Reviewerly/exHarmony huggingface dataset:
Matchgate Shadows for Fermionic Quantum Simulation

Abstract “Classical shadows” are estimators of an unknown quantum state, constructed from suitably distributed random measurements on copies of that state (Huang et al. in Nat Phys 16:1050, 2020, https://doi.org/10.1038/s41567-020-0932-7 ). In this paper, we analyze classical shadows obtained using random matchgate circuits, which correspond to fermionic Gaussian unitaries. We prove that the first three moments of the Haar distribution over the continuous group of matchgate circuits are equal to those of the discrete uniform distribution over only the matchgate circuits that are also Clifford unitaries; thus, the latter forms a “matchgate 3-design.” This implies that the classical shadows resulting from the two ensembles are functionally equivalent. We show how one can use these matchgate shadows to efficiently estimate inner products between an arbitrary quantum state and fermionic Gaussian states, as well as the expectation values of local fermionic operators and various other quantities, thus surpassing the capabilities of prior work. As a concrete application, this enables us to apply wavefunction constraints that control the fermion sign problem in the quantum-classical auxiliary-field quantum Monte Carlo algorithm (QC-AFQMC) (Huggins et al. in Nature 603:416, 2022, https://doi.org/10.1038/s41586-021-04351-z ), without the exponential post-processing cost incurred by the original approach.
Authoring an Interactive Narrative with Declarative Optimization-Based Drama Management

Drama managers reconfigure a game in reaction to a player's actions. In declarative optimization-based drama management (DODM), a game's story is abstracted as a sequence of plot points; possible drama manager interventions are abstracted as a set of DM actions. The author defines an func- tion evaluating story quality, and some optimization method (currently reinforcement learning) chooses DM actions so as to maximize expected story quality according to that evaluation function. While previous work has developed this ap- proach at a technical level and discussed its potential applications, no work to date has used DODM to write real games. We report on our experiences designing a game in the Neverwinter Nights engine, entitled The Guilty, in which we use DODM to create a dynamic plot that in a previous design iteration we had found difficult to create with other techniques.
Contrastive Linguistics and Translation Studies Interconnected: The Corpus-based Approach

The relationship between contrastive linguistics (CL) and translation studies (TS) as two disciplines within the field of applied linguistics has been explored in depth by several authors, especially in the 1970s and early 1980s. From the mid-nineties on both these disciplines have experienced a great boom due to the use of computerised language corpora in linguistic analysis. We will argue in this paper that this new corpus-based approach to CL and TS makes it necessary to revise the relationship between them, and look for a new common ground to work on. Our hypothesis is that the use of translation equivalence as a tertium comparationis for a corpus-based contrastive analysis provides essential data for TS in a wide range of aspects. On the other hand, the corpus approach of TS has shed a new light on numerous aspects of CL.
CrowdE: Filtering Tweets for Direct Customer Engagements

Many consumer brands have customer relationship agents that directly engage opinionated consumers on social streams, such as Twitter. To help agents find opinionated consumers, social stream monitoring tools provide keyword-based filters, which are often too coarse-grained to be effective. In this work, we introduce CrowdE, a Twitter-based filtering system that helps agents find opinionated customers through brand-specific intelligent filters. To minimize per-brand effort in creating these brand-specific filters, the system used a common crowd-enabled process that creates the filters through machine learning over crowd-labeled tweets. We validated the quality of the crowd labels and the performance of the filter algorithms built from the labels. A user evaluation further showed that CrowdE's intelligent filters improved task performance and were generally preferred by users in comparison to keyword-based filters in current social stream monitoring tools.
Adapting a Rapidly-Exploring Random Tree for Automated Planning

Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques as they offer a great degree of flexibility and reliability. However, their use in other search domains has not been thoroughly analyzed. In this work we propose the use of RRTs as a search algorithm for automated planning. We analyze the advantages that this approach has over previously used search algorithms and the challenges of adapting RRTs for implicit and discrete search spaces.
Machines that Forget: Learning from retrieval failure of mis-indexed explanations

