Judge Koh’s Monopolization Mania: Her Novel Antitrust Assault Against Qualcomm Is an Abuse of Antitrust Theory

I. Introduction: A Blockbuster Decision II. The Typology of Antitrust Offenses ... A. Per Se Offenses ... B. Rule of Reason Cases ... C. Per Se Legality or “No-Duty” Rules III. FTC v. Qualcomm ... A. The Complaint and the Ohlhausen Dissent ... B. The Monopolization Issue ... C. Market Definition ... D. Trinko and the Antitrust Duty to Deal ... E. Qualcomm’s Pricing Policy—The Use of Constant Rates ... F. The FTC Valuation Dilemma … G. Qualcomm Efficiency Justifications IV. Conclusio

WCDMA in Malaysia

Wideband Code Division Multiple Access (WCDMA) A 3G highspeed digital data service provided by cellular carriers that use the time division multiplexing (TDMA) or GSM technology worldwide, including AT&T (formerly Cingular) and T-Mobile in the U.S. WCDMA works on WCDMA cell phones as well as laptops and portable devices with WCDMA modems [1]. Users have typically experienced downstream data rates up to 400 Kbps [1]. WCDMA has been used in the Japanese Freedom of Mobile Multimedia Access (FOMA) system and in the Universal Mobile Telecommunications System (UMTS); a third generation follow-on to the 2G GSM networks deployed worldwide [1]. Although TDMA and GSM carriers both use TDMA modulation, WCDMA stems from CDMA. Part of the 3GPP initiative, the International Telecommunication Union (ITU) refers to WCDMA as the Direct Sequence (DS) interface within the IMT-2000 global 3G standards [1]

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.Comment: 14 pages, 12 figure