel d Bezenac · Ibrahim Ayed · Nicolas THOME · patrick gallinari

Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters.

Thu 6 May 4:35 - 4:45 PDT

(Spotlight)
##### Unlearnable Examples: Making Personal Data Unexploitable

Hanxun Huang · Xingjun Ma · Sarah Erfani · James Bailey · Yisen Wang

The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of personal data for training commercial models. It is thus crucial to develop methods to prevent unauthorized data exploitation. This paper raises the question: can data be made unlearnable for deep learning models? We present a type of error-minimizing noise that can indeed make training examples unlearnable. Error-minimizing noise is intentionally generated to reduce the error of one or more of the training example(s) close to zero, which can trick the model into believing there is "nothing" to learn from these example(s). The noise is restricted to be imperceptible to human eyes, and thus does not affect normal data utility. We empirically verify the effectiveness of error-minimizing noise in both sample-wise and class-wise forms. We also demonstrate its flexibility under extensive experimental settings and practicability in a case study of face recognition. Our work establishes an important ﬁrst step towards making personal data unexploitable to deep learning models.

Thu 6 May 4:45 - 4:55 PDT

(Spotlight)
##### Self-supervised Visual Reinforcement Learning with Object-centric Representations

Andrii Zadaianchuk · Maximilian Seitzer · Georg Martius

Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model. We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects.

Thu 6 May 4:55 - 5:05 PDT

(Spotlight)
##### On Self-Supervised Image Representations for GAN Evaluation

Stanislav Morozov · Andrey Voynov · Artem Babenko

The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature.

In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading.

Thu 6 May 5:05 - 5:15 PDT

(Spotlight)
##### Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Shangqing Liu · Yu Chen · Xiaofei Xie · Siow Jing Kai · Yang Liu

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR.

Thu 6 May 5:15 - 5:25 PDT

(Spotlight)
##### Practical Real Time Recurrent Learning with a Sparse Approximation

Jacob Menick · Erich Elsen · Utku Evci · Simon Osindero · Karen Simonyan · Alex Graves

Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights "online" (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse. We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix. SnAp only tracks the influence of a parameter on hidden units that are reached by the computation graph within $n$ timesteps of the recurrent core. SnAp with $n=1$ is no more expensive than backpropagation but allows training on arbitrarily long sequences. We find that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with $n=2$ remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online.

Thu 6 May 5:25 - 5:40 PDT

(Q&A)


text:
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01wp988j85k
 Title: Regularization of Linear Systems with Sparsity Constraints with Applications to Large Scale Inverse Problems Authors: Voronin, Sergey Advisors: Daubechies, Ingrid Contributors: Applied and Computational Mathematics Department Keywords: geotomographyinverse problemiterative methodregularizationsparsity Subjects: Applied mathematics Issue Date: 2012 Publisher: Princeton, NJ : Princeton University Abstract: This thesis is about numerical methods for the regularization of large scale inverse problems with sparsity constraints. Some new methods are proposed, and applied to an inverse problem from Geotomography, the goal of which is to determine latitudinal and longitudinal corrections to a spherically symmetric wave velocity model of the Earth's interior. The problem involves a very large, badly conditioned linear system, whose solutions, expressed in an intricate coordinate system, can be sparsely represented under the action of a wavelet transformation. The methods we develop and analyze in this thesis are simple to implement, efficient and easy to parallelize on large machines. In addition, the convergence analysis for the new algorithms assumes minimal conditions on the linear systems they are applied to. This thesis is organized as follows. After the introduction, we give in Chapter 2, an overview of existing schemes for regularization with sparsity constraints, and we introduce new material developed in the remainder of the thesis. Chapter 3 introduces a new firm thresholding based scheme that overcomes some shortcomings of soft thresholding; this scheme applies less penalty to the large coefficients of the iterates, while producing solutions of comparable sparsity. Chapter 4 introduces two novel methods based on an iteratively reweighted least squares strategy. These methods are designed to minimize a new more general sparsity promoting functional, which is especially useful for structured sparse problems, such as those encountered under the action of a wavelet transform. Detailed convergence analysis is provided for these two new algorithms. Chapter 5 discusses techniques that are useful for numerical implementation, such as a fast implementation of a randomized low rank SVD approximation and matrix column norm estimations, useful for large badly conditioned matrices. Finally, Chapter 6 presents the application, collecting ideas from the previous chapters and applying them to the inverse problem. URI: http://arks.princeton.edu/ark:/88435/dsp01wp988j85k Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog Type of Material: Academic dissertations (Ph.D.) Language: en Appears in Collections: Applied and Computational Mathematics

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# Spatial Effects on Killing Kinetics of Cytotoxic T Lymphocytes

##### Persistent Identifier

This model requires the external file boundary.tiff.

## Introduction

The immune system plays an important role in controlling infections and tumors. Knowledge about the mechanisms through which the immune system accomplishes this can be exploited to develop immunotherapies. A pivotal mechanism involves killing of target cells by Cytotoxic T Lymphocytes (CTLs).

A major limitation of many prior estimates of CTL killing is that analysis is performed on population level data in in vivo settings, with no direct measurements of the killing process. This approach has a number of drawbacks:

1. It can be challenging to accurately assess the frequency of CTLs and target cells.
2. Other immune cells may contribute to the killing process, confounding estimates of the true CTL killing rate.
3. The processes underlying CTL killing are complex and it may be insufficient to describe them with a single, time invariant rate constant.

Besides analysing CTL killing performance at the population level, a potentially useful approach is to analyse CTL killing at the single cell level. Such analysis can yield greater insights into the dynamics of the killing process.

Here, stochastic simulations of single CTLs killing small populations of target cells were developed, showing that multiple-hitting can lead to apparently heterogeneous killing kinetics between otherwise identical CTLs.

## Description

An agent based cellular Potts model (CPM) was employed to generate 2D simulations of CTLs interacting with and killing targets over a period of 12 hours (StopTime = 43200 s). For all CPM simulations the same underlying gamma model of CTL hit generation was maintained as was used for the Monte-Carlo simulations, however several modifications were made that would lead to different (yet not predictable a priori) distributions of hits amongst targets:

• Specifically, instead of allocating hits to all contacted targets with equal probability, target risk of receiving a hit was proportional to the length of the interface between CTL and target ctl.contact.length at the moment of hit generation.
• We also considered the effect of a lower bound on the time (time-step = 60 s with trigger = when-true) required for a CTL to complete a hit, by introducing a delay condition maturity > minhit that prohibited targets from being hit within an initial time window after contacting a CTL, which was reset (immaturing Event) every time the target broke contact with the CTL. Note that the delay condition was applied per target and therefore does not preclude the possibility of CTLs hitting other contacted targets simultaneously.
• We used the CPM model to simulate CTLs (with $\eta,\lambda = 1,2$ or $10$). CTLs hit targets at a constant rate $\lambda$ (intrinsic hitting rate), then targets died after receiving $\eta$ hits (nhits = 5).
• We simulated single- and multiple-hitting scenarios on the basis that the expected (mean) time for one target in contact with a CTL to be killed was 1 hour (hit.chance = nhits/3600 s), i.e., we set $\frac{\lambda}{\eta} = 1$.
• Finally, we varied CTL migration to create two groups of CTLs which we termed ‘high-motility’ (Protrusion[strength] = 10) or ‘low-motility’ (Protrusion[strength] = 2) CTLs.

## Results

Figure 3A shows high-motility in silico CTLs in the CPM simulations:

• Left color scheme: CTLs are red, uncontacted targets are grey, and contacted targets have various shades of blue based on their share of total CTL:target interface, which determines their probability of receiving a hit. Targets are overlaid with the number of hits they have received.
• Right color scheme: Lattice sites inhabited by the CTL are colored according to actin activity. Targets are black, turning yellow after 15 minutes of continuous contact with the CTL. Elapsed simulation time is displayed in the upper left corner of the stills, presented in minutes since the first frame shown.