A reasoner may fail at a cognitive task, not because it does not have appropriate knowledge with which to reason, but instead because it does not have the proper index or cue with which to retrieve such knowledge from memory. The reasoner knows this memory item; it simply cannot remember the item. This paper argues that forgetting provides an opportunity for learning through memory reorganization. A reasoner that takes full advantage of such opportunities, however, must be able to reason about its own memory system. To do so, it must possess a language for declaratively representing its reasoning failures and must reflectively inspect such representations if it is to fully explain the reason for its failure. Once such an error is understood as a memory failure, the problem of forgetting is to re-adjust the indexes (associations between cues and memory) so that the knowledge is properly retrieved in similar, future situations.
Handshaking: Negotiate to Provoke the Right Understanding of Requirements

Development teams that accept fragmentary and vague customer requirements have to cope with uncertainty and disorientation. Inadequate communication and submission to a dominating customer makes it hard to know if requirements are understood or not. Requirements engineering, with its focus on good specification practices, had yet to find working solutions for effective requirements communication. We are using a negotiation process called Handshaking with Implementation Proposals to make such requirements communication work – even in situations where written requirements are almost nonexistent and distance separates customers from developers. Handshaking is an efficient and effective technique that uses architectural options to achieve requirements understanding, to create value with implementation decisions, and to establish the foundation for a stable project. This article focuses on the interface between product management and the development organization in companies, where product management acts as a customer towards development. We describe the requirements communication challenges we encountered, explain how we addressed these challenges with the Handshaking process, and share the lessons we learned at European and Asian development sites from ABB and Danaher Motion.
Informed Initial Policies for Learning in Dec-POMDPs

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a formal model for planning in cooperative multiagent systems where agents operate with noisy sensors and actuators, and local information. Prevalent Dec-POMDP solution techniques have mostly been centralized and have assumed knowledge of the model. In real world scenarios, however, solving centrally may not be an option and model parameters maybe unknown. To address this, we propose a distributed, model-free algorithm for learning Dec-POMDP policies, in which agents take turns learning, with each agent not currently learning following a static policy. For agents that have not yet learned a policy, this static policy must be initialized. We propose a principled method for learning such initial policies through interaction with the environment. We show that by using such informed initial policies, our alternate learning algorithm can find near-optimal policies for two benchmark problems.
Automatic Generation of Game Level Solutions as Storyboards

Interactive Storytelling techniques are attracting much interest for their potential to develop new game genres but also as another form of procedural content generation, specifically dedicated to game events rather than objects or characters. However, one issue constantly raised by game developers, when discussing gameplay implications of Interactive Storytelling techniques, is the possible loss of designer control over the dynamically generated storyline. Joint research with industry has suggested a new potential use for Interactive Storytelling technologies, which stands precisely as an assistance to game design. Its basic philosophy is to generate various/all possible solutions to a given game level using the player character as the main agent, and gameplay actions as the basic elements of solution generation. We present a fully-implemented prototype which uses the blockbuster game Hitman as an application. This system uses Heuristic Search Planning to generate level solutions, each legal game action being described as a planning operator. The description of the initial state, the level's objective as well as the level layout, constitute the input data. Other parameters for the simulation include the Hitman's style, which influences the choice of certain actions and privileges a certain style of solution (e.g. stealth versus violent). As a design tool, it seemed appropriate to generate visual output which would be consistent with the current design process. In order to achieve this, we have adapted original Hitman storyboards for their use with a generated solution: we attach elements of storyboards to the planning operators so that a complete solution generates a comic strip similar to an instantiated storyboard for the solution generated. We illustrate system behaviour with specific examples of solution generation.
Automatic Move Pruning Revisited

In this paper we show that the move pruning method we presented at SoCS last year sometimes prunes all the least-cost paths from one state to another. We present two examples exhibiting this erroneous behaviour--a simple, artificial example and a slightly more complex example that arose in last year's experiments. We then formally prove that a simple modification to our move pruning method makes it "safe," i.e., it will never prune all the least-cost paths between a pair of states. Finally, we present the results of rerunning last year's experiments with this provably safe version of move pruning and show that last year's main conclusions still hold, namely: (1) in domains where there are short redundant sequences move pruning produces substantial speedups in depth-first search, and (2) in domains where the only short redundant sequences are 2-cycles, move pruning is faster than parent pruning by a factor of two or more.
Evaluating Temporal Plans in Incomplete Domains

