Soft Metrics Research Articles

Physics simulation capabilities of LLMs

Large Language Models (LLMs) can solve some undergraduate-level to graduate-level physics textbook problems and are proficient at coding. Combining these two capabilities could one day enable AI systems to simulate and predict the physical world. We present an evaluation of state-of-the-art (SOTA) LLMs on PhD-level to research-level computational physics problems. We condition LLM generation on the use of well-documented and widely-used packages to elicit coding capabilities in the physics and astrophysics domains. We contribute ∼50 original and challenging problems in celestial mechanics (with REBOUND), stellar physics (with MESA), 1D fluid dynamics (with Dedalus) and non-linear dynamics (with SciPy). Since our problems do not admit unique solutions, we evaluate LLM performance on several soft metrics: counts of lines that contain different types of errors (coding, physics, necessity and sufficiency) as well as a more educational’ Pass-Fail metric focused on capturing the salient physical ingredients of the problem at hand. As expected, today's SOTA LLM (GPT4) zero-shot fails most of our problems, although about 40% of the solutions could plausibly get a passing grade. About 70%–90% of the code lines produced are necessary, sufficient and correct (coding & physics). Physics and coding errors are the most common, with some unnecessary or insufficient lines. We observe significant variations across problem class and difficulty. We identify several failure modes of GPT4 in the computational physics domain, such as poor physical units handling, poor code versioning, tendency to hallucinate plausible sub-modules, lack of physical justification for global run parameters (e.g., simulation time, or upper-lower bounds for parametric exploration) and inability to define steady-state or stopping conditions reliably. Our reconnaissance work provides a snapshot of current computational capabilities in classical physics and points to obvious improvement targets if AI systems are ever to reach a basic level of autonomy in physics simulation capabilities.

Metrization of soft metric spaces and its application to fixed point theory

<abstract><p>Soft set theory has attracted many researchers from several different branches. Sound theoretical improvements are accompanied with successful applications to practical solutions of daily life problems. However, some of the attempts of generalizing crisp concepts into soft settings end up with completely equivalent structures. This paper deals with such a case. The paper mainly presents the metrizability of the soft topology induced by a soft metric. The soft topology induced by a soft metric is known to be homeomorphic to a classical topology. In this work, it is shown that this classical topology is metrizable. Moreover, the explicit construction of an ordinary metric that induces the classical topology is given. On the other hand, it is also shown that soft metrics are actually cone metrics. Cone metrics are already proven to be an unsuccessful attempt of generalizing metrics. These results clarify that most, if not all, properties of soft metric spaces could be directly imported from the related classical theory. The paper concludes with an application of the findings, i.e., a new soft fixed point theorem is stated and proven with the help of the obtained homemorphism.</p></abstract>

Convergence : Partially ordered soft topological space

This research paper introduce Symmetrical and sequentially dense soft set and isometry in the context of POS topological space. Inspired by Matthew’s seminal work on dataflow network research, we extend the study to soft universe, bridging classical metrics with soft computing to represent imprecision in complex systems. We investigate sequence convergence in partial soft topological spaces, revealing essential insights and contributing to our understanding of partial soft metrics. Additionally, we introduce the idea of Convergence of partially ordered soft topological spaces along with we provide examples to support the obtained results and relations. In the future, these results can be further applied to analyze and understand the properties and convergence of soft topological spaces with the contraction condition for soft fixed point theory.