S1 Video: Simulation of high-motility CTL, requiring 5 hits to kill targets. (CC BY 4.0: Beck et al.)

S2 Video: Simulation of low-motility CTL, requiring 5 hits to kill targets. (CC BY 4.0: Beck et al.)

In all videos, CTLs are shown in red whilst uncontacted targets are in grey. Contacted targets are shaded blue based on their share of total CTL:target interface, i.e. the probability that they will receive the next hit generated by the CTL. Targets are overlaid with the number of hits they have received. Elapsed simulation time (hours:minutes) is displayed in the upper right corner of the videos.

For both motility conditions the migration of the CTLs was influenced by the presence of the targets, as CTLs became corralled by surrounding targets. The difference between these models was that high-motility CTLs exhibited an increased propensity to break free from confinement and roam the well. This roaming ensured that over the course of 12 hours the high-motility CTL made new contacts with far greater frequency than low-motility CTLs (Fig 3D), although the average number of simultaneously contacted targets at any time was similar (Fig 3E). Thus, the high-motility CTL is expected to approach the previously modeled ‘all targets at risk’ scenario more closely than the low-motility CTLs.

The resulting datasets were visually similar to realistic microscopy data and could be used to investigate methods for recovering the hitting parameters ($\eta$ and $\lambda$) of CTLs from experimental data under various conditions.

• The total amount of targets killed by each CTL depended on the interaction between the parameters $\lambda$ and $\eta$, the CTL motility, and the presence or absence of the delay condition. In particular, the combination of high motility plus 15 minute delay resulted in a substantial decrease in killing in comparison to the other simulation groups, for all values of $\eta$ (Fig 4A).
• Together with the high rate of contact formation in that group (Fig 3D), this is consistent with targets spending significant time in transient contacts with the CTL, too short to result in successful hit delivery.
• The killing rate of the low-motility CTLs was initially greater than of high-motility CTLs, in particular for large $\eta$ (Fig 4B), due to the more stable nature of the contacts leading to greater accumulation of hits among the contacted targets (Fig 4C).
• High-motility CTLs reduced this deficit over the course of the simulations due to an accumulation of latent hits among uncontacted targets (Fig 4D). These spatial simulations therefore illustrate how CTL:target contact dynamics can play a role in determining killing performance.
• Moreover, since in these models CTLs with the same killing parameters – but different motility parameters – generated different killing kinetics, they are useful to test how underlying killing parameters might be recovered from microscopy data that are similar to data emanating from our realistic simulations.

Here we have used stochastic simulations to show that ‘multiple-hitting’ is a plausible explanation for the heterogeneous and time-inhomogeneous killing activity recently observed for CTLs in vitro:

• We showed that multiple-hitting leads to an increase in realised killing rate over time.
• Moreover, the extent of this late onset killing increases when more hits are required to kill targets, or when a greater number of antigen-presenting targets are simultaneously contacted.
• Furthermore, identical CTLs displayed varying killing performance depending on the number of targets available.
• Simulating CTLs with variable hitting rates, we also found that the killing performance of multiple-hitting CTLs is more heterogeneous than killing of single-hitting CTLs, given similar variation in underlying hitting rate.

## Reference

This model is the original used in the publication, up to technical updates:

R. J. Beck, D. I. Bijker, J. B. Beltman: Heterogeneous, delayed-onset killing by multiple-hitting T cells: Stochastic simulations to assess methods for analysis of imaging data. PLoS Comput. Biol. 16 (7): 1-25, 2020.

## Model

Get this model via:

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<Description>
<Title>Cytotoxic T Lymphocytes
</Title>
<Details>Full title:	Spatial Effects on Killing Kinetics of Cytotoxic T Lymphocytes
Date:	08.09.2021
Authors:	R. J. Beck, D. I. Bijker, J. B. Beltman
Curators:	J. B. Beltman, D. Jahn
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Reference:	This model is the original used in the publication, up to technical updates:
R. J. Beck, D. I. Bijker, J. B. Beltman: Heterogeneous, delayed-onset killing by multiple-hitting T cells: Stochastic simulations to assess methods for analysis of imaging data. PLoS Comput. Biol. 16 (7): 1-25, 2020.
https://doi.org/10.1371/journal.pcbi.1007972</Details>
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## CryptoDB

