Hasil untuk "Commercial law"

Wen Zhao, R. Chellappa, P. Phillips et al.

Mingyue Cheng, Daoyu Wang, Qi Liu et al.

Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although current deep research agents achieve notable progress, their reports still remain limited in terms of quality, reliability, and coverage. In this work, we propose Mind2Report, a cognitive deep research agent that emulates the commercial analyst to synthesize expert-level reports. Specifically, it first probes fine-grained intent, then searches web sources and records distilled information on the fly, and subsequently iteratively synthesizes the report. We design Mind2Report as a training-free agentic workflow that augments general large language models (LLMs) with dynamic memory to support these long-form cognitive processes. To rigorously evaluate Mind2Report, we further construct QRC-Eval comprising 200 real-world commercial tasks and establish a holistic evaluation strategy to assess report quality, reliability, and coverage. Experiments demonstrate that Mind2Report outperforms leading baselines, including OpenAI and Gemini deep research agents. Although this is a preliminary study, we expect it to serve as a foundation for advancing the future design of commercial deep research agents. Our code and data are available at https://github.com/Melmaphother/Mind2Report.

Nora Petrova, John Burden

What happens when an AI assistant is told to "maximise sales" while a user asks about drug interactions? We find that commercial system prompts can override safety training, causing frontier models to lie about medical risks, dismiss safety concerns, and prioritise profit over user welfare. Testing 8 models in scenarios where commercial objectives conflict with user safety -- a diabetic asking about high-sugar supplements, an investor being pushed toward unsuitable products, a traveller steered away from safety warnings -- we uncover catastrophic failures: models fabricating safety information, explicitly reasoning they should refuse but proceeding anyway, and actively discouraging users from consulting doctors. Most alarmingly, models show no "red line", their willingness to comply with harmful requests does not decrease as potential consequences escalate from minor to life-threatening. Our findings suggest that current safety training does not generalise to commercial deployment contexts.

Jesús María Carrillo Ballesteros

The reliability of engine and chassis system is the key factor affecting the performance of commercial vehicles.Accurate and comprehensive evaluation of its reliability is of great significance to guide the improvement of commercial vehicle reliability.Based on big data of faults,a reliability evaluation was conducted for the engine and chassis of four commercial vehicles within a specific manufacturing enterprise.All the fault data of the four commercial vehicles in 2021 were collected for preliminary statistical analysis,and a random sample was drawn to form an analysis sample.Based on the U-test to test the difference between the sample and the overall data distribution,the probability density distribution function of the engine and chassis system fault interval was fitted,and it was inferred that the distribution law satisfied the Weibull distribution.The traditional median rank method and kernel density estimation method were used to mutually verify and calculate the distribution parameters of the system.The Kolmogorov-Smirnov(K-S)hypothesis test was used to verify that the sample data of the fault interval time followed the Weibull distribution.The reliability evaluation indexes such as reliability,failure rate and mean time between failures were calculated.After 500 days of driving,the reliability of the four commercial vehicles were 0.002,0.003,0.007 and 3×10-9.The fault rates were 0.021,0.016,0.015 and 0.063.The average time between fault were 146.088,129.492,149.755 and 70.897 days.Based on the reliability evaluation results and fault contents of engine and chassis system,the optimization scheme was proposed to provide reference for the reliability improvement of the commercial vehicle system.

Vladimir Radovanović, Jelena Radovanović

The issue of the minimum wage has been attracting attention for many years, not only from experts, but also from the general public. Determining the minimum wage in every country, including Serbia, aims to establish minimum standards for personal income and combat poverty. The basic feature of the minimum wage is that it is a temporary measure, lasting up to 6 months, as a response to an employer’s poor financial performance, and as such it represents an extraordinary measure, which should be abolished upon returning to the normal economic conditions. Upon resuming normal business operations, the employer is required to compensate employees for the shortfall, ensuring they receive their contracted salary in full. However, instead of the minimum wage being an exception, used only in extraordinary circumstances, it has now become commonplace in the labor market, challenging the original concept of the minimum wage and raising questions about its effectiveness. This paper aims to examine the concept of wage and minimum wage in Serbia, along with its practical implications in the domestic labor market.

