22,000 research outputs found
q-Differential equations for q-classical polynomials and q-Jacobi-Stirling numbers
No full textWe introduce, characterise and provide a combinatorial interpretation for the so-called q-Jacobi–Stirling numbers.
This study is motivated by their key role in the (reciprocal) expansion of any power of a second order
q-differential operator having the q-classical polynomials as eigenfunctions in terms of other even order operators,
which we explicitly construct in this work. The results here obtained can be viewed as the q-version of
those given by Everitt et al. and by the first author, whilst the combinatorics of this new set of numbers is a
q-version of the Jacobi–Stirling numbers given by Gelineau and the second author
q-pac: A Python Package for Machine Learned Charge Equilibration Models
No full textMany state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They therefore lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models
go-python/gopy: Update with latest PRs applied
No full text<p>see https://github.com/go-python/gopy/pulls?q=is%3Apr+is%3Aclosed for recent PRs applied.</p>
ATEE-Q artifacts
No full textATEE-Q
ATEE-Q is an automatic testing tool exploiting GUI and functional equivalence to improve test efficacy for large-scale Android applications.
System Requirements
Python: 2.7
Android SDK: API 19-28 (make sure adb and aapt commands are available)
Linux: Ubuntu 16.04-20.04
The above versions of software have been tested in our experiments. We use Pyinstalller to bundle the ATEE-Q project into executable files which can be run in Linux. You don't need to install the tool or any other python dependency; Simply downloading and unzipping all the files will suffice. The application under test can be installed in a physical phone connected to a computer or in an Android virtual machine, of which API level 19 (4.4), 23(6.0) and 28 (9.0) have been tested.
Settings
Before running ATEE-Q, please create the Config.txt as follow:
[Path]
#Note: The apks should be placed under 'Benchmark' directory.
Benchmark = /Users/Your_Name/Benchmark/
APK_NAME = your_app_under_test.apk
MINITRACE_HOME = /Users/Your_Name/minitrace/
[Setting]
# You can use command `adb devices` to get the Android device's ID to replace the DEVICE_ID.
DEVICE_ID = emulator-5554
# You may change TIME_LIMIT for debug.
TIME_LIMIT = 3600
# You can specify the launchable Activity, if ATEE-Q cannot find one.
ACTIVITY_NAME =
# You can determine whether ATEE-Q should clear app's data from time to time in the test
SHOULD_CLEAR_DATA = True
In addition, "minitrace" is the instruction coverage measurement tool we used, and it is optional. For how to use it, please refer to http://gutianxiao.com/ape/install-mini-tracing.
Running
Install the app under test on the test device and initialize the app's setting, e.g., logging in;
Run the application with the following bash command:
./main -r Config.txt
Output
The output contents are placed in folder benchmark/_output-1/
/event_output -- The structures and actionable events of each GUI screen.
/crash_log.txt -- Test results including the test cases and recorded crashes and exceptions.
BTW, ATEE-Q usually ends with an Import Error: It is a bug injected in Pyinstalller with python 2.7
Programming in Two Semesters: Using Python and Java
No full textThis highly accessible textbook teaches programming from first principles. In common with many programming courses, it uses Python as the introductory programming language before going on to use Java as the vehicle for more advanced programming concepts.The first part, which teaches Python, covers fundamental programming concepts, such as data types and control structures and functions. It introduces more complex data types such as lists and dictionaries and also deals with file handling. It introduces object-oriented concepts and ends with a case study bringing together all the topics of the first semester. The second part uses Java to teach advanced concepts and centres around object-oriented programming, teaching key object-oriented concepts such as inheritance and polymorphism. The semester again ends with an advanced case study bringing together all the topics of the second semester.Topics and features:- Assumes no prior knowledge, and makes the transition from Python to Java a smooth process- Features numerous exercises and also an illustrative case study for each language- Examines procedural and object-oriented methodologies, as well as design principles- Covers such advanced topics as interfaces and lambda expressions, exceptions and Collections- Includes a chapter on graphics programming in Python using Tkinter - Introduces the latest Java technology for graphical interfaces, JavaFX- Explains design concepts using UML notationOffering a gentle introduction to the field and assuming no prerequisite background, Programming in Two Semesters is the ideal companion to undergraduate modules in software development or programming. In addition, it will serve as a strong primer for professionals looking to strengthen their knowledge of programming with these languages
Hyperbolic structures on 3-manifolds via volume maximization
No full textL'argomento di questa tesi è il problema di dare una struttura iperbolica (completa e di volume finito) a una 3-varietà.
Nel primo capitolo sono richiamati i risultati di geometria iperbolica utilizzati nel seguito.
Nel secondo capitolo è descritto il metodo introdotto da Thurston negli anni '70 per dare strutture iperboliche alle 3-varietà con cuspidi, tramite le equazioni di incollamento di Thurston, insieme ad una versione modificata di Casson, degli anni '90, in cui la struttura iperbolica viene ricavata cercando i punti di massimo del volume per delle strutture ad angoli euclidee.
Nel terzo capitolo sono riportati i due approcci noti per il caso delle varietà chiuse: quello di Manning, del 2002 (solo accennato), e quello di Luo, Tillman e Yang, del 2010. Quest'ultimo sfrutta il fatto che è possibile ottenere una struttura iperbolica a partire da una soluzione di volume massimo delle equazioni di Thurston algebriche (simili alle equazioni di incollamento).
Sia su varietà chiuse che con cuspidi sono definite delle "equazioni di Thurston": nel quarto capitolo è esposto un tentativo, di Luo ed altri autori, di generalizzare alcune di queste idee per triangolazioni di pseudovarietà. Su una pseudovarietà sono definite le equazioni di Thurston algebriche e le strutture ad angoli a valori in S¹. Su queste ultime è definito un volume, ed i punti di massimo danno origine:
* se lisci, a soluzioni delle equazioni di Thurston algebriche generalizzate;
* se non lisci, a soluzioni particolarmente semplici dell'equazione delle superfici normali.
Ho implementato in Python delle funzioni che permettono di trattare strutture ad angoli e equazioni di Thurston; nel quinto capitolo sono presenti degli esempi di calcolo, e in appendice è riportato il codice sorgente
Q-MM: une toolbox python pour la Majorization-Minimization Quadratique
No full textQ-MM is a Python implementation of Majorize-Minimize Quadratic optimization algorithms
Mirroring Mobile Phone in the Clouds
No full textThis paper presents a framework of Mirroring Mobile Phone in the Clouds (MMPC) to speed up data/computing intensive applications on a mobile phone by taking full advantage of the super computing power of the clouds. An application on the mobile phone is dynamically partitioned in such a way that the heavy-weighted part is always running on a mirrored server in the clouds while the light-weighted part remains on the mobile phone. A performance improvement (an energy consumption reduction of 70% and a speed-up of 15x) is achieved at the cost of the communication overhead between the mobile phone and the clouds (to transfer the application codes and intermediate results) of a desired application. Our original contributions include a dynamic profiler and a dynamic partitioning algorithm compared with traditional approaches of either statically partitioning a mobile application or modifying a mobile application to support the required partitioning
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Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
Network Q
No full textA press release from Network Q announcing that they will begin featuring Brian McNaught, a gay columnist and author, for a monthly segment
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