### Chen-Da Liu-Zhang

#### Publications

Year
Venue
Title
2020
PKC
Topology-Hiding Computation (THC) allows a set of parties to securely compute a function over an incomplete network without revealing information on the network topology. Since its introduction in TCC’15 by Moran et al., the research on THC has focused on reducing the communication complexity, allowing larger graph classes, and tolerating stronger corruption types. All of these results consider a fully synchronous model with a known upper bound on the maximal delay of all communication channels. Unfortunately, in any realistic setting this bound has to be extremely large, which makes all fully synchronous protocols inefficient. In the literature on multi-party computation, this is solved by considering the fully asynchronous model. However, THC is unachievable in this model (and even hard to define), leaving even the definition of a meaningful model as an open problem. The contributions of this paper are threefold. First, we introduce a meaningful model of unknown and random communication delays for which THC is both definable and achievable. The probability distributions of the delays can be arbitrary for each channel, but one needs to make the (necessary) assumption that the delays are independent. The existing fully-synchronous THC protocols do not work in this setting and would, in particular, leak information about the topology. Second, in the model with trusted stateless hardware boxes introduced at Eurocrypt’18 by Ball et al., we present a THC protocol that works for any graph class. Third, we explore what is achievable in the standard model without trusted hardware and present a THC protocol for specific graph types (cycles and trees) secure under the DDH assumption. The speed of all protocols scales with the actual (unknown) delay times, in contrast to all previously known THC protocols whose speed is determined by the assumed upper bound on the network delay.
2020
CRYPTO
Protocols for secure Multi-Party Computation (MPC) can be classified according to the underlying communication model. Two prominent communication models considered in the literature are the synchronous and asynchronous models, which considerably differ in terms of the achievable security guarantees. Synchronous MPC protocols can achieve the optimal corruption threshold $n/2$ and allow every party to give input, but become completely insecure when synchrony assumptions are violated. On the other hand, asynchronous MPC protocols remain secure under arbitrary network conditions, but can tolerate only $n/3$ corruptions and parties with slow connections unavoidably cannot give input. A natural question is whether there exists a protocol for MPC that can tolerate up to $t_s < n/2$ corruptions under a synchronous network and $t_a < n/3$ corruptions even when the network is asynchronous. We answer this question by showing tight feasibility and impossibility results. More specifically, we show that such a protocol exists if and only if $t_a + 2t_s < n$ and the number of inputs taken into account under an asynchronous network is at most $n-t_s$.
2020
TCC
This paper proposes a simple synchronous composable security framework as an instantiation of the Constructive Cryptography framework, aiming to capture minimally, without unnecessary artefacts, exactly what is needed to state synchronous security guarantees. The objects of study are specifications (i.e., sets) of systems, and traditional security properties like consistency and validity can naturally be understood as specifications, thus unifying composable and property-based definitions. The framework's simplicity is in contrast to current composable frameworks for synchronous computation which are built on top of an asynchronous framework (e.g. the UC framework), thus not only inheriting artefacts and complex features used to handle asynchronous communication, but adding additional overhead to capture synchronous communication. As a second, independent contribution we demonstrate how secure (synchronous) multi-party computation protocols can be understood as constructing a computer that allows a set of parties to perform an arbitrary, on-going computation. An interesting aspect is that the instructions of the computation need not be fixed before the protocol starts but can also be determined during an on-going computation, possibly depending on previous outputs.
2020
TCC
Understanding the communication complexity of Byzantine agreement (BA) is a fundamental problem in distributed computing. In particular, as protocols are run with a large number of parties (as, e.g., in the context of blockchain protocols), it is important to understand the dependence of the communication on the number of parties~$n$. Although adaptively secure BA protocols with $o(n^2)$ communication are known in the synchronous and partially synchronous settings, no such protocols are known in the fully asynchronous case. We show here an asynchronous BA protocol with subquadratic communication tolerating an adaptive adversary who can corrupt $f<(1-\epsilon)n/3$ of the parties (for any $\epsilon>0$). One variant of our protocol assumes initial setup done by a trusted dealer, after which an unbounded number of BA executions can be run; alternately, we can achieve subquadratic \emph{amortized} communication with no prior setup. We also show that some form of setup is needed for (non-amortized) subquadratic BA tolerating $\Theta(n)$ corrupted parties. As a contribution of independent interest, we show a secure-computation protocol in the same threat model that has $o(n^2)$ communication when computing no-input functionalities with short output (e.g., coin tossing).
2020
ASIACRYPT
Two paradigms for secure MPC are synchronous and asynchronous protocols. While synchronous protocols tolerate more corruptions and allow every party to give its input, they are very slow because the speed depends on the conservatively assumed worst-case delay $\Delta$ of the network. In contrast, asynchronous protocols allow parties to obtain output as fast as the actual network allows, a property called \emph{responsiveness}, but unavoidably have lower resilience and parties with slow network connections cannot give input. It is natural to wonder whether it is possible to leverage synchronous MPC protocols to achieve responsiveness, hence obtaining the advantages of both paradigms: full security with responsiveness up to t corruptions, and 'extended' security (full security or security with unanimous abort) with no responsiveness up to a larger threshold T of corruptions. We settle the question by providing matching feasibility and impossibility results: -For the case of unanimous abort as extended security, there is an MPC protocol if and only if T + 2t < n. -For the case of full security as extended security, there is an MPC protocol if and only if T < n/2 and T + 2t < n. In particular, setting t = n/4 allows to achieve a fully secure MPC for honest majority, which in addition benefits from having substantial responsiveness.
2018
TCC
Topology-hiding communication protocols allow a set of parties, connected by an incomplete network with unknown communication graph, where each party only knows its neighbors, to construct a complete communication network such that the network topology remains hidden even from a powerful adversary who can corrupt parties. This communication network can then be used to perform arbitrary tasks, for example secure multi-party computation, in a topology-hiding manner. Previously proposed protocols could only tolerate passive corruption. This paper proposes protocols that can also tolerate fail-corruption (i.e., the adversary can crash any party at any point in time) and so-called semi-malicious corruption (i.e., the adversary can control a corrupted party’s randomness), without leaking more than an arbitrarily small fraction of a bit of information about the topology. A small-leakage protocol was recently proposed by Ball et al. [Eurocrypt’18], but only under the unrealistic set-up assumption that each party has a trusted hardware module containing secret correlated pre-set keys, and with the further two restrictions that only passively corrupted parties can be crashed by the adversary, and semi-malicious corruption is not tolerated. Since leaking a small amount of information is unavoidable, as is the need to abort the protocol in case of failures, our protocols seem to achieve the best possible goal in a model with fail-corruption.Further contributions of the paper are applications of the protocol to obtain secure MPC protocols, which requires a way to bound the aggregated leakage when multiple small-leakage protocols are executed in parallel or sequentially. Moreover, while previous protocols are based on the DDH assumption, a new so-called PKCR public-key encryption scheme based on the LWE assumption is proposed, allowing to base topology-hiding computation on LWE. Furthermore, a protocol using fully-homomorphic encryption achieving very low round complexity is proposed.

#### Coauthors

Erica Blum (2)
Jonathan Katz (1)
Rio LaVigne (2)
Julian Loss (3)
Ueli Maurer (4)
Tal Moran (3)
Marta Mularczyk (2)
Daniel Tschudi (3)


text:
# How do you compute d/dx 3sinh(3/x)?

$\frac{d}{\mathrm{dx}} 3 \sinh \left(\frac{3}{x}\right) = 3 \cosh \left(\frac{3}{x}\right) \cdot \left(- \frac{3}{x} ^ 2\right) = - \frac{9}{x} ^ 2 \cosh \left(\frac{3}{x}\right)$.


text:
Multi-cubic functional equations in Lipschitz spaces

Document Type : Research Paper

Author

Urmia University

10.22075/ijnaa.2021.24215.2692

Abstract

In this paper, we prove stability of multi-cubic functional equations in Lipschitz spaces by property multi-symmetric left invariant mean. Indeed, we prove under certain Lipschitz condition a family of Lipschitz mappings can be approximated by multi-cubic mappings.

Keywords


text:
Trends in Metropolitan Network Circuity (working paper)

David J. Giacomin, Luke S. James, and David M. Levinson (2012) Trends in Metropolitan Network Circuity. (Working Paper)

Because people seek to minimize their time and travel distance (or cost) when commuting, the circuity–the ratio of network distance traveled to the Euclidean distance between two points–plays an intricate role in the metropolitan economy. This paper seeks to measure the circuity of the United States’ 51 most populated Metropolitan Statistical Areas and identify trends in those circuities over the time period from 1990- 2010. With many factors playing a role such as suburban development and varying economic trends in metropolitan areas over this timeframe, much is to consider when calculating results. In general, circuity is increasing over time.


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## Friday, August 20, 2004

### NYTimes.com Article: The Probability That a Real-Estate Agent Is Cheating You (and Other Riddles of Modern Life)

NYTimes.com Article: The Probability That a Real-Estate Agent Is Cheating You (and Other Riddles of Modern Life): "NYTimes.com Article: The Probability That a Real-Estate Agent Is Cheating You (and Other Riddles of Modern Life)"

Read this NYT article. It's just wonderful.


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# TeXLive 2019 (MacTeX): Lualatex can't find any image with \includegraphics

Running MacTex 2019, all packages updated, and suddenly every \includegraphics instance returns the following error:

Package luatex.def Error: File 'path/to/file' not found: using draft setting.

I haven't any clue about what's going on except this other post about a similar issue with MiKTeX: MiKTeX graphics version 1.3b bug on Windows 10 (\set@curr@file undefined)

Every file I tried was compiling fine a month ago or so.

• Welcome to TeX.SX! Please help us help you and add a minimal working example (MWE) that illustrates your problem. Reproducing the problem and finding out what the issue is will be much easier when we see compilable code, starting with \documentclass{...} and ending with \end{document}. – DG' Nov 4 '19 at 16:57
• Is this the exact error message? In this case, you may have forgotten to replace the string path/to/file with the actual path to your file. – Jonas Schwarz Nov 4 '19 at 16:58

Sorry, path/to/file was just a placeholder.

But I found the exact problem and possible workarounds while it is fixed.