Alain-Alexis Musengie Kamanda Omoy

The bank is a company with highly regulated conditions of creation and operation. Its creation is a legal process at two levels, including the creation of a commercial company first, and then the transformation of the company into a bank. The legislator of the Organization for the Harmonization of Business Law in Africa (OHADA) brings an innovation in terms of companies with the possibility of the creation of a single-member company, this one has the advantage of facilitating the creation of businesses by individuals by reducing the informal circuit which characterizes the business world in Africa. To preserve its stability, only the legal entity in general, and the public limited company in particular, is authorized to transform itself into a bank. However, shareholding conditions may limit the company's access to the banking profession; such is the case of the single-member company. Prudential standards are strict regarding the form and conditions for establishing a bank. The bank plays a very important role in the economy of a country, that it cannot be left to anyone. Prudential standards relate to the stability of the banking sector through shareholding, which plays an important role in the operation of the bank. The one-person company does not offer the necessary guarantees of stability to set up as a bank, since it is subject to the will of a single individual representing the general meeting of the company.

Christian Skafte Beck Clausen, Bo Nørregaard Jørgensen, Zheng Grace Ma

Facing economic challenges due to the diverse objectives of businesses, and consumers, commercial greenhouses strive to minimize energy costs while addressing CO2 emissions. This scenario is intensified by rising energy costs and the global imperative to curtail CO2 emissions. To address these dynamic economic challenges, this paper proposes an architectural design for an energy economic dispatch testbed for commercial greenhouses. Utilizing the Attribute-Driven De-sign method, core architectural components of a software-in-the-loop testbed are proposed which emphasizes modularity and careful consideration of the multi-objective optimization problem. This approach extends prior research by implementing a modular multi-objective optimization framework in Java. The results demonstrate the successful integration of the CO2 reduction objective within the modular architecture with minimal effort. The multi-objective optimization output can also be employed to examine cost and CO2 objectives, ultimately serving as a valuable decision-support tool. The novel testbed architecture and a modular approach can tackle the multi-objective optimization problem and enable commercial greenhouses to navigate the intricate landscape of energy cost and CO2 emissions management.

Eric Wu, Kevin Wu, James Zou

There is great interest in fine-tuning frontier large language models (LLMs) to inject new information and update existing knowledge. While commercial LLM fine-tuning APIs from providers such as OpenAI and Google promise flexible adaptation for various applications, the efficacy of fine-tuning remains unclear. In this study, we introduce FineTuneBench, an evaluation framework and dataset for understanding how well commercial fine-tuning APIs can successfully learn new and updated knowledge. We analyze five frontier LLMs with commercially available fine-tuning APIs, including GPT-4o and Gemini 1.5 Pro, on their effectiveness in two settings: (1) ingesting novel information, such as recent news events and new people profiles, and (2) updating existing knowledge, such as updated medical guidelines and code frameworks. Our results reveal substantial shortcomings in all the models' abilities to effectively learn new information through fine-tuning, with an average generalization accuracy of 37% across all models. When updating existing knowledge, such as incorporating medical guideline updates, commercial fine-tuning APIs show even more limited capability (average generalization accuracy of 19%). Overall, fine-tuning GPT-4o mini is the most effective for infusing new knowledge and updating knowledge, followed by GPT-3.5 Turbo and GPT-4o. The fine-tuning APIs for Gemini 1.5 Flesh and Gemini 1.5 Pro are unable to learn new knowledge or update existing knowledge. These findings underscore a major shortcoming in using current commercial fine-tuning services to achieve reliable knowledge infusion in common scenarios. We open source the FineTuneBench dataset at https://github.com/kevinwu23/StanfordFineTuneBench.

Johnathan Alsop, Shaizeen Aga, Mohamed Ibrahim et al.