Efficient Iterative Timing Recovery of Low-Density Parity-Check Decoding Metrics Using the Steepest Descent Algorithm for Satellite Communications at Low SNRs

An efficient iterative timing recovery via steepest descent of low-density parity-check (LDPC) decoding metrics is presented. In the proposed algorithm, a more accurate symbol timing synchronization is achieved at a low signal-to-noise (SNR) without any pilot symbol by maximizing the sum of the square of all soft metrics in LDPC decoding. The principle of the above-proposed algorithm is analyzed theoretically with the evolution trend of the probability mean of the soft LDPC decoding metrics by the Gaussian approximation. In addition, an efficiently approximate gradient descent algorithm is adopted to obtain excellent timing recovery with rather low complexity and global convergence. Finally, a complete timing recovery is accomplished where the proposed scheme performs fine timing capture, followed by a traditional Mueller–Müller (M&M) timing recovery, which acquires timing track. Using the proposed iterative timing recovery method, the simulation results indicate that the performance of the LDPC coded binary phase shift keying (BPSK) scheme with rather large timing errors is just within 0.1 dB of the ideal code performance at the cost of some rational computation and storage. Therefore, the proposed iterative timing recovery can be efficiently applied on occasions of the weak signal timing synchronization in satellite communications and so on.

Performance Metrics for Fluidic Soft Robot Rotational Actuators.

The field of soft robotics is continuing to grow as more researchers see the potential for robots that can safely interact in unmodeled, unstructured, and uncertain environments. However, in order for the design, integration, and control of soft robotic actuators to develop into a full engineering methodology, a set of metrics and standards need to be established. This paper attempts to lay the groundwork for that process by proposing six soft robot actuator metrics that can be used to evaluate and compare characteristics and performance of soft robot actuators. Data from eight different soft robot rotational actuators (five distinct designs) were used to evaluate these soft robot actuator metrics and show their utility. Additionally we provide a simple case study as an example of how these metrics can be used to evaluate soft robot actuators for a designated task. While this paper does not claim to present a comprehensive list of all possible soft robot actuator metrics, the metrics presented can 1) be used to initiate the development and comparison of soft robot actuators in an engineering framework and 2) start a broader discussion of which metrics should be standardized in future soft robot actuator research.

Evaluating Service Quality and Service Value in Manufacturer-Distributor Settings: A Systems Approach

ABSTRACT Purpose – The purpose of this study is to examine the antecedents of service quality and customer value in a manufacturer-distributor context by elaborating the basic principles of general systems theory. Based on previous research in the field we demonstrate how an initially multidimensional and complicated phenomenon can be explained and predicted by a relatively simple research model. Methodology/approach – After the theoretical discussion, this paper develops a systemic research model for measuring service quality and customer value. The model suggests that input dimensions positively affect process dimensions, which subsequently have a positive effect on output quality. It shows the mechanisms by which factors outside the traditional service management domain impact service outcomes directly and indirectly. Quantitative empirical research was carried out in order to test the hypotheses inherent in the research model, and the data were analyzed using the partial least squares (PLS) method. Findings – Study findings support the widespread idea that perceived service quality and customer value are grounded in the quality of the service process and also on critical input factors. Considering both direct and indirect effects, it seems that the most significant driver behind the service quality and customer value is employee response followed by employee assurance. Nevertheless, it is important to note that both tangibles and visuals (visually appealing physical facilities, equipment, and appearance of personnel), as well as information items (quality and accessibility of information and communication quality), are quite strong predictors of the associated process structures. These service attributes should not be rejected when in pursuit of a comprehensive quality policy in practice. Research Implications – The chief contribution of this study to the research community is that a more definite conceptualization and explanation of the service success can be found by the general systems theory. Practical Implications – Our advice to practitioners, and above all to service management, is that they must do everything in their power to increase the level of employee responsiveness. Without ignoring other dimensions in the quality system, the soft metrics inherent in employee assurance are valued highly by customers. Originality/value – The research model reveals previously unrecognized interactions between eight constructs. Our data and the empirical tests confirm that the adopted approach explains service quality and customer value exceptionally well. In addition to the explanatory power of the proposed approach, due to the methodology chosen, it also has strong predictive power. Thus, the model can be used to predict observations for cases that are similar to the case used in the sample. Even though the specific focus of the study is on the manufacturer-distributor context, the results are applicable to service management in general.