My exact problem is described here: grffile & lualatex does not work together anymore (Oct 2019)

And here is the discussion on Github: https://github.com/ho-tex/oberdiek/issues/73

A temporary workaround is explained here: https://github.com/ho-tex/oberdiek/issues/73#issuecomment-543101052

• note that no one other than you could have answered your question as you hadn't even mention grffile in the question. – David Carlisle Nov 4 '19 at 18:17
• I am very sorry about that. But, by the time I wrote my first message I didn't have any clue about what was going on. Further research pointed me to that direction, the interaction between grffile and lualatex. I mark the post as answered in case it is useful to anyone. My apologies. – fdiog Nov 6 '19 at 17:51
• sure: no harm done really but just to explain why questions should almost always include a complete small document that shows the error. Then you don't need to guess what the issue is, people can see it and debug. – David Carlisle Nov 6 '19 at 18:27


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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['figure.figsize'] = (10, 5)

## Cointegration Models

• What is Cointegration?
• Two series, $P_t$ and $Q_t$ can be random walks
• But the linear combination $P_t - c Q_t$ may not be a random walk!
• If that's true
• $P_t - c Q_t$ is forecastable
• $P_t$ and $Q_t$ are said to be cointegrated
• Analogy: Dog on a Leash
• $P_t =$ Owner
• $Q_t =$ Dog
• Both series look like a random walk
• Difference of distance between them, looks mean reverting
• If dog falls too far behind, it gets pulled forward
• If dog gets too far ahead, it gets pulled back
• What types of series are cointegrated?
• Economic substitutes
• Heating Oil and Natural Gas
• Corn and Wheat
• Corn and Sugar
• $\dots$
• Bitcoin and Ethereum
• Two steps to test for Cointegration
• Regress $P_t$ on$Q_t$ and get slope $c$
• Run Augmented Dickey-Fuller test on $P_t - c Q_t$ to test for random walk
• Alternatively, can use coint function in statsmodels that combines both steps

### A Dog on a Leash? (Part 1)

The Heating Oil and Natural Gas prices are pre-loaded in DataFrames HO and NG. First, plot both price series, which look like random walks. Then plot the difference between the two series, which should look more like a mean reverting series (to put the two series in the same units, we multiply the heating oil prices, in $/gallon, by 7.25, which converts it to$/millionBTU, which is the same units as Natural Gas).

The data for continuous futures (each contract has to be spliced together in a continuous series as contracts expire) was obtained from Quandl.

• Preprocess
HO = pd.read_csv('./dataset/CME_HO1.csv', index_col=0)

HO.index = pd.to_datetime(HO.index, format='%m/%d/%Y')
NG.index = pd.to_datetime(NG.index, format='%m/%d/%Y')

HO = HO.sort_index()
NG = NG.sort_index()

HO.head()

Close
Date
1991-01-02 0.7330
1991-01-03 0.7024
1991-01-04 0.6830
1991-01-07 0.7617
1991-01-08 0.7430
NG.head()

Close
Date
1991-01-02 1.832
1991-01-03 1.782
1991-01-04 1.743
1991-01-07 1.785
1991-01-08 1.786
plt.subplot(2, 1, 1)
plt.plot(7.25 * HO, label='Heating Oil');
plt.plot(NG, label='Natural Gas');
plt.legend(loc='best', fontsize='small');

plt.subplot(2, 1, 2)
plt.plot(7.25 * HO - NG, label='Spread');
plt.legend(loc='best', fontsize='small');
plt.axhline(y=0, linestyle='--', color='k');

### A Dog on a Leash? (Part 2)

To verify that Heating Oil and Natural Gas prices are cointegrated, First apply the Dickey-Fuller test separately to show they are random walks. Then apply the test to the difference, which should strongly reject the random walk hypothesis.

from statsmodels.tsa.stattools import adfuller

# Compute the ADF for HO and NG
print("The p-value for the ADF test on HO is ", result_HO[1])
print("The p-value for the ADF test on NG is ", result_NG[1])

The p-value for the ADF test on HO is  0.956710878501786
The p-value for the ADF test on NG is  0.9008747444676729

### Are Bitcoin and Ethereum Cointegrated?

Cointegration involves two steps: regressing one time series on the other to get the cointegration vector, and then perform an ADF test on the residuals of the regression. In the last example, there was no need to perform the first step since we implicitly assumed the cointegration vector was (1,−1). In other words, we took the difference between the two series (after doing a units conversion). Here, you will do both steps.

You will regress the value of one cryptocurrency, bitcoin (BTC), on another cryptocurrency, ethereum (ETH). If we call the regression coefficient b, then the cointegration vector is simply (1,−b). Then perform the ADF test on BTC −b ETH.

• Preprocess
BTC = pd.read_csv('./dataset/BTC.csv', index_col=0)

BTC.index = pd.to_datetime(BTC.index, format='%Y-%m-%d')
ETH.index = pd.to_datetime(ETH.index, format='%Y-%m-%d')

import statsmodels.api as sm

# Regress BTC on ETH
result = sm.OLS(BTC, ETH).fit()

b = result.params[1]

The p-value for the ADF test is  0.02336900232347285

## Case Study: Climate Change

### Is Temperature a Random Walk (with Drift)?

An ARMA model is a simplistic approach to forecasting climate changes, but it illustrates many of the topics covered in this class.

The DataFrame temp_NY contains the average annual temperature in Central Park, NY from 1870-2016 (the data was downloaded from the NOAA here). Plot the data and test whether it follows a random walk (with drift).

• Preprocess
temp_NY = pd.read_csv('./dataset/NOAA_TAVG.csv', index_col=0)
temp_NY.index = pd.to_datetime(temp_NY.index, format='%Y')

TAVG
DATE
1870-01-01 53.8
1871-01-01 51.3
1872-01-01 51.3
1873-01-01 50.9
1874-01-01 51.3
temp_NY.plot();

# Compute and print ADF p-value
print("The p-value for the ADF test is ", result[1])

The p-value for the ADF test is  0.583293898787112

### Getting "Warmed" Up: Look at Autocorrelations

Since the temperature series, temp_NY, is a random walk with drift, take first differences to make it stationary. Then compute the sample ACF and PACF. This will provide some guidance on the order of the model.

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Take first difference of the temperature Series
chg_temp = temp_NY.diff()
chg_temp = chg_temp.dropna()

# Plot the ACF and PACF on the same page
fig, axes = plt.subplots(2, 1)

# Plot the ACF
plot_acf(chg_temp, lags=20, ax=axes[0]);

# Plot the PACF
plot_pacf(chg_temp, lags=20, ax=axes[1]);

plt.tight_layout()

### Which ARMA Model is Best?

Recall from Chapter 3 that the Akaike Information Criterion (AIC) can be used to compare models with different numbers of parameters. It measures goodness-of-fit, but places a penalty on models with more parameters to discourage overfitting. Lower AIC scores are better.

Fit the temperature data to an AR(1), AR(2), and ARMA(1,1) and see which model is the best fit, using the AIC criterion. The AR(2) and ARMA(1,1) models have one more parameter than the AR(1) has.

from statsmodels.tsa.arima_model import ARMA

# Fit the data to an AR(1) model and print AIC:
mod_ar1 = ARMA(chg_temp, order=(1, 0))
res_ar1 = mod_ar1.fit()
print("The AIC for an AR(1) is: ", res_ar1.aic)

# Fit the data to an AR(2) model and print AIC
mod_ar2 = ARMA(chg_temp, order=(2, 0))
res_ar2 = mod_ar2.fit()
print("The AIC for an AR(2) is: ", res_ar2.aic)

# fit the data to an ARMA(1, 1) model and print AIC
mod_arma11 = ARMA(chg_temp, order=(1, 1))
res_arma11 = mod_arma11.fit()
print("The AIC for an ARMA(1,1) is: ", res_arma11.aic)

/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.
% freq, ValueWarning)
/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.
% freq, ValueWarning)
/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.
% freq, ValueWarning)

The AIC for an AR(1) is:  510.5346898313909
The AIC for an AR(2) is:  501.9274123160227
The AIC for an ARMA(1,1) is:  469.0729133043228

### Don't Throw Out That Winter Coat Yet

Finally, you will forecast the temperature over the next 30 years using an ARMA(1,1) model, including confidence bands around that estimate. Keep in mind that the estimate of the drift will have a much bigger impact on long range forecasts than the ARMA parameters.

