She obeyed without hesitation.
The sound of her palm striking Antonio’s cheek was a thunderclap.
Chapter 31
As Janet brought the car to a stop, Jensen saw two gray motor coaches that loomed nose to tail along the edge of the parking lot at Centennial Airport. “Bigger than I expected,” he said as he opened the door and stepped out of the car.
“Each one is forty-five feet long,” Brecht said.
“Are they identical?”
“Only on the outside.” Brecht pointed to the closest of the two behemoths. “This one is a mobile command post, packed with electronics. That one is a hospitality coach. It has a full kitchen and a dozen privacy pods—fairly comfortable to sleep in, unless you’re claustrophobic. A shower and toilet. Satellite receivers on the roofs link to data centers. Lots of specialized gear on board.”
Mark, Janet and Robert followed Brecht as he walked to the door of the closest bus. With no side windows, it was impossible to see into either vehicle. Someone within must have seen them coming on camera; as they approached, the door opened with a hydraulic hiss, and stairs extended.
“Weapons?” Sand asked.
Brecht gave a wry smile and gestured for them to climb aboard.
Jensen stepped up first. Along one side of the vehicle’s length ran a row of workstations that reminded him of NORAD, which he’d visited in a different life as an Air Force pilot. Huge monitors lined the wall; beneath each was a molded desk holding a keyboard and trackpad and other electronic devices he didn’t recognize. In front of each workstation was a comfortable chair mounted on a support strut fastened to the floor. Gleaming steel lockers lined the opposite wall.
Tinted glass made up portions of the long roof overhead, letting in some light; rows of LED spots added a pleasing amber glow. The interior hummed quietly with the sound of powerful fans.
About two-thirds of the way to the back, several technicians sat together, staring intently at one of the screens. The attractive Asian woman Jensen had met two days ago, Jennifer Takaki, swept her finger lightly across the surface of the panel, leaving bright green marks on what looked like a satellite image.
“What are they doing?” Jensen asked.
“Let’s go see,” Brecht replied, guiding them into the depths of the coach. Looking beyond, Jensen saw a small conference room at the rear of the bus, containing an oval table surrounded by a dozen slender chairs.
They joined Roady Kenehan and Robert Partridge, standing behind two seated people, Takaki and another man they had not met before; he sported a foot-long reddish beard below his bald cranium. The map vanished, and a detailed wire-frame diagram appeared—a wireframe rendering of a structure that rotated slowly about its vertical axis.
Jensen recognized it. “The parking garage?”
Kenehan nodded. “We’re integrating our lidar scans with design plans for the parking structure. We hacked Taubman’s plans out of the Municipal Planning Department’s server.
“Who is Taubman?”
“The developer who built the mall. We’re threading the data into a high-def geolocation matrix—the kind archeologists and cranial surgeons use, only larger—along with information from a lot of other sources.”
“What do you do with that?” Jensen asked.
Before answering, Kenehan looked to Brecht for approval to proceed.
“Go ahead, Roady. Lay it out.”
“We use this to make an extremely detailed point cloud, which is really just a three-dimensional database.”
“A three-dimensional database?” Janet queried.
“Actually, four dimensions,” Takaki chimed in. “Geolocation on a time axis.”
“What are the other sources?” Sand asked.
Kenehan hesitated, so Brecht answered for him. “Traffic cams, security cameras, satellite data streamed from orbital platforms—American, Israeli, Chinese, Russian and Indian spy birds—giving us visible spectrum, infrared, ultraviolet and all other electromagnetic bands.”
“Seriously?” Jensen asked. “You have access to all that?”
“Good to have friends in high places,” Sand observed.
“Not all friends,” Brecht said. “Some don’t know they’re sharing their ELINT with us.”
“Lint?” Janet asked.
“ELINT,” Kenehan said. “Electronic intelligence.”
Brecht continued. “We can map many kinds of radio transmissions: digital burst signals, aircraft flight paths, cell tower emissions, microwave, sat phones; we also monitor atmospheric data, seismic activity, magnetospheric anomalies and more. We track vehicular and human targets, aircraft, watercraft and heavy machinery above and below ground. Even subs.”
“I take it you didn’t get this equipment from Amazon,” said Sand.
Partridge smiled. “At least not all of it.”
“That’s a tremendous amount of data. How do you process it?”
“We feed it into a deep learning data mining system, DLDMS,” Takaki answered. “We’ve just finished provisioning a virtual silo for this operation. Next, we’ll kick-start the first-tier pattern recognition algorithms.”
“Artificial intelligence?” Jensen asked.
Kenehan shook his head. “Not quite, but close.”
“Deep learning,” Janet said. “This is way over my head.”
“I’ll explain,” Brecht said taking a deep breath. “The real power of deep machine learning is the effective unlimited memory and speed of access that computers possess. As far as we’ve come, computers lack true insight—even those with so-called artificial intelligence. What they do bring to the party is the ability to run millions of mathematical operations on giant data collections, keep track of all the results and then use those results to start over again with new base parameters. It’s like driving down every street in America to look for a collection of things without knowing the characteristics of that collection, gathering all the information, using bits of it to make assumptions and then redriving the journey again and again until patterns become evident that would be invisible to a human brain. Computers analyze data without observance bias, which means they don’t ignore, forget or treat anything as unimportant. They track all assumptions, use them and then systematically change them, again and again, repeating the process billions of times. The power of the system is repetition and memory.”
