Setup. Merchant will be solely responsible for the installation of such Equipment and any alterations necessary for such installation. Processor will not be liable for any delay or incompletion of an installation of Equipment. Merchant will be responsible for maintaining and paying for electrical power and a secured phone line or other secure internet connection to be used solely by the Equipment to communicate with Processor.
Setup. Before accessing the Service, you must complete the required Service Documentation which must be accepted by Bank. The type of ACH Entries you will be allowed to originate will be only as permitted by Bank in the Service Documentation. You must also complete the required setup procedures. If you wish to access any of the Services which are available through Online Banking for Business (“OLBB”), you must also sign up for the OLBB service and complete the applicable Service Documentation and setup process and Bank must agree to provide that service to you. You are responsible for the contents of all setup instructions delivered to Bank. Bank is not responsible for detecting errors contained in any instructions or Entry Data, and Bank is entitled to rely on the information contained in your instructions. You must maintain at least one demand deposit Account with Bank to receive the Service.
Setup. Prior to initiating the Program and in advance of each calendar year, HQY shall provide a planned contribution file form (the "Planned Contribution File Form") to Employer. Employer will complete the form in accordance with HQY's instructions and the standard file specifications provided to Employer by HQY from time to time, setting forth the then-available features and options of the Program. The Program shall be designed, implemented and administered in accordance with the elections made in the Planned Contribution File Form delivered to HQY by Employer. HQY shall rely on the Planned Contribution File Form delivered by Employer in developing the systems and platform for the administration of the Program. Once delivered to HQY, the Planned Contribution File Form shall be an irrevocable election by Employer (for the calendar year identified in the form) for HQY to design, implement and administer the Program in accordance with elections made therein. HQY shall have no responsibility for liabilities, penalties, or claims that result from Employer’s failure to provide timely and accurate Planned Contribution File Forms. Employer will deliver the Planned Contribution File Form to HQY at least weekly or in conjunction with the Eligibility File if an Account Holder must be added or removed from participation in the Program or the Annual HSA Balance Booster Amount of an Account Holder must be changed. “Annual HSA Balance Booster Amount” shall mean, with respect to each Account Holder, the dollar amount of planned HSA contributions Employer shall make available to such Account Holder as a contribution to such Account Xxxxxx’s HSA outside of the normal contribution cycle through the Program in a calendar year as conclusively set forth on the Planned Contribution File Form delivered to HQY by Employer from time to time. Employer shall promptly reimburse HQY for any costs incurred by the HQY as a result of any inaccurate, incomplete or erroneous data included in the Planned Contribution File Form. Employer shall provide HQY with timely, accurate and complete information regarding the methodology used by Employer to determine each Account Holder's Annual HSA Balance Booster Amount as well as where Account Holders may locate information regarding Employer's policies and procedures relating to the Program Information (collectively, "Program Information") in accordance with HQY's instructions and the standard file specifications provided to Employer by HQY from time to ...
Setup. Parties mutually agree to meet and fulfill implementation requirements as specified and set forth as follows. To facilitate a fast and effective implementation, eLuma will be responsible for the following:
Setup. A preimage resistant hash function H, a prime number p = 2m3nƒ±1, a supersingular elliptic curve E/Fp2 , and four points PA, QA, PB, QB ∈ E(Fp2 ) such that ⟨PA, QA⟩ = E[2m] and ⟨PB, QB⟩ = E[3n].
Setup. We evaluated our approach on Chinese-English and English- French machine translation tasks. For Chinese-English, the training corpus from LDC con- sists of 2.56M sentence pairs with 67.53M Chinese words and 74.81M English words. We used the NIST 2006 dataset as the validation set for hyper-parameter optimization and model se- lection. The NIST 2002, 2003, 2004, 2005, and 2008 datasets were used as test sets. In the NIST Chinese-English datasets, each Chinese sentence has four reference English transla- tions. To build English-Chinese validation and test sets, we simply “reverse” the Chinese-English datasets: the first En- glish sentence in the four references as the source sentence and the Chinese sentence as the single reference translation. For English-French, the training corpus from WMT 2014 consists of 12.07M sentence pairs with 303.88M English words and 348.24M French words. The concatenation of news-test-2012 and news-test-2013 was used as the valida- tion set and news-test-2014 as the test set. Each English sen- tence has a single reference French translation. The French- English evaluation sets can be easily obtained by reversing the English-French datasets. We compared our approach with two state-of-the-art SMT and NMT systems:
Setup. The target is a development board mounting an ATmega328P 8-bit microcon- troller working at 16 MHz clock frequency. We are storing random data (8-bit values) in flash memory using a memcpy() operation (in a random address each time). During that operation, we measure the power consumption of the device with a Tektronix CT1 current probe attached to a 20 GS/s digital oscilloscope
Setup. We evaluate our approach on Chinese-English alignment and translation tasks. The training corpus consists of 1.2M sentence pairs with 32M Chinese words and 35.4M English words. We used the SRILM toolkit (Xxxxxxx, 2002) to train a 4-gram language model on the Xinhua portion of the English GIGAWORD cor- pus, which contains 398.6M words. For alignment evaluation, we used the Tsinghua Chinese-English word alignment evaluation data set.1 The evalu- ation metric is alignment error rate (AER) (Och and Ney, 2003). For translation evaluation, we used the NIST 2006 dataset as the development set and the NIST 2002, 2003, 2004, 2005, and 2008 datasets as the test sets. The evaluation metric is case-insensitive BLEU (Xxxxxxxx et al., 2002). We used both phrase-based (Xxxxx et al., 2003) and hierarchical phrase-based (Chiang, 2007) translation systems to evaluate whether our approach improves translation performance. For the phrase-based model, we used the open-source toolkit Moses (Xxxxx and Xxxxx, 2007). For the hierarchical phrase-based model, we used an in- house re-implementation on par with state-of-the- art open-source decoders. We compared our approach with two state-of- the-art generative alignment models:
Setup. For our attack we use the Pin˜ata2 development board by Riscure as our target. The CPU on the board is a Cortex-M4F, working at a clock speed of 168 MHz. The CPU has a 32-bit Harvard architecture with a three-stage pipeline. The board is programmed and modified such that it can be targeted for SCA.