Earlier, you determined that the temperature data follows a random walk and you looked at first differencing the data. In this exercise, you will use the ARIMA module on the temperature data (before differencing), which is identical to using the ARMA module on changes in temperature, followed by taking cumulative sums of these changes to get the temperature forecast.

from statsmodels.tsa.arima_model import ARIMA

# Forecast temperatures using an ARIMA(1,1,1) model
mod = ARIMA(temp_NY, order=(1,1,1))
res = mod.fit()

# Plot the original series and the forecasted series
res.plot_predict(start='1872-01-01', end='2046-01-01');
plt.savefig('../images/tavg_predict.png')

/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.
% freq, ValueWarning)
/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.
% freq, ValueWarning)


text:
# Is ergodic hypothesis in contradiction with the notion of equilibrium?

From wikipedia:

In physics and thermodynamics, the ergodic hypothesis1 says that, over long periods of time, the time spent by a system in some region of the phase space of microstates with the same energy is proportional to the volume of this region, i.e., that all accessible microstates are equiprobable over a long period of time.

So if I understood it right, given enough time the system will move through all possible states. However, from thermodynamics we know that state of equilibrium is in a sense the "final state" in which system will get once and won't move to other states after that.

Aren't these two things in contradiction? If ergodic hypothesis is true then wouldn't that mean that system which is already in state of equilibrium will spontaneously move out of equilibrium into some other state (after enough time has passed)?

• The statistical fluctuation of systems at equilibrium to slightly out of equilibrium states and back again is well understood and well measured. Has been for decades, though it is a difficult experiment to do on even slightly macroscopic samples. You should think of this as refining what is meant by equilibrium. – dmckee Jul 26 '16 at 0:04
• I’ve seen Leonard Suskind go over that on a number of occasions. Many of his talks and classes are on youtube. – JDługosz Jul 26 '16 at 5:30

You have to be careful to distinguish between microstates and macrostates. Thermodynamic equilibrium is a macrostate which consists of a mixture of all possible microstates of energy $E$ weighted by a Boltzmann weight $e^{- \beta E} / Z$. A state in macroscopic thermal equilibrium can be thought of as "moving through phase space" ergodically (i.e. the microstate is constantly changing, but the fraction of time spent in each microstate is fixed to the Boltzmann weight).

• Are you saying that ergodic hypothesis applies only to the microstates that correspond to the one macrostate of equilibrium? I mean, this part of the quote from the first post "all accessible microstates are equiprobable over a long period of time." applies only for microstates that give just that one macrostate? In other words, once system is in equilibrium it will move through all microstates that belong only to that macrostate but it won't move to some other microstate that would correspond to other non-equilibrium macrostate? – matori82 Jul 26 '16 at 0:16
• @matori82 No, all the system microstates contribute to the thermal equilibrium macrostate, it's just that the high-energy ones don't contribute very much at low temperature. The statement "all accessible microstates are equiprobable over a long period of time" is confusing and misleading, because it seems to contradict the Boltzmann weighting. What they mean is that all accessible microstates of the system and the thermal bath together are equiprobable. When you restrict your attention to only considering the system microstates, this reduces to the Boltzmann weighting. – tparker Jul 26 '16 at 0:21
• Sorry, still trying to wrap my head around these concepts. Can you explain this in more simple words? Eg. without Boltzmann weighting as I am still not very familiar with it. I want to understand what is the basic idea behind introducing this hypothesis if it can be explained in simple words without equations. Maybe some simple example would help me get the intuition for the concept. – matori82 Jul 26 '16 at 2:23
• @matori82 Thermodynamic equilibrium does not mean that the system is in a particular microstate - it refers to a particular probability distribution over microstates: the simplest possible one, in which every microstate with the same energy is assigned the same probability. This probability distribution does not change over time (hence the "equilibrium"), even though the specific system microstate is constantly changing. – tparker Jul 26 '16 at 4:12

As far as I understand, the answer is: not only ergodicity but Poincare reccurence theorem "contradicts a bit" with the second law of thermodynamics.

The point is that actually time that every ergodic system (for instance, Boltzmann billiard, as Sinai proved) is in some measurable part of full phase space of system is proportional to the phase volume of this part. But due to some kind of large numbers law almost all of the phase space belongs to the parameters of the maximum entropy state.

So if you remove the boundary between two parts of the half-empty volume, system will periodically return in half-empty state (without boundary now), but the phase volume of this family of states is quite small (in fact, devastatingly tiny), so the proportion of time system belongs to this volume is devastatingly tiny too.

If you want, you can set up computer experiment with 1,2,3,...,10 molecules to see the character of rapid decreasing of the phase volume of small entropy (half-empty rectangle, for instance) while the number of molecules increases.

So in fact entropy is not increasing, it achieves its maximum possible value just when you "open the door" for it, and it persists for very long time interval (transcendentally huge, yes).

Sorry for terrible English.

To be concrete lets picture a box with an ideal mono-atomic gas in equilibrium and containing a constant energy. Let's not consider the deviations from the mean values of the atom momenta, so all the atoms have the same mean value of the momentum (I know this isn't realistic, but it is a good approximation of the situation). There are a lot of distributions of the momenta and positions of the atoms in phase space corresponding to the same, macroscopic equilibrium state of the gas. And the bigger the volume, the more of these distributions are possible (the distributions for which the positions of the atoms are, say, all concentrated in one corner of the box, or the momenta are separated into one high momentum part and a low momentum part are not considered, and I already stated that the momenta of the atoms are to be considered equal).

So you don't have to worry that the atoms in the box suddenly show a non-equilibrium sign (like all the atoms residing in one corner: for this to take place you have to wait for a much very much longer time than the time mentioned in the quote).

There is a nice metaphor for ergodicity: Imagine a lonely man taking a random walk, from the entrance to the exit, in the park each day. You can draw his path for many days after each other. This will give (more or less) the same result if you draw the random paths of a lot of people taking a random walk in the park one one single day.

• +1 for the analogy. – onurcanbektas Feb 3 at 8:53

Ergodic hypothesis is not contradiction to notion of equilibrium. In fact, it is the pillar of equilibrium statistical physics. The measurable quantities(like pressure, temperature,..) which are evaluated at the equilibrium assume that ergodic hypothesis is valid at equilibrium. One starts with finding the partition function(Z) $$Z = \sum_{\epsilon} e^{-\beta \epsilon}$$ and later on one finds the observable quantities (here we assumed discrete energy levels and we have taken Canonical Ensemble). Here we have summed over all the possible values of energy. In other words, ergodic hypothesis can also be stated as "at equilibrium, time average is equivalent to ensemble average" which we have used while finding the canonical ensemble partition function. We have summed over all the values of microstates as ergodic hypothesis states that " all accessible microstates are equiprobable at equilibrium ".

• you haven't showed the part "equiprobable"; just stated. – onurcanbektas Feb 3 at 8:40


text:
### Golden Thoughts

Rectangle PQRS has X and Y on the edges. Triangles PQY, YRX and XSP have equal areas. Prove X and Y divide the sides of PQRS in the golden ratio.

### Two Cubes

Two cubes, each with integral side lengths, have a combined volume equal to the total of the lengths of their edges. How big are the cubes? [If you find a result by 'trial and error' you'll need to prove you have found all possible solutions.]

### Square Mean

Is the mean of the squares of two numbers greater than, or less than, the square of their means?

# How Old Am I?

##### Stage: 4 Challenge Level:
If I am $x$ years old now how old was I 15 years ago?
If I am $x$ years old now how old will I be in 15 years' time?
Can you use these expressions to form an equation to solve?


text:
# Possible to set all App pools/Websites on an IIS 6 and/or IIS 7 server to 4.0 instead of 3.5 at one time?

Is it possible to set all App pools/Websites on an IIS 6 and/or IIS 7 server to 4.0 instead of 3.5 in one fell swoop?