“Okay,” Janet said with a shrug. “So what do you do with the results?”
“The best use of AI is to triage information so we know where to deploy human resources best. It’s that simple.”
“Any hot clues yet?” Sand asked.
“Actually, yes,” Kenehan said. “Videos from three security cameras installed in common areas of condo complexes on the opposite side of Cherry Creek. Low quality, but we can merge and enhance the digital data streams.”
Jensen nodded. “Some of this I’ve seen before. We hire experts to turn two-dimensional images into three-dimensional virtual worlds using the same kind of lidar scans you guys took yesterday. We hire forensic experts who use match-moving software programs like SynthEyes, PFTrack and Boujou to turn 2D images into 3D virtual spaces called moving point clouds. They make detailed and realistic accident reconstruction animations we show in court. The trick is getting them into evidence.”
“I’m impressed, Mark,” Kenehan said. “What we do is based on similar principles. Our software is more advanced, with bigger computers. But you’ve already got the basic concepts.”
“How is your technology more advanced than what Mark’s experts use?” Sand asked.
Kenehan gestured for Jennifer Takaki to explain.
“Spatiotemporal data and geospatial data are not the same thing. They’re both based on tracking data points as they move through well-defined volumes, but the time indices are far more precise with the former.”
“Huh?” Janet blinked.
“Well, we wrote our own software to plug-fill data voids in the moving digital stream.”
&nb
sp; “Can you make that a little simpler for me?” Janet asked. “As in plain English?”
Takaki shrugged. “When we have more than one video source together with high-resolution scans of the environment shown in the videos, we can interpolate between the pixels using Fourier transforms and matrix operations—”
Thomas, who had just joined the group from the rear of the bus, took over. “The mathematical question to solve is: what physical reality created each video? Each video has many possible solutions—but with more than one video, the number of potential physical solutions that could have fathered each image narrows considerably. The system randomly generates billions of imaginary realities to try out and checks each one, frame by frame, throwing out all but those that could have fathered all the images. The software guesses and corrects with error-checking routines to solve the question. It’s a highly iterative stochastic process; the algorithm statistically weights point-matched constants and renders highly detailed virtual models that we can look at and manipulate.”
Sand smirked. “Yup. Exactly what I thought.”
“Of course,” Janet said with sarcasm in her voice. “It’s obvious.”
“Can that work with small objects at a distance, like faces?” asked Jensen.
“Sometimes,” Kenehan replied, nodding enthusiastically. “That’s what we’re hoping for, but it’s a hit-and-miss technology. If it’s possible, BEAST will do it.”
“Beast?” Janet asked.
“It stands for Binary Evaluation of Analogue Statistical Temporals. BEAST. Our secret sauce. The group of algorithms we run on a supercomputer handles jobs that would swamp ordinary computers.”
“The Brecht Group has its own supercomputer?”
“No, we use the Summit supercomputer at Oak Ridge National Laboratory in Tennessee.”
“Well, for this project, I’ll cover that cost,” Jensen said.
Brecht shook his head. “No need. Oak Ridge lets us use Summit without cost in exchange for access to our proprietary BEAST algorithms. They use our kernels—the ‘engines’ that drive our software—for everything from simulating entire galaxies to enhancing digital files for the DOD.”
“How did you develop that software?” Jensen asked.
“More importantly,” Janet interrupted, “how did you get the videos?”
“Same place we got the design drawings for the mall,” Partridge replied.
“You hacked them,” Sand observed.
“Do they show what happened?” Janet asked.
“Not as they are,” Kenehan replied. “You just see black shadows.”
“So, what you’re saying,” Sand summarized, “is that you’ve got three indistinct videos with the parking garage in the background, which might show some of what happened if they’re enhanced? My car was parked along the half-wall, so maybe you can pull out usable detail with your software?”
Brecht nodded, taking a seat and dabbing his forehead with a handkerchief.
“What is the likelihood you’ll be able to extract anything useful?” Jensen asked.
After taking a deep breath and letting it out slowly, Brecht said, “Well, of course there are no guarantees, but I’d say the odds are, ah, good enough that we’ve started preparing for ops that may be necessary after finishing the processing. We’ve got a tactical helicopter inbound. It’ll be here on standby in case we need it. It’s bringing some additional field specialists.”
“Extra muscle?” Sand queried.
“Yup,” Kenehan confirmed.
“An Apache?”
“No, an older Black Hawk, refurbished and refitted,” Brecht explained, his voice growing weary.
Once again, Thomas picked up the discussion. “The General Services Administration auctioned it off in 2016. We picked it up for about four hundred thousand and put another million into it. It’s quite a nice bird. It should be here in a couple of hours. It’s great for aerial surveillance, tracking vehicles and carrying small tactical teams. Of course, we can land it just about anywhere.”