Defining Environments: Understanding Architectural Performance through Modelling, Simulation and Visualisation

Largely due to environmental concerns, approaches to architecture are becoming increasingly crossdisciplinary. With this comes a need for ever more integrated digital simulation tools. Brady Peters – Assistant Professor at the University of Toronto and a Director of Smartgeometry, an organisation that promotes computation in architecture – discusses what these tools can achieve, and highlights two companies that are pioneering their use and development: Danish architecture firm BIG, and international engineers BuroHappold. They set out to visualise not only ‘hard metrics’ such as energy consumption, but also ‘soft metrics’ like wellbeing and productivity.

Redeployment of clinical support to assist junior doctors: A cost analysis

Junior doctors' working hours are regulated by the current New Deal Junior Doctors' contract and European Working Time Directive. Failure to adhere to these requirements results in substantial financial penalties for the employing organisation. Working hours in general surgery are the most frequent sources of such infringements, affecting patient safety, workforce morale and the attractiveness of a surgical career. In this article, we present our data analysing the financial metrics of a 6-month workforce modernisation pilot study introducing a novel clinical and administrative support role of the ‘clinical assistant’ into a busy tertiary general surgical unit; which had a persistent history of working hour non-compliance. We present our results on a successful ‘spend to save’ pilot, which resulted in a significant reduction in working hours negating all financial penalties, while delivering on additional soft metrics including career development and workforce morale.

Demonstrating the Value of Marketing

Marketing departments are under increased pressure to demonstrate their economic value to the firm. This challenge is exacerbated by the fact that marketing uses attitudinal (e.g., brand awareness), behavioral (e.g., brand loyalty), and financial (e.g., sales revenue) performance metrics, which do not correlate highly with each other. Thus, one metric could view marketing initiatives as successful, whereas another could interpret them as a waste of resources. The resulting ambiguity has several consequences for marketing practice. Among these are that the scope and objectives of marketing differ widely across organizations. There is confusion about the difference between marketing effectiveness and efficiency. Hard and soft metrics and offline and online metrics are typically not integrated. The two dominant tools for marketing impact assessment, response models and experiments, are rarely combined. Risk in marketing planning and execution receives little consideration, and analytic insights are not communicated effectively to drive decisions. The authors first examine how these factors affect both research and practice. They then discuss how the use of marketing analytics can improve marketing decision making at different levels of the organization. The authors identify gaps in marketing's knowledge base that set the stage for further research and enhanced practice in demonstrating marketing's value.

Leveraging Localized Social Media Insights for Early Warning Systems

Social Media is more and more positioning as the channel to implement early warning systems for prompt reaction to events that might affect a company's core business. The enabling of geo-location for social media interactions unlocks new possibilities for decision making scenarios aiming at triage situation where the quality of service decays. In this work we provide a system and a set of soft metrics to quantify the impact created by the reaction of people directly affected by an incident in a particular area in order to facilitate the service providers’ appropriate reaction, the decision making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.

Soft Metrics and Their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise

In this paper, we address the classical problem of maximum-likelihood (ML) detection of data in the presence of random phase noise. We consider a system, where the random phase noise affecting the received signal is first compensated by a tracker/estimator. Then the phase error and its statistics are used for deriving the ML detector. Specifically, we derive an ML detector based on a Gaussian assumption for the phase error probability density function (PDF). Further without making any assumptions on the phase error PDF, we show that the actual ML detector can be reformulated as a weighted sum of central moments of the phase error PDF. We present a simple approximation of this new ML rule assuming that the phase error distribution is unknown. The ML detectors derived are also the aposteriori probabilities of the transmitted symbols, and are referred to as soft metrics. Then, using the detector developed based on Gaussian phase error assumption, we derive the symbol error probability (SEP) performance and error floor analytically for arbitrary constellations. Finally we compare SEP performance of the various detectors/metrics in this work and those from literature for different signal constellations, phase noise scenarios and SNR values.