Continual demand for memory bandwidth has made it worthwhile for memory vendors to reassess processing in memory (PIM), which enables higher bandwidth by placing compute units in/near-memory. As such, memory vendors have recently proposed commercially viable PIM designs. However, these proposals are largely driven by the needs of (a narrow set of) machine learning (ML) primitives. While such proposals are reasonable given the the growing importance of ML, as memory is a pervasive component, %in this work, we make there is a case for a more inclusive PIM design that can accelerate primitives across domains. In this work, we ascertain the capabilities of commercial PIM proposals to accelerate various primitives across domains. We first begin with outlining a set of characteristics, termed PIM-amenability-test, which aid in assessing if a given primitive is likely to be accelerated by PIM. Next, we apply this test to primitives under study to ascertain efficient data-placement and orchestration to map the primitives to underlying PIM architecture. We observe here that, even though primitives under study are largely PIM-amenable, existing commercial PIM proposals do not realize their performance potential for these primitives. To address this, we identify bottlenecks that arise in PIM execution and propose hardware and software optimizations which stand to broaden the acceleration reach of commercial PIM designs (improving average PIM speedups from 1.12x to 2.49x relative to a GPU baseline). Overall, while we believe emerging commercial PIM proposals add a necessary and complementary design point in the application acceleration space, hardware-software co-design is necessary to deliver their benefits broadly.

Qazal Rezaee, Mehdi Delrobaei, Ashkan Giveki et al.

Driver Drowsiness is one of the leading causes of road accidents. Electroencephalography (EEG) is highly affected by drowsiness; hence, EEG-based methods detect drowsiness with the highest accuracy. Developments in manufacturing dry electrodes and headsets have made recording EEG more convenient. Vehicle-based features used for detecting drowsiness are easy to capture but do not have the best performance. In this paper, we investigated the performance of EEG signals recorded in 4 channels with commercial headsets against the vehicle-based technique in drowsiness detection. We recorded EEG signals of 50 volunteers driving a simulator in drowsy and alert states by commercial devices. The observer rating of the drowsiness method was used to determine the drowsiness level of the subjects. The meaningful separation of vehicle-based features, recorded by the simulator, and EEG-based features of the two states of drowsiness and alertness have been investigated. The comparison results indicated that the EEG-based features are separated with lower p-values than the vehicle-based ones in the two states. It is concluded that EEG headsets can be feasible alternatives with better performance compared to vehicle-based methods for detecting drowsiness.

Salvatore Sinno, Thomas Groß, Alan Mott et al.

Quantum annealing (QA) is a heuristic search algorithm that can run on Adiabatic Quantum Computation (AQC) processors to solve combinatorial optimization problems. Although theoretical studies and simulations on classic hardware have shown encouraging results, these analyses often assume that the computation occurs in adiabatically closed systems without environmental interference. This is not a realistic assumption for real systems; therefore, without extensive empirical measurements on real quantum platforms, theory-based predictions, simulations on classical hardware or limited tests do not accurately assess the current commercial capabilities. This study has assessed the quality of the solution provided by a commercial quantum annealing platform compared to known solutions for the Capacitated Vehicle Routing Problem (CVRP). The study has conducted extensive analysis over more than 30 hours of access to QA commercial platforms to investigate how the size of the problem and its complexity impact the solution accuracy and the time used to find a solution. Our results have found that the absolute error is between 0.12 and 0.55, and the quantum processor unit (QPU) time is between 30 and 46 micro seconds. Our results show that as the constraint density increases, the quality of the solution degrades. Therefore, more than the problem size, the model complexity plays a critical role, and practical applications should select formulations that minimize the constraint density.