Recent work on planning in incomplete domains focuses on constructing plans that succeed despite incomplete knowledge of action preconditions and effects. As planning models become more expressive, such as in temporal planning, the types of incompleteness may not only change, but plans become more challenging to evaluate. The primary difficulty to temporal plan evaluation is accounting for temporal constraints that may not be satisfied under all interpretations of the incomplete domain. In this work, we formulate incomplete temporal plan evaluation as a generalization of the temporal consistency problem, called partial temporal consistency. We present a knowledge compilation approach that is combined with symbolic constraint propagation and model counting algorithms for counting the number of incomplete domain model interpretations under which a plan is consistent. We present an evaluation that identifies the aspects of incomplete temporal plans most impact performance.
Quantum states cannot be transmitted efficiently classically

We show that any classical two-way communication protocol with shared randomness that can approximately simulate the result of applying an arbitrary measurement (held by one party) to a quantum state of $n$ qubits (held by another), up to constant accuracy, must transmit at least $\Omega(2^n)$ bits. This lower bound is optimal and matches the complexity of a simple protocol based on discretisation using an $\epsilon$-net. The proof is based on a lower bound on the classical communication complexity of a distributed variant of the Fourier sampling problem. We obtain two optimal quantum-classical separations as easy corollaries. First, a sampling problem which can be solved with one quantum query to the input, but which requires $\Omega(N)$ classical queries for an input of size $N$. Second, a nonlocal task which can be solved using $n$ Bell pairs, but for which any approximate classical solution must communicate $\Omega(2^n)$ bits.
Defining The It Curriculum: The Results Of The Past 2½ Years

Efforts to define IT curriculum and accreditation standards began at the first Conference on Information Technology Curriculum (CITC-1) in December 2001, which included representatives from 15 Information Technology (IT) programs at four-year schools in the United States.Also in attendance were representatives from the Association for Computing Machinery (ACM), the Institute for Electrical and Electronic Engineers (IEEE), and the Accreditation Board for Engineering and Technology (ABET).Much foundational work began at this conference, and committees were formed to continue the work.This work has been ongoing since this first CITC conference, continuing at CITC-2 (April 2002), CITC-3 (September 2002), and CITC-4 (October 2003), and through committee work which has progressed between these conferences.The three main thrusts of this work have been to define standards for accreditation of IT programs, to define a model curriculum for IT programs, and to distinguish IT programs from the most closely-related academic programs, such as Information Systems and Computer Science.Membership in SIGITE (Special Interest Group on Information Technology Education) of the ACM is now over 100 members and represents most 4-year IT programs and several 2-year IT programs in the United States.Because of the wide representation in SIGITE, it is felt that the outcome of these three main thrusts is of wide interest to all those in related programs or at institutions considering forming a similar program.This paper gives some of the details of the results of the work on these three thrusts.
Evaluating the Group Detection Performance: The GRODE Metrics

The detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from the people detection field, other from clustering, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches hard to be carried out. Moreover, most of the existent metrics are scarcely expressive, addressing groups as they are atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident. Then, the metrics have been applied to eight approaches of group detection, on eight public datasets, providing a fresh-new panorama of the state-of-the-art, discovering interesting strengths and pitfalls of the recent approaches.
Achieving Super-Resolution With Redundant Sensing

