קולוקוויום וסמינריםכדי להצטרף לרשימת תפוצה של קולוקוויום מדעי המחשב, אנא בקר בדף מנויים של הרשימה.
Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.
Academic Calendar at Technion site.
- Bioinformatics Forum
- BizTEC Forum
- CGGC Weekly Seminar
- Coding Theory Seminar
- Haifux, Haifa Linux Club
- Pixel Club
- Theory Seminar
קולוקוויום וסמינרים בקרוב
Class invariants: old concept and new results
- Bertrand Meyer - GUEST LECTURE - Note unusual day
- יום שני, 20.2.2017, 14:30
- חדר 337 טאוב.
Pixel Club: Computational Imaging Through Scattering
- גיא סתת (MIT)
- יום שלישי, 21.2.2017, 11:30
- חדר 1061, בניין מאייר, הפקולטה להנדסת חשמל
Imaging through scattering media has long been a challenge, as scattering corrupts measurements in a non-invertible way. Using near-visible wavelengths to image through scattering media can realize broad applications in bio-medical and industrial imaging. It provides many advantages, such as optical contrast, non-ionizing radiation and availability of fluorescent tags. In this talk I'll discuss recent techniques that were developed to overcome and use scattering in order to recover scene parameters. Our computational imaging approach is based on an ultrafast time-resolved measurement of light transport. The measurement provides high-dimensional data that is used in an algorithmic framework to computationally invert the scattering. I'll demonstrate this approach in two different scenarios: first, a method to recover the location of fluorescent markers hidden behind turbid layer and classify them based on florescence lifetime analysis; second, a method to recover a scene hidden behind a thick tissue phantom. The seminar is self-contained and no prior knowledge is required.
Guy Satat is a PhD student and research assistant in the Camera Culture Group at the MIT Media Lab, under the supervision of prof. Ramesh Raskar. His interests include imaging through scattering, time-resolved imaging, compressive imaging, and medical imaging. He graduated with honors from the Technion - Israel Institute of Technology, where he obtained a BSc in both Electrical Engineering and Physics as part of the Technion Excellence Program.
Remote Memory References at Block Granularity
- גילי יבנה, הרצאה סמינריונית למגיסטר
- יום שלישי, 21.2.2017, 13:00
- טאוב 601
- Prof. Hagit Attiya
The cost of accessing shared objects that are stored in remote memory, while neglecting accesses to shared objects that are cached in the local memory, is evaluated by the number of remote memory references (RMRs) in an execution. two flavours of this measure- cache-coherent (CC) and distributed shared memory (DSM)-model two popular shared-memory architectures. The number of RMRs, however, does not take into account the granularity of memory accesses, namely, the fact that accesses to the shared memory are performed in blocks. This thesis proposes a new measure, called block RMRs, counting the number of remote memory references while taking into account the fact that shared objects can be grouped into blocks. On the one hand, this measure reflects the fact that the RMR paid for bringing a shared object to the local memory might save another RMR for bringing another object placed at the same block. On the other hand, this measure accounts for false sharing: the fact an RMR may be paid when accessing an object due to a concurrent access to another object in the same block. This paper proves that in the CC model finding an optimal placement, i.e., grouping of objects into blocks, is NP-hard when a block can store three objects or more; the result holds even if the sequence of accesses is known in advance. In the DSM model, the answer depends on the cost of invalidating data throughout the system. If cache coherence is supported (i.e., some mechanism exists to inform processes that the data in their local memory is no longer valid), and if the cost of invalidation is negligible compared to the cost of an RMR, then finding an optimal solution is NP-hard. If invalidation is not negligible, an optimal layout can be approximated within an additive factor (depending on the number of processes), if the sequence of accesses is known in advance. In both the CC and the DSM models, finding an optimal placement is NP-hard when objects have different sizes, even for a single process.