Basically we have many servers, and will be upgrading all them 4.0 from 3.5. Right clicking and changing each site would be rather cumbersome. The servers are a mix of IIS 6 and IIS 7

-

You could use appcmd.exe coupled with PSEXEC.exe.
psexec.exe @file="c:\path\to\file\with\allserverx.txt" %systemroot%\system32\inetsrv\APPCMD.exe set apppool /apppool.name:www.mysite.com /managedRuntimeVersion:4.0


text:
Deals Of The Week - hours only!Up to 80% off on all courses and bundles.-Close
Know your data
Visualize your data – categorical variables
Work with your chart 2
Check yourself 2

## Instruction

Ok. We now know what data we need and what we should achieve after the first step of our mosaic plot construction project. In reality, when you use ggplot2 you don't have to prepare data for a mosaic plot – a new geometry called geom_mosaic will take care of it. Nevertheless, knowing what values are encoded in the mosaic plot is crucial if you want to understand this complicated chart.

To create this first stage of the mosaic plot using R, you have to add geom_mosaic to the main ggplot function:

ggplot(data = dataset) + geom_mosaic(aes(x = product(variable_x)))

Note that you specify which variable will be on the horizontal axis by the argument product(variable_x). Obviously, the variable name will replace the variable_x placeholder text.

## Exercise

Start your mosaic plot using the commands discussed above. Use alcohol_wealth2 for the dataset and wealth_index_cat as variable_x.

When you're done, press the button.

### Stuck? Here's a hint!

You should write:

ggplot(data = alcohol_wealth2) + geom_mosaic(aes(x = product(wealth_index_cat)))


text:
SoK: Verifiability Notions for E-Voting Protocols

SoK: Verifiability Notions for E-Voting Protocols. Véronique Cortier, David Galindo, Ralf Küsters, Johannes Müller, and Tomasz Truderung. In Proceedings of the 36th IEEE Symposium on Security and Privacy (S&P'16), IEEE Computer Society Press, San Jose, CA, USA, May 2016.

Abstract

There have been intensive research efforts in the last two decades or so to design and deploy electronic voting (e-voting) protocols and systems which allow voters and/or external auditors to check that the votes were counted correctly. This security property, which not least was motivated by numerous problems in even national elections, is called verifiability. It is meant to defend against voting devices and servers that have programming errors or are outright malicious. In order to properly evaluate and analyze e-voting protocols and systems w.r.t. verifiability, one fundamental challenge has been to formally capture the meaning of this security property. While the first formal definitions of verifiability were devised in the late 1980s already, new verifiability definitions are still being proposed. The definitions differ in various aspects, including the classes of protocols they capture and even their formulations of the very core of the meaning of verifiability. This is an unsatisfying state of affairs, leaving the research on the verifiability of e-voting protocols and systems in a fuzzy state.
In this paper, we review all formal definitions of verifiability proposed in the literature and cast them in a framework proposed by Küsters, Truderung, and Vogt (the KTV framework), yielding a uniform treatment of verifiability. This enables us to provide a detailed comparison of the various definitions of verifiability from the literature. We thoroughly discuss advantages and disadvantages, and point to limitations and problems. Finally, from these discussions and based on the KTV framework, we distill a general definition of verifiability, which can be instantiated in various ways, and provide precise guidelines for its instantiation. The concepts for verifiability we develop should be widely applicable also beyond the framework used here. Altogether, our work offers a well-founded reference point for future research on the verifiability of e-voting systems.

BibTeX

@inproceedings{SP16-verifiability,
author = {V\'eronique Cortier and David Galindo and Ralf K\"usters and Johannes M\"uller and Tomasz Truderung},
title = {SoK: Verifiability Notions for E-Voting Protocols},
abstract = {There have been intensive research efforts in the last two decades
or so to design and deploy electronic voting (e-voting)
protocols and systems which allow voters and/or
external auditors to check that the votes were counted
correctly. This security property, which not least was
motivated by numerous problems in even national elections,
is called \emph{verifiability}. It is meant to defend
against voting devices and servers that have programming errors or
are outright malicious. In order to properly evaluate and analyze
e-voting protocols and systems
w.r.t.~verifiability, one fundamental challenge has been to formally
capture the meaning of this security property. While the first
formal definitions of verifiability were devised in the late 1980s
already, new verifiability definitions are still being proposed. The
definitions differ in various aspects, including the classes of
protocols they capture and even their formulations of the very core
of the meaning of verifiability. This is an unsatisfying state of
affairs, leaving the research on the verifiability of e-voting protocols and systems
in a fuzzy state.
\par
In this paper, we review all formal definitions of verifiability
proposed in the literature and cast them in a framework proposed by K{\"u}sters,
Truderung, and Vogt (the KTV framework), yielding a uniform
treatment of verifiability. This enables us to provide a detailed
comparison of the various definitions of verifiability from the
point to limitations and problems.  Finally, from these discussions
and based on the KTV framework, we
distill a general definition of verifiability, which can be
instantiated in various ways, and provide precise guidelines for its
instantiation. The concepts for verifiability we develop should be
widely applicable also beyond the framework used here. Altogether,
our work offers a well-founded reference point for future research
on the verifiability of e-voting systems.},
year = {2016},
address =       {San Jose, CA, USA},
booktitle =     {{P}roceedings of the 36th IEEE Symposium on Security and Privacy (S\&P'16)},
month =         may,
OPTpages     =     {},
OPTnote = {},
publisher =     {{IEEE} Computer Society Press},
doi = {10.1109/SP.2016.52},
}


text:
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# Chicago vs Wharton

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08 Mar 2008, 22:14
nick_sun wrote:
Wharton is a GND program. The current student has just clarified So there is no difference in academic rigor of Chicago or Wharton (amended my previous post).

i am not sure that GND is the yardstick for academic rigor. i agree that both of the schools are probably similar in terms of rigor (how do you measure this anyway?) since they are both top schools with quant slants. i would argue though that the rigor has more to do with curriculum structure, individual course selection, and, to a lesser extent, peer competition. at the end of the day, however, i think the rigor is basically what you make of it -- if you get into either school, you can handle the rigor and won't fail out.

my wharton interviewer straight up told me not to work hard if i get in. i said "oh no, don't say that," to which he laughed, told me to get drunk a lot and meet as many people as possible. he told me the work load is not that bad. i guess it's all relative though -- if you're a banker, nothing is going to be as bad as 100 hour weeks. likewise (to a slightly lesser extent) chugging 60-70 hours a week at work and trying to pound through the CFA is probably a lot harder than b-school. which reminds me, back to level 3 (fun times on a saturday night!!!).
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08 Mar 2008, 22:23
Probably, you are right. It may be "rigor" rather than "academic rigor". But it is all connected with the grades, so it can define how hard you will work at school.
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09 Mar 2008, 06:39
Students at every school I have talked to said grades are far different than high school and undergrad. The vast majority of students get B's, then a small amount at the top get A's, and people at the bottom get C's. One professor I talked to at a school with grade disclosure said they had a meeting a few years ago where the administration said that they wanted some professors to actually give out grades below Bs. You do the work and put the effort in, participate in class, and study for an exam to at least do fairly well...and you will get at least a B. One student noted that sponsored students and those returning to their old employer typically do the worst since they dont really care as much, and some students put way more into recruiting and networking than classes. Obviously some sponsored students will work very hard and some non-sponsored will do nothing.
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09 Mar 2008, 10:56
Wharton does have a GND, the administration wants disclosure but leaves the issue up to the student government which opts for a GND. I think both schools would be great but here's what I came away with after visiting Wharton. City, yea Philly isn't that great but center city (where you will live) + university city are very nice. Honestly the areas I saw were great and you don't have to venture outside of that. Location is better than Chicago, close to NYC, Philly which helps if you want to do IB/PE etc...since you can make more trips to the companies offices (this is what I was told, don't really know how it helps except to spread things out versus the nyc interview week offered by a lot of other schools). Wharton has a lot of programmes in int'l business that cross over to research centers on campus, which for me is a big plus. Both schools will give you a great platform, everyone I met at Wharton loved it and I'm sure everyone at GSB will say the same...go with location if you have to choose.
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09 Mar 2008, 15:48
Just to add this if I may. This is one amazingly awesome decision to have to make. Both schools have some strengths and weaknesses, but both are also probably the best two finance schools in the world. Even outside of finance, each school holds its own in most other fields. Not a bad decision to have to make.
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09 Mar 2008, 22:45
nick_sun wrote:
Wharton is a GND program. The current student has just clarified So there is no difference in academic rigor of Chicago or Wharton (amended my previous post).