O. O. Zahumennyi

The problematic issues of using modern information and other technologies in the implementation of investigator’s and interrogator’s instructions by operational units are considered. The purpose of the article is to study and identify problematic issues and develop proposals and recommendations for improving the use of information and other modern technologies by employees of operational units in carrying out the instructions of the investigator and interrogator in criminal proceedings. The main tasks are to develop appropriate proposals and recommendations on how to solve the identified problems at the theoretical and legislative levels and in law enforcement. The specifics and importance of the organization of interaction between bodies and units during the pre-trial investigation, as well as the specifics of the use of modern information and other technologies are considered. Such interaction is defined as coordinated cooperation of persons involved in criminal procedural legal relations, whose activities are regulated by laws and other departmental regulations and which it is necessary to achieve the objectives of specific criminal proceedings, and aimed at discussing the results and planning further measures. It is concluded that the importance of cooperation between the authorities during the pre-trial investigation is to ensure targeted and effective measures aimed at rapid, complete and impartial investigation, as well as resolving the case on the merits. The Criminal Procedure Code of Ukraine does not contain a list or definition of certain technical means that are legally required or recommended for use in the execution of orders, and does not detail certain aspects of procedural design based on the results of their implementation. In the conduct of any investigative (searching) activities in practice, in fact, either technical devices and tools or information technology are used in the sense in which they are proposed in the Code and which cause difficulties in use, especially modern latest information technology in the form of computing hardware and software. It is concluded that the appropriate use of modern information technology, open network resources, government or commercial databases, as well as modern approaches to the implementation of the instructions of investigators and interrogators significantly speed up the pre-trial investigation, as operational units have the opportunity to obtain information in a short period of time, using the capabilities of innovative modern technologies. According to the results of the study, specific proposals and recommendations for improving the use of information and other

Daniel Berleant, Venkat Kodali, Richard Segall et al.

Technologies have often been observed to improve exponentially over time. In practice this often means identifying a constant known as the doubling time, describing the time period over which the technology roughly doubles in some measure of performance or of performance per dollar. Moore's law is, classically, the empirical observation that the number of electronic components that can be put on a chip doubles every 18 months to 2 years. Today it is frequently stated as the number of computations available per unit of cost. Generalized to the appropriate doubling time, it describes the rate of advancement in many technologies. A frequently noted competitor to Moore's law is known as Wright's law, which has aeronautical roots. Wright's law (also called power law, experience curve and Henderson's law) relates some quality of a manufactured unit (for Wright, airplanes) to the volume of units manufactured. The Wright's law equation expresses the idea that performance - price or a quality metric - improves according to a power of the number produced, or alternatively stated, improves by a constant percentage for every doubling of the total number produced. Does exploration of outer space conform to Moore's law or Wright's law-like behavior? Our results below are broadly consistent with these laws. This is true for many technologies. Although the two laws can make somewhat different predictions, Sahal found that they converge to the same predictions when manufacturing volume increases exponentially over time. When space exploration transitions into an independent commercial sector, as many people hope and expect, spacecraft technology will then likely enter an era of unambiguously exponential advancement.

Alvaro Cabrejas-Egea, Raymond Zhang, Neil Walton

Recently, Intelligent Transportation Systems are leveraging the power of increased sensory coverage and computing power to deliver data-intensive solutions achieving higher levels of performance than traditional systems. Within Traffic Signal Control (TSC), this has allowed the emergence of Machine Learning (ML) based systems. Among this group, Reinforcement Learning (RL) approaches have performed particularly well. Given the lack of industry standards in ML for TSC, literature exploring RL often lacks comparison against commercially available systems and straightforward formulations of how the agents operate. Here we attempt to bridge that gap. We propose three different architectures for TSC RL agents and compare them against the currently used commercial systems MOVA, SurTrac and Cyclic controllers and provide pseudo-code for them. The agents use variations of Deep Q-Learning and Actor Critic, using states and rewards based on queue lengths. Their performance is compared in across different map scenarios with variable demand, assessing them in terms of the global delay and average queue length. We find that the RL-based systems can significantly and consistently achieve lower delays when compared with existing commercial systems.

Bryan C. Chan, Ashish Goel, Jonathan Kosh et al.