A Scalable Linearizable Multi-Index Table
- גל שפי, הרצאה סמינריונית למגיסטר
- יום רביעי, 22.2.2017, 14:30
- טאוב 401
- Prof. E. Petrank
Cocurrent data structures typically index data using a single primary key and provide fast access to data associated with a given key value. However, it is often required to access information via multiple primary and secondary keys, and even through additional properties that do not represent keys for the given data. We propose a lock-free and lock-based designs of a table with multiple indexing, supporting linearizable inserts, deletes and retrieve operations. We have implemented the proposed design and ran measurements on a 64-core AMD platform. The obtained performance demonstrates efficiency and high scalability.
Hash Code 2017 של גוגל במדעי המחשב
- יום חמישי, 23.2.2017, 18:30
- חדר 337, בניין טאוב למדעי המחשב
גוגל תארח Hub for the Online Qualification Round of Hash Code - בפקולטה למדעי המחשב, ביום ה', 23 בפברואר 2017, ב-18:30 (זמן אירופה), בחדר 337, בניין טאוב למדעי המחשב.
המעוניינים לקחת חלק מתבקשים להירשם תוך בחירת [Israel > Haifa > Technion] מתוך הרשימות.
נא לעיין בפרטים נוספים בדף האנגלי.
Pixel Club: Perceptual Representation Learning Across Diverse Modalities and Domains
- טרבור דארל (ברקלי)
- יום שלישי, 28.2.2017, 14:30
- חדר 1003, בניין מאייר, הפקולטה להנדסת חשמל
Learning of layered or "deep" representations has provided significant advances in computer vision in recent years, but has traditionally been limited to fully supervised settings with very large amounts of training data. New results show that such methods can also excel when learning in sparse/weakly labeled settings across modalities and domains. I'll review state-of-the-art models for fully convolutional pixel-dense segmentation from weakly labeled input, and will discuss new methods for adapting deep recognition models to new domains with few or no target labels for categories of interest. As time permits, I'll present recent long-term recurrent network models can learn cross-modal description and explanation
Prof. Darrell is on the faculty of the CS Division of the EECS Department at UC Berkeley. He leads Berkeley’s DeepDrive Industrial Consortia, is co-Director of the Berkeley Artificial Intelligence Research (BAIR) lab, and is Faculty Director of PATH at UC Berkeley. Darrell’s group develops algorithms for large-scale perceptual learning, including object and activity recognition and detection, for a variety of applications including multimodal interaction with robots and mobile devices. His interests include computer vision, machine learning, natural language processing, and perception-based human computer interfaces. Prof. Darrell previously led the vision group at the International Computer Science Institute in Berkeley, and was on the faculty of the MIT EECS department from 1999-2008, where he directed the Vision Interface Group. He was a member of the research staff at Interval Research Corporation from 1996-1999, and received the S.M., and PhD. degrees from MIT in 1992 and 1996, respectively. He obtained the B.S.E. degree from the University of Pennsylvania in 1988.
CGGC Seminar: Geometric Methods for Realistic Animation of Faces
- עמית ברמנו (פרינסטון)
- יום שבת, 15.4.2017, 13:00
- חדר 337, בניין טאוב למדעי המחשב
In this talk, I will briefly introduce myself, mainly focusing on my doctoral dissertation, addressing realistic facial animation.
Realistic facial synthesis is one of the most fundamental problems in computer graphics, and is desired in a wide variety of fields, such as film and advertising, computer games, teleconferencing, user-interface agents and avatars, and facial surgery planning.
In the dissertation, we present the most commonly practiced facial content creation process, and contribute to the quality of each of its three steps.
The proposed algorithms significantly increase the level of realism attained and therefore substantially reduce the amount of manual labor required for production quality facial content.
סדנת המרכז להנדסת מחשבים ע"ש סטפן ושרון זיידן 2017
- יום שישי, 5.5.2017, 09:30
- המרכז להנדסת מחשבים,טכניון
הנכם מוזמנים לסדנת המרכז להנדסת מחשבים ע"ש סטפן ושרון זיידן 2017 של המרכז להנדסת מחשבים בנושא:
"Beyond CMOS: From Devices to Systems" אשר תתקיים בימים שני-שלישי, 5-6 ביוני, 2017 בטכניון.
ההרשמה תיפתח ב-15 במרס, 2017, ופרטים נוספים בדף האנגלי ובאתר הסדנה.