I am not clear what this means? So both Chicago and Wharton is GND program?

And when people reply by saying which city you like better, wouldn't you be in that city for approximately 2 years, unless you choose a job in that city. Wouldn't both schools be able to place you anywhere in the nation?

I would visit both schools if possible, and see which has a better click with your personal lifestyle.
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16 Mar 2008, 09:42
rhyme wrote:
I have some good comments on this stuff, but im very hungover at the moment, so I'll try to come back later when my eyes dont feel like they are being squished.

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16 Mar 2008, 11:25
2
KUDOS
Some more thoughts:

Wharton is probably the most recognized B-school on earth (perhaps even more than HS), I don't know if you guys remember when I asked what is the importance of the main institution at the B-school? Well, when I was thinking whether or not I should apply to Wharton (after being admitted to GSB) I made some research of the B-schools academics and prestigiousness (is it a word? ):

Claimed Nobel Laureates:
University of Chicago: 81
University of Pennsylvania: 19
http://en.wikipedia.org/wiki/Nobel_Priz ... ffiliation

Economics Nobel Laureates:
UofC: 10
UPenn: 1
For the B-schools it would be (6 for 1)
http://nobelprize.org/nobel_prizes/economics/laureates/

Total University Endowment:
UofC:U$6.2Bi (total undergrad and grad students: 14,700) UPenn: U$6.78Bi (total undergrad and grad students: 19,800)

B-School Faculty Number:
GSB: 175
Wharton: 304

In terms of ranking we all know that Wharton has always been among the TOP3 schools, while GSB among the TOP5, I'd say that rank wise Wharton is the clear winner here.

I met a bunch of people during GSB admit weekend who were deciding between these 2 schools, and from what they told me most of them were leaning towards GSB (not sure though if they told this because they were in Chicago at that time).

Once again: You can't go wrong with neither of them, so if you have to choose, good for you.
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16 Mar 2008, 12:06
How do these 2 schools compare to each other in terms of workload? In general, how many hours per week does one dedicate to the school, to work, to recruiting events, etc? Are we talking 60 - 70 hours per week, more, less? How do the other schools fare on that point of view btw? I know it depends on the courses you choose, etc, but I would like to have a broad idea.
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16 Mar 2008, 12:07
kwam wrote:
Some more thoughts:

Wharton is probably the most recognized B-school on earth (perhaps even more than HS), I don't know if you guys remember when I asked what is the importance of the main institution at the B-school? Well, when I was thinking whether or not I should apply to Wharton (after being admitted to GSB) I made some research of the B-schools academics and prestigiousness (is it a word? ):

Claimed Nobel Laureates:
University of Chicago: 81
University of Pennsylvania: 19
http://en.wikipedia.org/wiki/Nobel_Priz ... ffiliation

Economics Nobel Laureates:
UofC: 10
UPenn: 1
For the B-schools it would be (6 for 1)
http://nobelprize.org/nobel_prizes/economics/laureates/

Total University Endowment:
UofC:U$6.2Bi (total undergrad and grad students: 14,700) UPenn: U$6.78Bi (total undergrad and grad students: 19,800)

B-School Faculty Number:
GSB: 175
Wharton: 304

In terms of ranking we all know that Wharton has always been among the TOP3 schools, while GSB among the TOP5, I'd say that rank wise Wharton is the clear winner here.

I met a bunch of people during GSB admit weekend who were deciding between these 2 schools, and from what they told me most of them were leaning towards GSB (not sure though if they told this because they were in Chicago at that time).

Once again: You can't go wrong with neither of them, so if you have to choose, good for you.

Good stuff Kwam!
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16 Mar 2008, 18:31
2
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Audio wrote:
How do these 2 schools compare to each other in terms of workload? In general, how many hours per week does one dedicate to the school, to work, to recruiting events, etc? Are we talking 60 - 70 hours per week, more, less? How do the other schools fare on that point of view btw? I know it depends on the courses you choose, etc, but I would like to have a broad idea.

Its going to vary a LOT. If you take Kevin Murphy's Turbo Microeconomics course, its not uncommon for people to quote figures like 20 hours a week -- just for his class. Other super popular courses are also heavy workloads -- the new venture challenge for instance, I think averages 11 hours.

Most courses seem to require approx 3 to 4 hours outside of class. With four courses per week, thats 12 hours in class, with approx 16 outside -- lets say 30 total for attending class and doing the homework. If you push yourself a bit, expect a bit more, maybe 35. Throw in a couple group meetings here and there that tend to take longer than you always think they will, and 40 seems like a reasonable figure. Now, layer in some extracurricular activities, volunteering, etc, and 50 hours isn't hard to get to. Layer in recruiting dinners, social cocktail hours, meetings with career services, mock interviews, actual interviews, resume editing, company research, cover letter editing, day-at-company events, coffee chats, conversations with alumni, meetings with current students, flying out for 2nd rounds, going to 3-hour long back to back consulting interviews, commuting.... and for a few weeks there in the winter, yea, you'll cry uncle.....

Let me be clear: If you think graduate school is going to be a time to 'work on your tan' or 'improve your golf game', if you plan on going to a top 10 - or even a top 15 probably - I'd say you had best adjust your expectations. Wharton, Chicago, or other.... expect to earn your MBA.
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16 Mar 2008, 18:46
Very aptly put rhyme, kudos
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16 Mar 2008, 19:09
EUR/USD 1,5829

Nikkei down 514 points...

It's going to be a mad Monday...
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16 Mar 2008, 23:51
kwam wrote:
EUR/USD 1,5829

Nikkei down 514 points...

It's going to be a mad Monday...

It is going to be a mad year (or two).
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17 Mar 2008, 06:04
xerox wrote:
kwam wrote:
EUR/USD 1,5829

Nikkei down 514 points...

It's going to be a mad Monday...

It is going to be a mad year (or two).

Ops sorry, it was in the wrong thread...
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27 Mar 2008, 13:21
Audio wrote:
rhyme wrote:
I have some good comments on this stuff, but im very hungover at the moment, so I'll try to come back later when my eyes dont feel like they are being squished.

The choice is, I have to admit, excruciating, and although I know that professionally I can't go wrong, the choice on a personal point of view is so difficult...

PS: how do the partying and the sports compare? Those are the things I love the most in life
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27 Mar 2008, 13:38
Audio wrote:
Audio wrote:
rhyme wrote:
I have some good comments on this stuff, but im very hungover at the moment, so I'll try to come back later when my eyes dont feel like they are being squished.

The choice is, I have to admit, excruciating, and although I know that professionally I can't go wrong, the choice on a personal point of view is so difficult...

PS: how do the partying and the sports compare? Those are the things I love the most in life

Come on, are you really asking us that, are you comparing Philly to Chi-town, regarding partying and sports, or are you comparing UofC and UPenn?
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27 Mar 2008, 15:14
rhyme wrote:
Let me be clear: If you think graduate school is going to be a time to 'work on your tan' or 'improve your golf game', if you plan on going to a top 10 - or even a top 15 probably - I'd say you had best adjust your expectations. Wharton, Chicago, or other.... expect to earn your MBA.