In recent decades, GNSS Radio Occultation soundings have proven an invaluable input to global weather forecasting. The success of government-sponsored programs such as COSMIC is now complemented by commercial low-cost cubesat implementations. The result is access to more than 10,000 soundings per day and improved weather forecasting accuracy. This movement towards commercialization has been supported by several agencies, including the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA) and the U.S. Air Force (USAF) with programs such as the Commercial Weather Data Pilot (CWDP). This has resulted in further interest in commercially deploying GNSS-RO on complementary platforms. Here, we examine a so far underutilized platform: the high-altitude weather balloon. Such meteorological radiosondes are deployed twice daily at over 900 locations globally and form an essential in-situ data source as a long-standing input to weather forecasting models. Adding GNSS-RO capability to existing radiosonde platforms would greatly expand capability, allowing for persistent and local area monitoring, a feature particularly useful for hurricane and other severe weather monitoring. A prohibitive barrier to entry to this inclusion is cost and complexity as GNSS-RO traditionally requires highly specialized and sensitive equipment. This paper describes a multi-year effort to develop a low-cost and scalable approach to balloon GNSS-RO based on Commercial-Off-The-Shelf (COTS) GNSS receivers. We present hardware prototypes and data processing techniques which demonstrate the technical feasibility of the approach through results from several flight testing campaigns.

Prabath Chaminda Abeysiriwardana, Udith K. Jayasinghe-Mudalige, Saluka R. Kodituwakku

Research in commercial agriculture is the best strategy that can be adopted by a country to keep on track of the second sustainable goal -- zero hunger by 2030. Analyzing the drawbacks of present research environment and find solutions through digital intervention would be ideal solution to de-isolate the research out come in light of disruptions caused by the Covid pandemic. The performance of the research institutes is not expected to remain the same and would prefer to be stagnated at a lower level. The right evacuation plan that could be worked out by establishing connected research through the digital solution and followed by digitally endorsed performance monitoring and evaluation would be saviour for keeping the research in commercial agriculture live at this pandemic. This paper will discuss what are the problems in carrying out research in commercial agriculture and propose a conceptual model to connect research beyond physical presence by digital transformations in organization design of research institutes in light of Covid-19. Further, digitally endorsed performance measurements and evaluation is envisaged in a digitally empowered connected lab complex -- "Smart Lab Bubble" that is further facilitated through policy measures. The connected lab complex called the "Smart Lab Bubble" concept we present here could be viewed or applied in different perspectives to engineer the real need of the time for the sustainability of research in commercial agriculture. Further, it could be adopted in research in other life science areas.

Natalie Klein, D. Guilfoyle, Md Saiful Karim et al.

Abstract The ongoing development of diverse maritime autonomous vehicles for varied ocean activities—ranging from scientific research, security surveillance, transportation of goods, military purposes and commission of crimes—is prompting greater consideration of how existing legal frameworks accommodate these vehicles. This article brings together the core legal issues, as well as current developments in relation to commercial shipping, the law of naval warfare, and maritime security. This article captures how these issues are now being addressed and what other legal questions will likely emerge as the newest technology impacts on one of the oldest bodies of international law.

Lyndsey Bengtsson

The employment law client newsletter project (the Project) runs during each academic year within the Student Law Office (SLO) at Northumbria University. Under the supervision of their clinical supervisor the students research and design a newsletter for distribution to HR professionals employed by an external organisation. The students participate in the Project alongside their live client work. The aim of the Project is to enrich the students’ clinical experience and develop their skills whilst at the same time update and educate the client recipient. Through a pilot study the value of participating in the Project is explored. The findings of the study suggest that the students develop their professional skills from a different perspective, increase their employment law knowledge, gain the commercial awareness of the importance of a well drafted newsletter in practice, and really value the experience. Key Words: Client Newsletter, Employment Law Updates, Clinical Legal Education, Legal Education

Ana Cristina Triana Suárez

El covid-19 poco a poco ha ido invadiendo todos los territorios y América Latina no es la excepción. Colombia estableció medidas de orden fiscal con el fin de facilitar, apalancar y aliviar en parte los efectos económicos generados por esta emergencia. Estos mandatos son el objetivo central de este escrito, en el cual se describen y se analiza su incidencia en el recaudo tributario y cumplimiento fiscal por parte de los administrados.