A new technique is presented to enhance the precision of the analog-to-digital (AD) and digital-to-analog (DA) conversion, which are fundamental operations of many biomedical information processing systems. In practice, the precision of these operations is always bounded, first by the random mismatch error occurred during system implementation, and subsequently by the intrinsic quantization error determined by the system architecture itself.Here, we derive a new mathematical interpretation of the previously proposed redundant sensing architecture that not only suppresses mismatch error but also allows achieving an effective resolution exceeding the system's intrinsic resolution, i.e., super-resolution (SR). SR is enabled by an endogenous property of redundant structures regarded as "code diffusion" where the references' value spreads into the neighbor sample space as a result of mismatch error.Using Monte Carlo methods, we show a profound theoretical increase of 8-9 b effective resolution or 256-512× enhancement of precision on a 10-b device at 95% sample space.The proposed SR mechanism can be applied to substantially improve the precision of various AD and DA conversion processes beyond the system resource constraints.The concept opens the possibility for a wide range of applications in low-power fully integrated sensors and devices where the cost-accuracy tradeoff is inevitable. As a proof-of-concept demonstration, we point out an example where the proposed technique can be used to enhance the precision of an implantable neurostimulator design.
A new spatial-temporal histograms of gradients descriptor and HOD-VLAD encoding for human action recognition

Automatic human action recognition is a core functionality of systems for video surveillance and human object interaction. In the whole recognition system, feature description and encoding represent two crucial key steps. In order to construct a powerful action recognition framework, it is important that the two steps must provide reliable performance. In this paper, we proposed a new human action feature descriptor which is called spatio-temporal histograms of gradients (SPHOG). SPHOG is based on the spatial and temporal derivation signal, which extracts the gradient changes between consecutive frames. Compared to the traditional descriptors histograms of optical flow, our proposed SPHOG costs less computation resource. In order to incorporate the distribution information of local descriptors into Vector of Locally Aggregated Descriptors (VLAD), which is a popular encoding approach for Bag-of-Feature representation, a Gaussian kernel is implanted to compute the weighted distance histograms of local descriptors. By doing this, the encoding schema for bag-of-feature (BOF) representation is more effective. We validated our proposed algorithm for human action recognition on three public available datasets KTH, UCF Sports and HMDB51. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
Modular-value-based metrology with spin coherent pointers

Modular values are quantities that described by pre- and postselected states of quantum systems like weak values but are different from them: The associated interaction is not necessary to be weak. We discuss an optimal modular-value-based measurement with a spin coherent pointer: A quantum system is exposed to a field in which strength is to be estimated through its modular value. We consider two cases, with a two-dimensional and a higher-dimensional pointer, and evaluate the quantum Fisher information. The modular-value-based measurement has no merit in the former case, while its sensitivity can be enhanced in the latter case. We also consider the pointer under a phase-flip error. Our study should motivate researchers to apply the modular-value-based measurements for quantum metrology.
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically-motivated Test Suite

We present an alternative method of evaluating Quality Estimation systems, which is based on a linguistically-motivated Test Suite. We create a test-set consisting of 14 linguistic error categories and we gather for each of them a set of samples with both correct and erroneous translations. Then, we measure the performance of 5 Quality Estimation systems by checking their ability to distinguish between the correct and the erroneous translations. The detailed results are much more informative about the ability of each system. The fact that different Quality Estimation systems perform differently at various phenomena confirms the usefulness of the Test Suite.
Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and non-monotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.
Enhanced perception of risk in a driving simulator

A crucial factor influencing the validity of VR is sense of presence. The aim of this study is, firstly, to increase sense of presence of simulator driving by introducing an anxiety-inducing risk factor. Secondly, to add clarification to the partly inconclusive relationship between sense of presence, anxiety and physiological markers. The study consisted of two groups. The control group drove in the simulator normally. The threat group expected an electric shock in case of collision. Self-report ratings (on sense of presence, anxiety and task load), behavioral (speed, steering reversal rate, brake pedal use) and physiological measurements (EDA, HR) were taken. We expected an increase in the sense of presence, arousal, steering reversals, brake pedal use, task load and anxiety ratings, and a decrease of speed in the threat group. Analyses revealed partial group differences in speed and steering reversal rate. Sense of presence was identical and notably high in both groups. This implies that either a more aversive stimulus might be needed to induce anxiety and increase sense of presence, or that it can hardly be increased at all in highly immersive VR systems.