Well-said, rhyme. Looks like my golf game will have to wait until summer 2011...
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27 Mar 2008, 18:02
Hehe well let's compare sports...

Sixers > Bulls - This year, especially after Feb.
Flyers > Blackhawks
Eagles = Bears - Rex, nuff said
Phillies = Cubs - Both are going to be great...but Chicago wins cause of the White Sox 2 to 1

I don't know anything about UChicago or UPenn college sports.
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27 Mar 2008, 18:15
shmegs wrote:
Hehe well let's compare sports...

Sixers > Bulls - This year, especially after Feb.
Flyers > Blackhawks
Eagles = Bears - Rex, nuff said
Phillies = Cubs - Both are going to be great...but Chicago wins cause of the White Sox 2 to 1

I don't know anything about UChicago or UPenn college sports.

http://beta.uchicago.edu/features/20080324.shtml

It's kind of have 2 x1 here for Philly then, let's talk about Partying

PS - Don't bring that NYC is 1.5 hours by train, otherwise I'd say that Chicago's downtown is just 15 minutes away from the Harper Center...

PS - I'm just kidding here...
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Re: Chicago vs Wharton   [#permalink] 27 Mar 2008, 18:15

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text:
# Math Help - Have I got this nonhomogeneous q right?

1. ## Have I got this nonhomogeneous q right?

Not sure about this one, a quick check would be appreciated and a bit of help as to where I've gone wrong if it's not right... a friend got a different answer so I'm questioning my own.

$y''(x) - 2y'(x) - 3y(x) = x^3$ subject to y(0) = 1 and y'(0) = -1

I got $y(x) = -e^{-x} + \frac{14}{27}e^{3x} - \frac{1}{3}x^3 + \frac{2}{3}x^2 - \frac{14}{9}x + \frac{40}{27}$

Testing y(0) there does get me 1 so it seems right to me...

2. Originally Posted by 0renji
Not sure about this one, a quick check would be appreciated and a bit of help as to where I've gone wrong if it's not right... a friend got a different answer so I'm questioning my own.

$y''(x) - 2y'(x) - 3y(x) = x^3$ subject to y(0) = 1 and y'(0) = -1

I got $y(x) = -e^{-x} + \frac{14}{27}e^{3x} - \frac{2}{3}x^3 + \frac{2}{3}x^2 - \frac{14}{9}x + \frac{40}{27}$

Testing y(0) there does get me 1 so it seems right to me...
Your homogenous solution looks OK. Does the particular solution work when you substitute into the DE? If so, then you have the correct answer.

3. Just tried it out with x = 3 and indeed the result was 27 (I tried with 2 at first and the answer came out wrong but I think that was just a calculation mistake). I will take great delight in telling my friend he is wrong and I am right. Hahaha.

4. I think there's a problem here. If you want the solution subject to the following IC's, $y(0)=1,\;\;y'(0)=1$, then the solution you give is right (with a slight change- should be $- \frac{1}{3}x^3$). However, if you want the solution with the IC's you give $y(0)=1,\;\;y'(0)=-1$, I believe the correct solution is

$
y(x) = -\frac{1}{2}e^{-x} + \frac{1}{54}e^{3x} - \frac{1}{3}x^3 + \frac{2}{3}x^2 - \frac{14}{9}x + \frac{40}{27}$

5. Originally Posted by danny arrigo
I think there's a problem here. If you want the solution subject to the following IC's, $y(0)=1,\;\;y'(0)=1$, then the solution you give is right (with a slight change- should be $- \frac{1}{3}x^3$). However, if you want the solution with the IC's you give $y(0)=1,\;\;y'(0)=-1$, I believe the correct solution is

$
y(x) = -\frac{1}{2}e^{-x} + \frac{1}{54}e^{3x} - \frac{1}{3}x^3 + \frac{2}{3}x^2 - \frac{14}{9}x + \frac{40}{27}$
You're right, $-\frac{2}{3}x^3$ was a typo which I corrected in the first post eventually, and my y'(0) does come out to 1. Oops. No idea how I missed that. Thanks for the help, I'll go back and see where I went wrong when I worked it out...

edit: Ah, I missed a negative sign when working out y'(0)... stupid mistake heh. All fixed now.


text:
# recluster

0th

Percentile

##### Reclustering EMM states

Use various clustering methods to recluster states/clusters in an EMM. The centers of the clusters in the EMM object are used as data points by the reclustering algorithm. States/centers put by reclustering into the same cluster are merged to produce a new reclustered EMM.

Keywords
manip, models, cluster
##### Usage
"recluster_hclust"(x, k=NULL, h=NULL, method="average",  ...,prune=NULL, copy=TRUE)
"recluster_kmeans"(x, k, ..., prune=NULL, copy=TRUE)
"recluster_pam"(x, k, ..., prune=NULL, copy=TRUE)
"recluster_reachability"(x, h, ..., prune=NULL, copy=TRUE)
"recluster_tNN"(x, threshold=NULL, ..., prune=NULL, copy=TRUE)
"recluster_transitions"(x, threshold=NULL, ..., prune=NULL, copy=TRUE)
##### Arguments
x
an "EMM" object.
k
number of clusters.
h
heights where the dendrogram tree should be cut.
threshold
threshold used on the dissimilarity to join clusters for tNN. If no threshold is specified then the threshold stored in the EMM is used.
method
clustering method used by hclust.
...
additional arguments passed on to the clustering algorithm.
prune
logical; prune states with less than prune counts before reclustering.
copy
logical; make a copy of x before reclustering? Otherwise the function will change x!
##### Details

For recluster_kmeans k can also be a set of initial cluster centers (see argument centers for kmeans in package stats).

For recluster_hclust k or h can also be a vector. The result is then a list with several (nested) EMMs, one for each value.

For recluster_reachability reclusters all clusters which are reachable from each other. A cluster $j$ is reachable from $i$ if $j$'s center is closer to $i$'s center than h or if $j$ is reachable by any cluster reachable by $i$.

For recluster_tNN reclusters such that two clusters with centers less than the threshold apart will be reclustered into a single cluster. This is useful, for example, after combining two models.

For recluster_transitions does not recluster clusters! It find groups of clusters which are overlapping (centers are less than 2 thresholds apart) and then redistributes the transition weights such that all members of one group are connected to all the members of the other group using the same weight.

##### Value

An object of class "EMM" or, if copy=FALSE a refernece to the changed object passed as x.Clustering information is available as the attribute "cluster_info". The information provided depends in the clustering algorithm (see hclust, kmeans and pam).

merge_clusters, prune, kmeans, hclust, pam

##### Aliases
• recluster
• recluster_hclust
• recluster_hclust,EMM-method
• recluster_kmeans
• recluster_kmeans,EMM-method
• recluster_pam
• recluster_pam,EMM-method
• recluster_tNN
• recluster_tNN,EMM-method
• recluster_reachability
• recluster_reachability,EMM-method
• recluster_transitions
• recluster_transitions,EMM-method
##### Examples
data(EMMsim)
emm <- EMM(threshold = .2)
build(emm, EMMsim_train)

## do reclustering on a copy of the emm and plot dendrogram
emm_hc <- recluster_hclust(emm, h = 0.6)

attr(emm_hc, "cluster_info")

## compare original and clustered EMM
op <- par(mfrow = c(2, 2), pty = "m")
plot(emm, method= "MDS", main ="original EMM", data = EMMsim_train)
plot(attr(emm_hc, "cluster_info")\$dendrogram)
abline(h=0.6, col="red")
plot(emm_hc, method="MDS", main ="clustered EMM", data = EMMsim_train)
plot(emm_hc, method="MDS", main ="clustered EMM")
par(op)

Documentation reproduced from package rEMM, version 1.0-11, License: GPL-2

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