Soft Metrics and EXIT Chart Analysis of Noncoherent MFSK with Diversity Reception in Rician Fading Channel

A convolutionally encoded noncoherent M-ary Frequency Shift Keying (MFSK) modulation scheme with diversity reception is considered. Soft metrics for Soft Decision Decoding (SDD) of noncoherent MFSK are derived assuming Rician channel model. Furthermore, the convergence behavior of the MFSK receiver is investigated using EXtrinsic Information Transfer (EXIT) charts. It has been demonstrated that the derived expressions for soft information are useful allowing SDD of the MFSK scheme exploiting diversity and the EXIT chart analysis provides useful insights into its iterative decoding behavior.

Performance of Soft Decision Decoded Synchronous FHSS Multiple Access Networks Using MFSK Modulation under Rayleigh Fading

In this paper, we analyze the performance of soft decision decoded, synchronous, fast frequency-hopping spread-spectrum multiple-access networks using M-ary frequency shift keying in Rayleigh fading channels under the assumption that the number of frequency hopping slots is sufficiently large. The joint probability density function (pdf) of the absolute values of the correlator outputs is derived to compute the channel capacity and evaluate the soft decision decoding performance of the 3GPP Turbo and convolutional codes. Results indicate that soft decision decoding based on the derived pdf drastically outperforms traditional decoders based on the Gaussian approximation of the effect of the multiple access interference (MAI). The sensitivity of the derived soft decision decoder to estimation errors in system parameters and the required computational complexities of the derived soft decision decoding metrics are also investigated.

ROMI-Driven Sales Promotions: How The Biggest Coca-Cola Bottler Outside Of The U.S. Learned How To Measure The Impact Of Their Sales Promotions

As part of the arsenal of marketing, sales promotions are strategies wherein an incentive is offered to the final consumer or customer to impact sales in the short term (not to exceed three months). Also part of the family are trade promotions that have the same objective, but whose incentives are targeted to the channel of distribution or the POP owners instead of the final consumer. By this definition, one would suppose it is one of the tools of marketing that is the easiest to measure in financial terms because of its short-term nature and specific aim to impact hard metrics, such as sales vs. other campaigns, that try to impact soft metrics, such as brand consideration of purchase intent. Sadly, as the market study presented indicates, few marketing executives, well knowing the exact costs related in each promotion, bother to estimate the required resulting sales to obtain an attractive return on investment and few and far between do a post-mortem examination to quantify the real returns. As such, the objective of this paper is to present an easy methodology to evaluate, before and after, in financial terms, sales promotions of all different types and of all verticals, whether they are B2C or B2B. Also, an in-the-field assessment of the methodology will be presented inside the promotional division of FEMSA, the biggest Coca-Cola Bottler in the world (outside of the US) which performs over 300 promotions per year. The example will serve to illustrate how to implement and replicate this methodology inside of other companies, as well as give evidence of its impact.

Soft metrics: What are they and what use are they for the Intelligence Community?

Modern decision making challenges the human capacity to reason in an environment of uncertainty, imprecision, and incompleteness of information. Atop the uncertainty, ranking in the presence of multiple criteria, multiple agents, and heterogeneous sources of information is often the main task to accomplish by the analysts in the Intelligence Community (IC). Soft metrics are attributes of decision criteria that cannot be expressed numerically. These metrics are at the core of a computational engine that is perception-based, with computational 'atoms' expressed in natural language (NL). The soft metrics approach as a basis for natural language-based computing contributes to fast analyses and an efficient use of human resources in contemporary decision making such as: intelligence data analysis, the Global Strike (target pairing), risk analysis, threat assessment, strategic interactions, conflict analysis, and the strategic deterrence assessment.