Jensen couldn’t miss how pale and tired Brecht looked. “You feeling okay, Albert?”
“Oh, I’ll be fine in a minute,” Brecht said with a weak smile. He withdrew a pill bottle from his pocket, tapped out a couple of small white tablets and carefully placed them under his tongue. “Once the nitro gets into my system.”
Janet looked worried. “That sounds serious. Do you need to see a doctor?”
Brecht waved the question away with his hand. “No, just give me a minute. He turned to Jennifer. “How long will the enhancement process take?”
Takaki looked at her watch. “A few hours at least. Maybe a day or more. It’s hard to say.”
No one spoke for a while. Finally, Jensen broke the silence. “Kind of reminds me of when I was a kid. Dad used to take pictures of us with a Polaroid camera. While we waited for the pictures to develop, we couldn’t stand the suspense to see how they turned out.”
Chapter 32
A sliver of sunlight crept slowly along the edge of the bed and finally struck Antonio in his right eye. The piercing brightness wrenched him for a panicked instant; he thought it was the beam of a police flashlight. Then he was awake, and he knew it was morning. He rolled over to move away from the intrusive glare and tried for a few seconds to go back to sleep but could not.
Something nagged at him; then he remembered it all—the flood of images from the night before carried him away.
A wave of dizziness came over him. He felt very strange. He was having a hard time catching his breath.
The images were real. It had happened. He had done the unthinkable. The actuality of it hit him like a great stone hurled by a laughing, malevolent ogre. The stone was wrapped in a note—an unbidden thought: this is who you are now.
He was now officially a kidnapper and a rapist and was well on the way to becoming a murderer. There was no turning back.
He didn’t feel like a god—at least not right now.
What had happened to him? Where had his euphoria and bloodlust gone? His newfound confidence?
He opened his eyes. His buttocks and groin muscles ached from overuse and strain. His head was pounding. How had this happened to him? How had he come to this point?
“Fucking bitch,” he muttered, as he fondled himself with curiosity. He felt traumatized, as though he were the sexually assaulted one.
He remembered the girl slapping him; he’d snapped into an animal rage. He’d been treating her all nice and stuff, and then bam! She’d let him have it across the face, out of nowhere, so hard he’d almost cried. Beeman had laughed, howling like a hyena, making a sound he’d never heard from the man before, scaring him and humiliating him.
So he’d slugged the bitch hard in the stomach. She’d doubled over, dropping to her knees. He’d grabbed a fistful of her hair to hold her head still while he’d slapped her face—again and again—and then dragged her up the stairs to the bedroom. He’d thrown her around, ripping her cute little dress away like wrapping paper, cutting her skin while yanking her bra and panties off, and then shimmying out of his pants. He’d taken her, every way possible. He hadn’t asked, and she hadn’t consented; the fierce brutality of his assault had smothered any chance of resistance on her part. He’d just used her like a rag doll. Her tears and cries had flamed his passions, his lust and his years of pent-up frustration and anger.
He’d been more than a brute—he’d been a monster.
At the time, the experience had been exquisite—venting his lust brutally—but now something had worn off, and right now, in the light of day, he felt like he was the fucking victim.
I never thought raping someone could traumatize me.
Downstairs, Beeman spoke softly into his cell phone. The conversation was going out of control.
“I said bring your friend,” Kim ordered. “He’s part of this now.”
“But he knows nothing of—”
Kim cut Beeman off. “We’re running out of time. The longer you da
wdle, the greater our danger. The next phase is critical. Needless complications can’t hamper us, and unless he becomes an asset, Antonio Pessoa is a needless complication.”
“An asset?” Beeman fumed. This man had no idea what he was proposing. “You plan to kill him.”
“Not yet. Just bring him along with you. I’m looking forward to meeting him.” The line went dead.
“Were you on the phone?”
Beeman suppressed his startled response. He hadn’t heard Antonio coming down the stairs. Exhaling softly, he turned slowly and gave Antonio a warm smile while he slipped his cell into his pocket.
Antonio’s face was slack and haggard; his eyes blinked rapidly. Behind the dreadful idiot’s bleary fatigue, Beeman could see remorse and severe anxiety.
Beeman chuckled and then said, “You look like hell, my friend.”
Antonio stared at him for a moment and then grinned sheepishly.
“We need to go into town right away,” Beeman added.
“What?”
“I’ll explain in the car. Hurry up.”
“Wait—what? Why do we … where are we going?”
“We’ll talk on the way. We need to meet someone. We have to be there by nine o’clock. We can’t be late.” As he spoke, Beeman guided Antonio toward the stairs. “Please, Antonio. Grab your clothes and shoes so we can leave. We’ll have breakfast in town. I’ll tell you everything on the way there.”
Antonio groaned and headed up the stairs.
Thirty minutes later in the passenger seat of the Toyota, Antonio rubbed his shaking hands on his jeans while Beeman drove, struggling to absorb the shock of what Beeman had just said.
“You’ve got to be fucking kidding, man,” he whined. “I can’t fucking believe there was someone watching us that day. Jesus. You and your fucking superbug.”
Kim stirred his coffee and looked at his watch.
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