Soft-Decision Decoding in Asynchronous FH/SSMA Networks Using MFSK Modulation

Soft-decision decoding techniques are applied to asynchronous frequency-hop/spread-spectrum multiple-access (FH/SSMA) networks, where M-ary frequency shift keying (MFSK) is employed to transmit one modulated symbol per hop. Coding schemes using soft-decision decoded binary convolutional codes or turbo codes are considered, both with or without bit-interleaving. Performances of several soft metrics are examined for each coding scheme. It is shown that when multiple access interference is the main source of errors, the product metric offers the best performance among the soft metrics considered for all coding schemes. Furthermore, the application of soft-decision decoded convolutional codes or turbo codes without bit-interleaving is shown to allow for a much larger number of simultaneously transmitting users than hard-decision decoded Reed-Solomon codes. Finally, it is observed that when soft-decision decoding techniques are employed, synchronous networks attain better performance than asynchronous networks.

Performance of soft metrics for convolutional coded asynchronous fast FHSS-MA networks using BFSK under rayleigh fading

Performance of robust soft decoding metrics is examined for use in convolutional coded asynchronous fast frequency-hop spread-spectrum multiple-access networks using binary frequency-shift keying under Rayleigh fading channels. For comparison, maximum-likelihood metrics based on a Gaussian approximation are derived. Significant gains are observed compared to hard decision decoding by using appropriate soft metrics.

Iterative multiuser detection

In this paper, we propose a novel scheme for iterative multiuser detection and turbo decoding. Multiuser detection is based on the soft output maximum a posteriori (MAP) detector, which passes soft metrics to the single-user turbo decoders. Parallel concatenated convolutional codes (PCCC) are employed as single user turbo-codes, where each user is assigned a unique pseudorandom interleaver. Turbo decoding is done in a parallel fashion and decoders are modified to produce a posteriori probabilities of coded bits. The PCCC turbo decoder and multiuser detector are coupled such that after each single-user turbo decoding iteration, the extrinsic probabilities of the interfering users are passed to the multiuser detector and after each multiuser iteration, updated a posteriori probabilities are passed to the single-user turbo decoders as the soft input metrics. Simulation results for binary synchronous multiuser systems in additive white Gaussian noise show that the proposed iterative detection/decoding scheme approaches the multiuser capacity limit within 1 dB in the low signal-to-noise ratio region.

Internet Industry Mergers and Acquisitions

If the prediction that two-thirds of today9s Internet companies will fail within five years proves accurate, now is the time to develop an M&A strategy in the event that you9re not among the fortuitous one-third. Valuation volatility in the dot.com sector reflects the lack of a consistent valuation methodology—one that depends on a highly subjective combination of both hard and soft metrics, the latter comprised primarily of human capital—ideas, experience, talent. Internet M&A trends point to reasonable valuation, as well as speed in the determination of whether to sell, as important factors in extracting maximum value in a rapidly consolidating sector.

Adaptive soft-input soft-output algorithms for iterative detection with parametric uncertainty

The soft-input soft-output (SISO) module is the basic building block for established iterative detection (ID) algorithms for a system consisting of a network of finite state machines. The problem of performing ID for systems having parametric uncertainty has received relatively little attention in the open literature. Previously proposed adaptive SISO (A-SISO) algorithms are either based on an oversimplified channel model, or have a complexity that grows exponentially with the observation length N (or the smoothing lag D). In this paper, the exact expressions for the soft metrics in the presence of parametric uncertainty modeled as a Gauss-Markov process are derived in a novel way that enables the decoupling of complexity and observation length. Starting from these expressions, a family of suboptimal (practical) algorithms is motivated, based on forward/backward adaptive processing with linear complexity in N. Previously proposed A-SISO algorithms, as well as existing adaptive hard decision algorithms are interpreted as special cases within this framework. Using a representative application-joint iterative equalization-decoding for trellis-based codes over frequency-selective channels-several design options are compared and the impact of parametric uncertainty on previously established results for ID with perfect channel state information is assessed.